PhD Esther Belade
email: esther.belade@orange.fr

Lucie Armand

Laurent Martinon

Laurence Kheuang

Jocelyne Fleury-Feith

Armelle Baeza-Squiban

Sophie Lanone

Marie-Annick Billon-Galland

Jean-Claude Pairon

Jorge Boczkowski

A comparative transmission electron microscopy study of titanium dioxide and carbon black nanoparticles uptake in human lung epithelial

Keywords: nanotechnologies, nanomaterials, toxicity, lung

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Nanotechnology is an emerging field involving a wide range of technologies that measure, manipulate, or incorporate materials having at least one dimension between 1 and 100 nanometers (ASTM International, 2006). Manufactured nanoparticles (MNPs) are intentionally produced for use in various consumer products or industrial techniques (e.g., as pigments or chemical catalysts). The properties of nanoparticles differ from those of bulk materials of the same compound, allowing them to exert novel physical and chemical functional activities [START_REF] Lanone | Biomedical applications and potential health risks of nanomaterials: molecular mechanisms[END_REF][START_REF] Lanone | Comparative toxicity of 24 manufactured nanoparticles in human alveolar epithelial and macrophage cell lines[END_REF]Oberdörster et al., 2005a).

Although new applications of MNPs are generating considerable enthusiasm, there is increasing evidence that MNP exposure can lead to adverse health effects. The respiratory system is a major route of unintentional exposure to aerosolised MNPs. Moreover, the respiratory system is a potential route for MNP translocation to the systemic circulation.

However, MNP translocation studies showed that this was a limited phenomenon, the pulmonary retention being more important [START_REF] Kreyling | Translocation of ultrafine insoluble iridium particles from lung epithelium to extrapulmonary organs is size dependent but very low[END_REF]. Therefore, MNP toxicological studies have been widely focused on the MNP fate in the pulmonary system. In vitro and in vivo studies established that MNP exposure can alter cell viability, induce inflammation and pulmonary tissue remodelling, and impair redox regulation (Donaldson et al., 2005;[START_REF] Stone | Air pollution, ultrafine and nanoparticle toxicology: cellular and molecular interactions[END_REF]. Furthermore, several studies suggest that the biological responses induced by MNPs may be linked to MNP accumulation within cells [START_REF] Bartneck | Rapid uptake of gold nanorods by primary human blood phagocytes and immunomodulatory effects of surface chemistry[END_REF][START_REF] Oh | Cellular uptake, cytotoxicity, and innate immune response of silica-titania hollow nanoparticles based on size and surface functionality[END_REF]. Indeed, internalised MNPs may directly target organelles such as the mitochondria, leading to oxidative stress [START_REF] Li | Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage[END_REF]Oberdörster et al., 2005a).

Another target of MNPs is the nucleus: thus, MNPs can directly or indirectly induce DNA oxidative damages in the nuclear compartment [START_REF] Bhattacharya | Titanium dioxide nanoparticles induce oxidative stress and DNA-adduct formation but not DNAbreakage in human lung cells[END_REF][START_REF] Mroz | Nanoparticle carbon black driven DNA damage induces growth arrest and AP-1 and NFkappaB DNA binding in lung epithelial A549 cell line[END_REF][START_REF] Trouiller | Titanium dioxide nanoparticles induce DNA damage and genetic instability in vivo in mice[END_REF]. However, MNP internalisation has not yet been sufficiently characterised.

Various techniques have been used to study MNP internalisation. Flow cytometry or confocal microscopy was used in recent studies [START_REF] Faklaris | Detection of single photoluminescent diamond nanoparticles in cells and study of the internalisation pathway[END_REF][START_REF] Thurn | Endocytosis of titanium dioxide nanoparticles in prostate cancer PC-3M cells[END_REF].

However, these techniques require MNP labeling or surface modification, which may constitute a major disadvantage given that changes in MNP physical characteristics (size, shape, surface chemistry) can modify the internalisation and/or subcellular localisation of MNPs [START_REF] Al-Rawi | Uptake and intracellular localization of submicron and nano-sized SiO(2) particles in HeLa cells[END_REF]Gupta et al., 2005;[START_REF] Nativo | Uptake and intracellular fate of surface-modified gold nanoparticles[END_REF][START_REF] Win | Effects of particle size and surface coating on cellular uptake of polymeric nanoparticles for oral delivery of anticancer drugs[END_REF].

Transmission electron microscopy (TEM) is the technique of reference for studying MNP internalisation. With TEM, nano-sized structures can be identified in the cell environment and within cellular organelles (Oberdörster et al., 2005b). However, TEM has rarely been used to accurately quantify MNP internalisation. TEM evaluation of internalisation is a cumbersome and expensive procedure and requires optimal sample preparation [START_REF] Schrand | Preparation of cells for assessing ultrastructural localization of nanoparticles with transmission electron microscopy[END_REF]. To the best of our knowledge, no comparative TEM studies of the internalisation of various MNPs by various cell types are available. Moreover, the relationship between MNP internalisation and their physico-chemical characteristics has been insufficiently analysed.

Therefore, the aim of this study was to use TEM to compare the intracellular uptake of 3 well-characterized MNPs (one made of titanium dioxide and two of carbon black) in 2 different relevant target cells (one representative of the bronchial epithelium, the other of the underlying connective tissue), according to MNP concentration and time of exposure. This comparison was supported by several parameters (localisation of aggregated MNPs in cells, percentage of cells having accumulated MNPs, number of aggregated MNPs in cells, size of MNP aggregates in cells analysed at different time points). Furthermore, we examined the effects of MNPs on cell viability.

We choose titanium dioxide (TiO 2 ) and carbon black (CB) MNPs because TiO 2 and CB nanoparticles are among the most widely produced nanomaterials [START_REF] Baan | Carcinogenicity of carbon black, titanium dioxide, and talc[END_REF]. TiO 2 in particles of supra-nanometer scale has been used commercially for over 100 years as a white pigment in numerous products including paints and other coatings, foods, cosmetics, and skin-care preparations such as topical sunscreens. Several newer technologies use TiO 2 MNPs, for instance for producing paints and sunscreen lotions/sprays. CB MNPs are used chieftly as a pigment in inks and paints and in automobile tires [START_REF] Donaldson | Combustion-derived nanoparticles: a review of their toxicology following inhalation exposure[END_REF][START_REF] Ema | Reproductive and developmental toxicity studies of manufactured nanomaterials[END_REF].

Material and methods

2.1.

Cellular models MRC5 cells (ATCC CCL-171), a human fibroblast cell line, and the 16HBE14obronchial cell line (provided by Dr. D.C. Gruenert; Medical Research Facility, California Pacific Medical Centre, San Francisco, CA, USA) were used in this study. MRC5 cells were derived from normal lung tissue of a 14-week-old male foetus [START_REF] Jacobs | Characteristics of a human diploid cell designated MRC-5[END_REF]. The 16HBE140-cell line was originally developed from human bronchial epithelium, transformed with SV40 large T-antigen [START_REF] Cozens | CFTR expression and chloride secretion in polarized immortal human bronchial epithelial cells[END_REF]. Both cell lines were maintained in a culture medium (DMEM for MRC5 and DMEM/F12 for 16HBE14o-) with L-Glutamine supplemented with 10% foetal bovine serum (FBS) for MRC5 cells, 2% Ultroser G (UG) for 16HBE cells and 1% antibiotics at 37°C in a 5%-CO 2 humidified incubator. These cells were seeded at a density of 1 to 1.6 x 10 4 cells.cm -².

Manufactured nanoparticles

Three types of MNPs were tested: two made of CB (CB21 [P60] and CB13 [FW2] from Evonik/Degussa, Essen, Germany) and one of TiO 2 (637254 from Sigma-Aldrich, Saint Louis, MO, USA). The mean aerodynamic diameters reported by the suppliers were 21 and 13 nm for the CB21 and CB13 MNPs, respectively, and 15 nm for TiO 2 MNPs.

Dry powders were used to evaluate the physico-chemical characteristics of MNPs.

Specific surface area was measured at -196°C using the nitrogen absorption-desorption technique (Brunauer Emmet Teller method, BET). MNP granulometry and aggregation status were evaluated in particle suspensions using photon correlation spectroscopy (PCS) and zeta potential measurement, respectively (Zetasizer 300HS, Malvern Instruments, Malvern, UK).

Stock suspensions of MNPs were prepared by weighing out the dry powders and then suspending them in serum-free (0%) culture medium to reach the concentration of 2 mg.ml -1 .

The stock suspensions were stored at -20°C. Ten minutes before starting the experiments, we sonicated the defrosted MNP stock suspensions in an ultrasound bath to achieve optimal dispersion. We then diluted the suspensions in serum-free culture medium by successive dilutions. Cells were exposed to MNP concentrations of 0.5, 5 and 10 µg.cm -² (of cultured surface) for 6, 24 and 48 hours. Given that cells were cultured in 75 cm² flasks containing 15 ml of DMEM, we needed suspensions to be diluted at the doses of 2.5, 25 and 50 µg.ml -1 (corresponding to 0.5, 5 and 10 µg.cm -²). To do that, the stock suspensions were first diluted at 1: 4, then a second dilution (1: 10) was made from the new suspension to reach the concentration of 50 µg.ml -1 (or 10 µg.cm -²). Finally, the suspensions of 0.5 and 5 µg.cm -² (2.5, 25 µg.ml -1 ) were done by diluting the 10 µg.cm -² MNP suspension.

MNP endotoxin content was measured using the Limulus Amebocyte Lysate (LAL) kit QCL-1000 (Lonza, Basel, Switzerland). Briefly, particles in DMEM were sonicated (as described above), and the particle suspensions were centrifuged for 10 min at 18,000g. The supernatants were collected and centrifuged. This sequence was repeated twice and the endotoxin levels in the supernatants were then determined.

The main physico-chemical characteristics are summarized in Table 1.

Cytotoxicity assay

Cytotoxicity was assessed using the colorimetric WST-1 assay (Roche Diagnostics, Rotkreuz, Switzerland), which measures the mitochondrial dehydrogenase activity in viable Shortly before starting the exposures, 10 µg.cm -² of each MNP suspension were prepared as described above. However, since surfaces (0.32 cm²) and volumes (200 µl) in microplate wells were different from those in flasks, to expose cells to the same MNP concentration, the doses were adapted considering that 10 µg.cm -² corresponded to 16 µg.ml -1 of MNPs. After removing DMEM from wells and washing cells with 100µl/well of phosphate buffered saline (PBS), MNP suspensions were distributed in each well. After 24h of contact, MNP suspensions were removed and cells were washed with PBS. Then, cells were incubated with 100 µl/well of WST-1 solution for 2h, at 37°C, in the dark. Two hours later, the cytotoxicity was determined by measuring the absorbance with a scanning multi-well spectrophotometer (Multiskan Ex microplate photometer, ThermoFisher Scientific, Waltham, MA, USA). The absorbance directly correlates to the number of viable cells. Three independent experiments were performed with 3 replicate wells used for each condition in each experiment.

Transmission electron microscopy

Cell pellet fixation

Following exposure of cells to MNPs in flasks, attached cells were washed with culture media and fixed in situ with 2.5% glutaraldehyde in 0.045M sodium cacodylate buffer (pH 7.4) at 4°C for 2 hours. The cells were then washed 3 times in sodium cacodylate buffer.

Once recovered by de-adhering with scrapers, cell suspensions in 0.045M sodium cacodylate buffer were centrifugated at 1500 rpm for 5-7 min, and the pellets were post-fixed with 2% osmium tetroxide in distilled water for 30 min, at room temperature and in the dark.

Cell pellet embedding

The post-fixed pellets were dehydrated in graded ethanol series before being transferred into Beem capsules filled with epon resin (48.9%), dodecenyl succinic anhydride (17.9%) and nadic methyl anhydride (33.3%). Then, the capsules were incubated at 37°C, in an oven vacuum for 24h, followed by incubation in a 60°C oven for 24h, to complete the embedding.

Sample ultrathin cuts and staining

Ultrathin sections (60 nm thickness) were made with a Leika ultramicrotome and transferred without contrasting onto copper grids, they were observed with an analytical transmission electron microscope (TEM).

TEM evaluation

The microscope used for the study was a JEOL 1200 EX II TEM at 60kV (JEOL, Tokyo, Japan), fitted out with an energy dispersive X-ray spectrometer (EDS OXFORD LINK ISIS 300 spectrometer) and a digital GATAN camera (ERLANGSHEN ES500W). Observed

MNPs were classified as aggregated within cells. We defined an MNP aggregate as a cluster of more than 1 MNP [START_REF] Hackley | The use of nomenclature in dispersion science and technology[END_REF].

Measurements were made on 50 morphologically preserved cells chosen randomly from 5 different grids in each condition. Observations were performed by 2 different observers, between whom agreement was greater than 95%. The following parameters were calculated using the SAISAM software (MicroVision, Evry, France):

1. Cell localisation of MNPs (free in the cytoplasm or in vacuoles, mitochondria, or nuclei) To improve the accuracy of our estimate of intracellular MNP accumulation, each cell displaying morphological signs of cellular death (cellular membrane disruption, cellular splitting/blebbing) was systematically excluded. Thus, only cells with visible and well preserved cytoplasm, nucleus, and mitochondria were studied. Furthermore, the chemical nature of TiO 2 MNPs was checked using EDX analysis. Aggregated MNPs were classified based on their subcellular localisation (free in cytoplasm or in vacuoles, mitochondria, or nuclei). The time-response relationship was examined after 6, 24 and 48h exposure to 5 µg.cm -2 and the dose-response relationship was investigated after 24h of contact to 0.5, 5 and 10 µg.cm -2 of MNPs.

Percentage of cells containing MNPs

Statistical analysis

Three independent experiments were performed by exposing the cells to the different MNPs at the different doses and during the different time periods. In the TEM studies, we analysed 50 cells of each experiment. We compared MNP accumulation across doses and exposure times using the Chi-2 test and the other parameters using one-way ANOVA. When ANOVA showed statistically significant differences between treatments (p<0.05), pairwise comparisons were performed using Tukey's test. The cellular viability data were compared by performing a Kruskal-Wallis test, followed by a Dunn's test. All statistical analyses were conducted using GraphPad Prism software (GraphPad Software, Inc., La Jolla, CA, USA).

p<0.05 was considered statistically significant.

Results

Physico-chemical characterisation of MNPs

Table 1 shows the physico-chemical characteristics of the MNPs. The 3 types of MNP had similar sizes and a round-like shape (L'Azou, 2008). Furthermore, CB13 MNPs had a significantly larger specific surface area than CB21 MNPs. The 3 MNPs shared a tendency to form aggregates of around 1 µm in the culture media, the CB21 aggregates being significantly larger than the TiO 2 aggregates. No endotoxin contamination was detected in any of the three MNP samples.

Cell localisation of MNP

Percentage of cells containing MNPs

Figure 3 shows the percentages of the 2 cell lines containing the three MNPs.

Time-response relationship

About 60% of MRC5 cells contained CB13 MNPs after 6h (Fig. 3A). This percentage increased to nearly 80% after 24h (p<0.05 vs. 6h) then remained stable until 48h. Moreover, the percentage of cells containing TiO 2 and CB21 MNPs also increased significantly over time.

Almost 95% of 16HBE cells accumulated CB13 MNPs after 6h and this value did not change over 24 and 48h (Fig. 3B). An absence of time-response effect was also observed with TiO 2 MNPs. Moreover, the percentage of cells containing MNPs after 6h was significantly lower with TiO 2 and CB21 MNPs than with CB13 MNPs (p<0.05).

Finally, with 16HBE cells, the percentage of cells containing CB13 and CB21 MNPs at 6h was significantly higher than MRC5 cells (p<0.05).

Dose response relationship

About 60% of MRC5 cells contained CB13 MNPs after exposure to 0.5 µg.cm -2 (Fig. 3C). This percentage increased to near 80% when cells were exposed to 5 µg.cm -2 (p<0.05 vs. 0.5 µg.cm -2 ) and no further increase was observed after exposure to 10 µg.cm -2 . Similar results were obtained with CB21 MNPs. A slightly different response was observed after exposure to TiO 2 MNPs, since the percentage increased significantly after exposure to 5 µg.cm -2 compared to 0.5 µg.cm -2 and then decreased when a dose of 10 µg.cm -2 was used.

Almost 80% of 16HBE cells accumulated CB13 MNPs after exposure to 0.5 µg.cm -2 and this value did not change when doses of 5 and 10 µg.cm -2 were used (Fig. 3D). A similar response was observed with TiO 2 MNPs. However, with CB21 MNPs, the percentage of cells containing MNPs increased dose-dependently between 5 and 10 µg.cm -2 (p<0.05 in both cases). Moreover, the percentage of cells containing MNPs after exposure to 0.5 µg.cm -2 was significantly lower with TiO 2 and CB21 MNPs than with CB13 MNPs (p<0.05).

No significant difference in the dose-response effect was found between the 2 cell lines, except for CB13 accumulation that seemed to occur in more 16HBE cells with the lowest dose.

Overall, these results show time-and dose-dependent modulations in the percentage of MRC5 cells containing the three MNPs. These modifications were not found consistently with 16HBE cells.

Mean number of aggregated MNPs in cells

Figure 4 shows the average number of aggregated MNPs observed in both cell lines.

Indeed, as accumulated MNPs were found chiefly as aggregates in both cell types, we therefore counted only MNP aggregates.

Time-response relationship

With MRC5 cells, we found a time-dependent increase in the number of CB13 MNP aggregates but not in the number of CB21 and TiO 2 MNPs (Fig. 4A). By contrast, a timedependent modification in the number CB21 and TiO 2 MNPs aggregates was observed in 16HBE cells (Fig. 4B). In neither cell type were there any major differences across the 3 MNP types. Finally, differences were found between the MNP aggregate numbers in 16HBE and MRC5 cells after 6h but not after 24h or 48h.

Dose response relationship

A dose-response effect was observed for the 3 MNPs in MRC5 cells (Fig. 4C). With 16HBE cells, a dose-response effect was noted only with TiO 2 MNPs (Fig. 4D). Finally, 16HBE cells contained larger aggregate numbers of CB13 and CB21 MNPs at 0.5 µg.cm -2 than did MRC5 cells (p<0.05).

Mean size of intracellular MNP aggregates

3.5.1. Mean size of individual aggregates (Fig. 5).

Figure 5 shows the mean size of individual MNP aggregates observed in both cell lines. As we performed these observations by TEM, a two-dimensional technique, the aggregate size was expressed as a surface (µm²).

Time-response relationship

In MRC5 cells, the mean size of individual CB13 and CB21 MNP aggregates increased transiently at 24h (Fig. 5A). After 6h, the mean size was significantly greater for TiO 2 MNP aggregates than for CB13 and CB21 MNP aggregates (6 fold increase, p<0.05 for both comparisons) and did not change significantly over time. A similar general pattern was observed in 16HBE cells (Fig. 5B) except that the transient increase was noted only with TiO 2 MNPs. No major differences were found between the 2 cell lines.

Dose response relationship

The size of MNP aggregates showed no major dose-dependency with either cell line (Fig. 5C and5D). In both cell lines, the size with exposure to 0.5 µg.cm -² was higher with TiO 2 MNPs than with the other 2 MNPs (p<0.05 for both comparisons). The size was greater in 16HBE cells than in MRC5 cells.

Percentage of cell surface area occupied by MNP aggregates (Fig. 6).

As compared to the size of individuals MNP aggregates, similar time-and dosedependencies of aggregate size were obtained when the results were expressed as the percentage of the cell surface area occupied by MNP aggregates on TEM sections (Fig. 6).

Cytotoxicity of MNPs in MRC5 and 16HBE cell lines

Figure 7 shows the percentage of viable MRC5 and 16HBE cells after 24h exposure to each MNP in a dose of 10 µg.cm -².

After 24h of exposure to 10 µg.cm -² of CB13 MNPs, the percentage of viable MRC5 cells decreased significantly to about 30%. No changes in 16HBE cell viability were noted under the same conditions. Furthermore, the viability of MRC5 and 16HBE cells did not change after exposure to CB21 and TiO 2 MNPs.

Discussion

The main results of our study are as follows: 1) MNP accumulation occurred in a high percentage of cells overall (60-80%), was close to the peak value after only 6h, and occurred even with the lowest dose of 0.5 µg.cm -²; 2) MNPs accumulated chiefly as aggregates in cytosolic vesicles, and the number of aggregates increased over time or with the dose, except with CB MNPs in 16HBE cells whose aggregate number was at the peak after 6h (CB13) or with the lowest dose studied (CB13 and CB21); and 3) with the shortest exposures and lowest doses, the size was higher overall for TiO 2 MNP aggregates than for the 2 CB MNP aggregates. Although several statistically significant differences between the 2 cell lines were found, these differences were quantitatively minor, indicating that the behaviour of the 2 cell lines was similar overall after exposure to all 3 MNP types. Thus, intracellular MNP accumulation seems to be a common and rapid phenomenon that occurs in both epithelial and mesenchymal cells. However, the chemical nature of the MNPs influences intracellular accumulation. Thus, compared to the 2 CB MNP types, TiO 2 MNPs had a larger mean aggregate size within cells and a smaller specific surface area and aggregate size in the culture media (Table 1). Finally, MNP accumulation was dissociated from cellular toxicity.

Indeed, CB13 was the only cytotoxic MNP type, and this cytotoxicity occurred only with the MRC5 cells.

The first aim of the study was to compare MNP accumulation in different cell types.

Since the respiratory system is a major route of MNP entry into the body, we used 16HBE and MRC5 cells as models of the bronchial epithelium and subjacent connective tissue, respectively. It is reasonable to use TEM to confirm cellular uptake of MNPs, TEM being a good qualitative method to analyse particle uptake. However, because any TEM picture is only reflecting one plane of cells, this technique is considered at most as a semiquantitative method to determine the MNP distribution in a whole cell. Therefore, to obtain reproducible and representative results, 50 cells on several different fields were observed and each entire cell was examined. In both cell types, MNPs accumulated rapidly, in more than half the cultured cells, but occupied a small percentage of the cell surface area (between 0.04 and 4%). Furthermore, in both cell types, invaginations of the plasmic membrane were observed and MNPs were found chiefly as aggregates located in cytosolic vesicles. These observations suggest an endocytosis-mediated mechanism of internalisation, as described previously [START_REF] Faklaris | Photoluminescent diamond nanoparticles for cell labeling: study of the uptake mechanism in mammalian cells[END_REF][START_REF] Saxena | Isolation and quantitative estimation of diesel exhaust and carbon black particles ingested by lung epithelial cells and alveolar macrophages in vitro[END_REF][START_REF] Singh | Endocytosis, oxidative stress and IL-8 expression in human lung epithelial cells upon treatment with fine and ultrafine TiO 2 : role of the specific surface area and of surface methylation of the particles[END_REF][START_REF] Stearns | Endocytosis of ultrafine particles by A549 cells[END_REF][START_REF] Thurn | Endocytosis of titanium dioxide nanoparticles in prostate cancer PC-3M cells[END_REF]. In these studies, various endocytic pathways have been suspected to be involved in MNP accumulation (e.g., macropinocytosis, clathrin-mediated endocytosis, or caveolae-mediated endocytosis). The results showed that macropinocytosis seemed to be the principal mechanism of MNP uptake. Our observations are in agreement with these results. Indeed, membrane invaginations and cytoplasmic vesicles are signs of an endocytic mechanism. Moreover, Hussain and coworkers showed that macropynocitosis was involved in 16HBE internalisation of the same CB MNP than those used in the present study [START_REF] Hussain | Oxidative stress and proinflammatory effects of carbon black and titanium dioxide nanoparticles: role of particle surface area and internalized amount[END_REF]. We did not find MNPs in the mitochondria or nuclei, in agreement with studies by [START_REF] Xia | Comparison of the abilities of ambient and manufactured nanoparticles to induce cellular toxicity according to an oxidative stress paradigm[END_REF] and [START_REF] Bhattacharya | Titanium dioxide nanoparticles induce oxidative stress and DNA-adduct formation but not DNAbreakage in human lung cells[END_REF]. However, in other studies that used primary human monocyte macrophages or mesenchymal stem cells, MNPs were found in these compartments [START_REF] Hackenberg | Silver nanoparticles: evaluation of DNA damage, toxicity and functional impairment in human mesenchymal stem cells[END_REF][START_REF] Porter | Uptake of C60 by human monocyte macrophages, its localization and implications for toxicity: studied by high resolution electron microscopy and electron tomography[END_REF], suggesting that the site of accumulation may depend on the MNP type and/or cell type. Overall, the data reported here suggest that MNPs may accumulate similarly in lung epithelial cells and fibroblasts.

We evaluated the influence of MNP physico-chemical characteristics on intracellular accumulation. We focused on MNP with a narrow range of diameters (13-21 nm), but differences in chemical composition (TiO 2 vs. CB), and specific surface area (CB13 vs.

CB21). Both TiO 2 and CB MNPs accumulated rapidly, in keeping with earlier studies in several lung models showing accumulation after only 6h [START_REF] Geiser | Ultrafine particles cross cellular membranes by nonphagocytic mechanisms in lungs and in cultured cells[END_REF][START_REF] Stearns | Endocytosis of ultrafine particles by A549 cells[END_REF]. For example, a flow cytometry study reported in 2011 showed that internalisation of SiO 2 MNPs measuring 50 to 300 nm by the alveolar epithelial cell line A549 cells occurred chiefly within the first 2 hours and reached a plateau after 6 hours [START_REF] Shapero | Time and space resolved uptake study of silica nanoparticles by human cells[END_REF]. The percentage of cells containing MNPs was similar with the 3 MNP types, all of which were visible as aggregates surrounded by a membrane, and, rarely, as isolated particles.

Aggregates may reflect internalisation of isolated MNPs and/or of MNP aggregates. The granulometric evaluation of MNPs in the culture media strongly supports the second possibility, since all 3 MNP types were detected as aggregates measuring about 1 µm in diameter. Furthermore, it has been shown that non-phagocytic cells internalise MNP aggregates more easily than isolated MNPs [START_REF] Andersson | Polymorph-and Size-Dependent Uptake and Toxicity of TiO(2) Nanoparticles in Living Lung Epithelial Cells[END_REF][START_REF] Hackenberg | Silver nanoparticles: evaluation of DNA damage, toxicity and functional impairment in human mesenchymal stem cells[END_REF]. Therefore, the cells probably internalised the MNPs chiefly as aggregates. The mean size of MNP aggregate within cells, as determined using TEM to evaluate sections, seemed to be smaller (about 0.1µm²) than the mean diameter of aggregates in culture medium, as determined using PCS. If the particles have a round-like shape, then the aggregate size in the culture medium should be greater than 0.8µm², and MRC5 and 16HBE cells may unable to internalize the largest aggregates. However, these considerations must be viewed with caution because we compared a measured aggregate size within cells (obtained using TEM)

to a calculated aggregate size in the culture medium. Overall, our results suggest that the chemical nature and specific surface area of the 3 MNPs influenced neither the kinetics of MNP internalisation nor the percentage of cells containing accumulated MNPs. Furthermore, all 3 MNP were found as aggregates within cells.

Internalised TiO 2 MNPs behaved differently from accumulated CB MNPs. Thus, the size of intracellular TiO 2 MNP aggregates was higher overall than that of the CB MNP aggregates. This different was seen with both cell types, starting at 6h and with the lowest dosages. This result suggests that TiO 2 MNPs may penetrate the cell and accumulate in a single vesicle that is constantly full. [START_REF] Pan | Adverse effects of titanium dioxide nanoparticles on human dermal fibroblasts and how to protect cells[END_REF] obtained similar results using primary dermal fibroblasts incubated with rutile TiO 2 MNPs. However, they also observed that anatase particles produced huge holes in the cell cytoplasm, a finding not replicated in our study. MNP size was similar in the study by [START_REF] Pan | Adverse effects of titanium dioxide nanoparticles on human dermal fibroblasts and how to protect cells[END_REF] and in our study, but the discrepancy may be related to other differences in the experimental conditions (e.g., presence vs. absence of serum in the culture media with the MNPs and primary vs.

fibroblastic cell line). Since the 3 MNPs used in our study were closely similar in size and shape, these 2 parameters were probably not involved in the difference between TiO 2 and CB MNPs. A role for the specific surface area of the MNPs [START_REF] Hsiao | Effects of various physicochemical characteristics on the toxicities of ZnO and TiO₂ nanoparticles toward human lung epithelial cells[END_REF][START_REF] Yue | Particle size affects the cellular response in macrophages[END_REF] is unlikely since this parameter was similar for the TiO 2 and CB21 MNPs. Furthermore, specific surface area was greater for the CB13 MNPs than for the CB21 MNPs, whereas the size of the intracellular aggregates was similar for these 2 MNPs. The chemical nature of the MNPs may affect the capacity for aggregation, leading to differences in intracellular behaviour. There is evidence that TiO 2 MNPs form aggregates easily and rapidly in (hydrophilic) polar environments such as culture media [START_REF] Geiser | Ultrafine particles cross cellular membranes by nonphagocytic mechanisms in lungs and in cultured cells[END_REF] internalised MNPs do not always exert cytotoxic effects such as those described in murine macrophages exposed to gold nanoparticles [START_REF] Zhang | Uptake of gold nanoparticles in murine macrophage cells without cytotoxicity or production of proinflammatory mediators[END_REF], and that 2) the biological responses induced by MNP depend on the cell type. Inversely, we demonstrated previously that carbon nanotubes induced cytotoxicity in epithelial cells in the absence of internalisation [START_REF] Tabet | Adverse effects of industrial multiwalled carbon nanotubes on human pulmonary cells[END_REF]. The relationship between intracellular accumulation and cellular responses such as inflammation remains to be investigated. Another remarkable fact is that CB13 MNPs, the only cytotoxic particles in our study, also had the highest specific surface area. This parameter seems to play a crucial role in the biological effects of CB13 MNPs. In vivo and in vitro biological responses such as oxidative stress or inflammation correlate closely with the specific surface area of particles such as CB or silica [START_REF] Brown | Size-dependent proinflammatory effects of ultrafine polystyrene particles: a role for surface area and oxidative stress in the enhanced activity of ultrafines[END_REF][START_REF] Hussain | Oxidative stress and proinflammatory effects of carbon black and titanium dioxide nanoparticles: role of particle surface area and internalized amount[END_REF][START_REF] Stoeger | Instillation of six different ultrafine carbon particles indicates a surface area threshold dose for acute lung inflammation in mice[END_REF][START_REF] Waters | Macrophage responses to silica nanoparticles are highly conserved across particle sizes[END_REF]. However, this parameter was unrelated to accumulation in our study, emphasising the complexity of MNP effects on cells.

Conclusion

In conclusion, we showed that MNPs were widely and rapidly internalised by bronchial cells and pulmonary fibroblasts. In both cell types, MNPs accumulated as aggregates in cytosolic vesicles and were absent from the mitochondria or nuclei. MNP accumulation was rapid, and dependent on exposure time or concentration. Overall, CB and TiO 2 MNP had similar accumulation patterns, although TiO 2 aggregates had a higher size than CB aggregates. Intracellular MNP accumulation was dissociated from cytotoxicity.

These results shed new light on the interactions between MNPs and cells and indicate that internalisation is a relatively stereotyped cellular response to MNP exposure, at least in nonphagocytic cells.

Tables Table 1. Physico-chemical characterisation of manufactured nanoparticles.

Zeta potential and granulometry were evaluated in DMEM without foetal bovine serum.

Corresponding data are presented as the mean ± SEM. * Significantly different compared to CB21 (p<0.05). nd = not detected. # Significant difference between cell types (p < 0.05). Y axis: number of MNPs in cells.

3.

  Mean number of aggregated MNPs in cells (per cell) 4. Mean size of intracellular MNP aggregates (per cell) 4.1. Mean size of each aggregate 4.2. Percentage of cell surface area occupied by MNP aggregates

Figures 1

 1 Figures 1 and 2 show typical TEM images of MRC5 and 16HBE cells exposed to 5 µg.cm -2 of CB13, CB21 or TiO 2 MNP for 6 hours. The results of MRC5 exposure to 5 µg.cm -² of MNPs are shown in figure 1. Panel A illustrates the CB13 MNP distribution in a MRC5 cell. CB13 MNPs were found as aggregates in the cytosol, usually in vesicle-like compartments and in rare instances free in the cytoplasm, being impossible to quantify such rare events. No particles were seen in the mitochondria, nuclei, or other organelles. Panels B and C of figure 1 show CB21 and TiO 2 MNP accumulation in the MRC5 cell line. Both MNP types were also found as aggregates in cytosolic vesicles. Panels A, B and C of figure 2 display the distributions of the 3 MNPs in 16HBE bronchial cells. As previously described in MRC5 cells, each MNP type was usually seen as aggregates in cytoplasmic vesicles and less often visualised free in the cytosol. Again, no MNPs were found in mitochondria or nuclei.

Fig. 1 .

 1 Fig. 1. Transmission electron microscopy views of MRC5 cells incubated with 5 µg.cm -2 of manufactured nanoparticles (MNPs) for 6 hours. (A) Typical MRC5 cell with CB13 MNPs within a vesicle (scale bars: 2 µm on the left, 0.5 µm on the right). (B) CB21 MNPs being internalised by a MRC5 cell (scale bars: 5 µm on the left, 0.5 µm on the right). (C) MRC5 cell with TiO 2 MNPs in a vesicle (scale bars: 5 µm on the left, 0.5 µm on the right).

Fig. 2 .

 2 Fig. 2. Transmission electron microscopy views of 16HBE cells incubated with 5 µg.cm -2 of manufactured nanoparticles (MNPs) for 6 hours. (A) Typical 16HBE cell with CB13 MNPs in a vesicle (scale bars: 10 µm on the left, 0.5 µm on the right). (B) 16HBE cell with a vesicle containing CB21 MNPs (scale bars: 5 µm on the left, 0.5 µm on the right). (C) 16HBE cell with TiO 2 MNPs in a vesicle (scale bars: 2 µm on the left, 0.5 µm on the right).

Fig. 3 .

 3 Fig. 3. Percentage of cells containing manufactured nanoparticles (MNPs). (A) MRC5 cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h, or 48h. (B) 16HBE cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h or, 48h. (C) MRC5 cells incubated for 24h with 0.5, 5, or 10 µg.cm -2 of MNPs. (D) 16HBE cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. The Figure displays the results of one representative experiment (N = 50 cells) of 3 independent experiments. * Significant difference across MNP types (p < 0.05). # Significant difference between cell types (p < 0.05). Y axis: percentage of cells containing MNPs.

Fig. 4 .

 4 Fig. 4. Number of manufactured nanoparticles (MNPs) in cells. (A) MRC5 cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h, or 48h. (B) 16HBE cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h, or 48h. (C) MRC5 cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. (D) 16HBE cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. The Figure displays the results of one representative experiment (N = 30-40 cells) of 3 independent experiments. Data are mean ± SEM. * Significant difference across MNP types (p < 0.05).

Fig. 5 .

 5 Fig. 5. Mean size of individual manufactured nanoparticles (MNPs) aggregates in cells. (A) MRC5 cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h, or 48h. (B) 16HBE cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h or 48h. (C) MRC5 cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. (D) 16HBE cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. The Figure displays the results of one representative experiment (N = 30-40 cells) of 3 independent experiments. Data are mean ± SEM. * Significant difference across MNPs (p < 0.05). # Significant difference between cell types (p < 0.05). Y axis: mean size of individual MNP aggregates (µm²).

Fig. 6 .

 6 Fig. 6. Percentage of cell surface area occupied by manufactured nanoparticles (MNPs) aggregates. (A) MRC5 cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h or 48h. (B) 16HBE cells incubated with 5 µg.cm -2 of MNPs for 6h, 24h or 48 hours. (C) MRC5 cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. (D) 16HBE cells incubated for 24h with 0.5, 5 or 10 µg.cm -2 of MNPs. The Figure displays the results of one representative experiment (N = 30-40 cells) of 3 independent experiments.. Data are mean ± SEM. * Significant difference across MNP types (p < 0.05). # Significant difference between cell types (p < 0.05). Y axis: percentage of cell surface area occupied by MNP aggregates.

Fig. 7 .

 7 Fig. 7. Cytotoxicity of manufactured nanoparticles (MNPs) in MRC5 and 16HBE cells. Cells were exposed to 10 µg.cm-² of MNPs for 24 h. Cell viability was measured using the WST-1 assay. Data are the percentages of surviving cells relative to the control reported as the mean ± SEM of three independent experiments. * Significantly different from control (p < 0.05).

  . The zeta potential data showing that TiO 2 MNPs in DMEM had the lowest aggregation capacity and the smaller size of the TiO 2 MNP aggregates compared to the CB21 MNP aggregates argue against this possibility. Another possibility is disaggregation of CB MNPs within cells, leading

	to a smaller size compared to TiO 2 MNP aggregates. Although data supporting this
	hypothesis was observed in MRC5 cells between 24 and 48h (fig. 5A), a similar phenomenon
	was observed concerning TiO 2 aggregates, thus ruling out this hypothesis. Further work is
	needed to explain the difference in aggregate size between TiO 2 MNPs and CB MNPs.
	We evaluated MNP cytotoxicity by performing the WST-1 assay on MNP-exposed
	cells. Of the 3 MNP types, only CB13 induced a decrease in MRC5 cellular viability. None of
	the 3 MNP types affected the viability of 16HBE cells. These results suggest that 1)

Acknowledgements

This work was supported by the "Agence Nationale de la Recherche France" (Nanotox project, ANR N° 05979-5 SET 024-01), by the Department of Paris, by the "Agence nationale de sécurité sanitaire de l'Alimentation, de l'Environnement et du Travail" for a PhD grant and funding of Esther Belade, and by the "ABIES" PhD program for a PhD grant of Lucie Armand.

Conflict of Interest statement

The authors declare that there are no conflicts of interest.


Chesner Désir

Caroline Petitjean
email: caroline.petitjean@univ-rouen.fr

Laurent Heutte

Mathieu Salaün

Luc Thiberville

An SVM distal lung image classification using texture descriptors

Keywords: Image classification, feature extraction, feature selection, LBP, SIFT, lung, alveoli

Introduction

The lungs are the essential respiration organ. They are divided into two anatomic and functional regions: the air conduction system (that includes the trachea, bronchi, and bronchioles) and the gas-exchange region made of alveolar sacs. Whereas the conduction airways can be explored during bronchoscopy, the alveolar region is currently investigated only in vitro, using invasive techniques such as open lung biopsies. Recently, a new endoscopic technique, called Fibered Confocal Fluorescence Microscopy (FCFM), has been developed that enables the visualisation of the more distal regions of the lungs in vivo [START_REF] Thiberville | In vivo imaging of the bronchial wall microstructure using fibered confocal fluorescence microscopy[END_REF]. The technique is based on the principle of fluorescence confocal microscopy, where the microscope objective is replaced by a fiberoptic miniprobe, made of thousands of fiber cores. The miniprobe can be introduced into the 2 mm working channel of a flexible bronchoscope to produce in vivo endomicroscopic imaging of the human respiratory tract in real time. This very promising technique could replace lung biopsy in the future and might prove to be helpful in a large variety of diseases, including interstitial lung diseases [START_REF] Thiberville | Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy[END_REF].

Images acquired with FCFM represent the alveolar structure (Figures 1 and2), which can be altered by distal lung pathologies. Medical experts are still examining visually these images to search for typical properties that could be of interest for discriminating pathological subjects from healthy ones. Our aim is thus to provide the clinician with a computer aided-diagnosis tool, so as to help him to analyze these images, by automatically classifying FCFM images into healthy or pathological ones. The most crucial step when designing an automatic image classification system is the choice of relevant features to describe the image. In a preliminary work [START_REF] Petitjean | Classification of in-vivo endomicroscopic images of the alveolar respiratory system[END_REF], we have designed a first classification system, so as to discriminate healthy cases from pathological cases, in which images are described by an ad hoc feature vector based on a visual analysis of the images. But several generic, low-level texture descriptions have been proved very efficient for various image description tasks [START_REF] Mikolajczyk | A performance evaluation of local descriptors[END_REF]. We thus investigate in this paper those state-of-the-art feature descriptors to describe our FCFM images and compare their results to the ones obtained with the ad hoc feature vector so as to determine which descriptors are best adapted to the classification of FCFM images. Using a Support Vector Machine (SVM) for classification, we show that one low-level feature vector is better adapted to the discrimination between pathological and healthy images. Furthermore, we point out that some insight about these images can be gained by studying, through a feature selection process, the most relevant features.

The remainder of this paper is thus organized as follows: the imaging technique is detailed in Section 2, our classification system is described in Section 3, and results and discussion are provided in Section 4. Conclusion is presented in Section 5 along with perspectives for this work.

FCFM images

FCFM images represent the alveolar structure, made of elastin fiber (Figures 1-4), with a lateral resolution of 3.5 µm. The FCFM imaging technique enables to observe the elastin as it is the main endogenous fluorophore at 488 nm in the respiratory tract from the bronchus to the alveoli. The elastin framework appears as a network of (almost) continuous lines in healthy subjects, as shown in Figure 1. In [START_REF] Thiberville | Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy[END_REF], morphometric analysis of the alveolar structures was performed, based on the elastin distribution observed with FCFM. We showed that results were reproducible and concordant to ex vivo morphometric analysis based on lung stereology. Our study confirmed that the FCFM observations were reproducible between subjects and between different alveolar areas in the same subject. In the bronchus, the elastin that can be imaged with FCFM is a component of the basement membrane. However, in the alveoli, elastin is the main element of the elastic structure that frames the alveolar ducts and sacs and originates the fluorescent signal. The alveolar walls are not visible during FCFM observation in vivo, but only the elastin framework of the interstitial tissue. In this study, the aim is thus to perform an automatic classification of alveolar FCFM images, where the signal only comes from the elastin that belongs to the acinar elastic framework.

Images acquired on smoking subjects differ from the ones acquired on non-smoking ones, notably because of the presence of macrophages, cells which digest cellular debris (cf. Figures 3 and4). In smoker subjects, the identification of alveolar macrophages using FCFM relies on bronchoalveolar lavage analysis from smokers using fluorescence and conventional microscopy, which confirmed that these fluorescent cells correspond exclusively to alveolar macrophages [START_REF] Thiberville | Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy[END_REF]. These cells are made visible on these images because of tobacco-tar induced fluorescence. We also demonstrated by in-situ microspectrometry experiments that whereas the main endogenous fluorophore is elastin in non-smokers, it is the tobacco-tar in smokers [START_REF] Thiberville | Human in vivo fluorescence microimaging of the alveolar ducts and sacs during bronchoscopy[END_REF].

In this context, a clinical trial is currently being conducted that collects FCFM images in several pathological conditions of the distal lungs, including:

• post radiation therapy fibrosis, silicosis, bronchioloalveolar carcinoma, systemic sclerosis, connective lung (Sharp syndrome), sarcoidosis with interstitial involvement, alveolar proteinosis, asbestos related interstitial fibrosis, Bleomycin induced lung fibrosis, Furadantine induced lung fibrosis, idiopathic pulmonary fibrosis for non-smoking patients,

• sarcoidosis with interstitial involvement, systemic sclerosis, Amiodarone induced intersitial pneumoniae, smoke induced respiratory bronchiolitis, Langerhans histiocytosis, alveolar proteinosis for smoking patients.

The trial also includes images acquired on healthy smoking and non-smoking volunteers. The images are selected from the in vivo FCFM image database by two medical experts, and are labeled as (i) healthy if they are obtained from the healthy volunteer group, and (ii) pathological if they are obtained from a patient with a diagnosed interstitial lung disease and if the image was obtained from a lung segment that appeared abnormal on the chest CT Scan. Note that there is no correlation study between histology and FCFM features intended in this work. The description of the FCFM features is beginning and the description of the FCFM imaging in interstitial lung diseases is not yet established. Therefore, the study is based on images from miscellaneous infiltrative lung diseases patients, as well as healthy volunteers, and is not designed to authorize a lesion/FCFM feature comparison at this point. The database of FCFM images, that includes a total of 133 non-smoker images and 93 smoker images, is used for the elaboration of the automatic image classification system, described in the following section.

Image classification method

Feature extraction

In image classification system, feature extraction is still a critical step. Features are usually adapted to the image content. For instance, blood cell images can be described by frequency analysis, using Fourier-Mellin transform [START_REF] Dahmen | Automatic classification of red blood cells using gaussian mixture densities[END_REF]; wooden image texture and color can be described by LBP (Local Binary Patterns) descriptor [START_REF] Ojala | Gray scale and rotation invariant texture classification with local binary patterns[END_REF]; protein sub-cellular images can be described by their texture, with cooccurrence matrix using Haralick statistical parameters [START_REF] Haralick | Textural features for image classification[END_REF]. The image description can also be local, e.g. by extracting keypoints and describing their neighbourhoods using gradient as local features [START_REF] Mikolajczyk | Scale and affine invariant interest point detectors[END_REF]. Thus, an initial approach to numerically describe FCFM images can be based on a visual analysis of these images. As shown in Figures 1 and3, the alveolar structure in healthy subjects can be described as contrasted continuous lines and curves. On the other hand, in the pathological subset, the disorganization of the meshing is illustrated by the numerous irregularities and the tangle of the fibered structures (Figures 2 and4). Differences are mostly visible for the structure shape, the image texture and the contrast, implying that numerical features could therefore be chosen among the ones that best describe the visual differences from these three points of view.

The structure contrast can be characterized by studying first order statistics of the distribution of gray levels, and computing pixel densities. Because structures also show local differences, local parameters are computed on subwindows of the image. Subwindows are obtained by dividing by 4 the image height and width (Figure 5). Features characterizing the image contrast are (i) first order statistics on global and local histogram: mean, variance, skewness, kurtosis, entropy, (ii) global and local pixel densities obtained on binarized images using Otsu thresholding, (iii) the sum of the image gradient values, obtained using Prewitt operator. We could suppose that pathological images will have high values for densities, because of the tangle of the fibered structure. The complexity of the structure shape can be characterized by studying the image skeleton. After skeletonization [START_REF] Dibajaa | Skeletonization algorithm running on pathbased distance maps[END_REF] obtained on the binary image, the number of junction points is computed. One can suppose that on clearly organized, healthy images, this number will be small, contrary to pathological images where the meshing mess will induce a higher number of points. At last, the image texture can be characterized by Haralick parameters computed from gray level cooccurrence matrices (GLCM) [START_REF] Haralick | Textural features for image classification[END_REF]. The GLCM provide the joint distribution of gray-level intensities between two image points. These two points are located according to several configurations, along 4 directions (0˚, 45˚, 90˚, 135˚) in our case. Energy, contrast, homogeneity and correlation are then computed on the GLCM.

This first characterization of FCFM images leads to a 120-feature vector called ad hoc feature vector, as shown in Table 1.

On the other hand, FCFM images can also be described using low-level, generic feature vectors, thus making little assumption about the image content. An image texture can be classically characterized with:

• Haralick parameters [START_REF] Haralick | Textural features for image classification[END_REF], computed from GLCM, as explained above. In order to design a feature vector based exclusively on Haralick parameters, we have included almost all of the features originally proposed by Haralick, obtained on a large number of GLCM. Thus, the following features are computed: energy, contrast, correlation, variance, inverse different moment, entropy, sum average, sum entropy, sum variance, difference entropy, difference variance, and two information measures of correlation. The only discarded feature is the maximum correlation coefficient, which is too computationally expensive. To these 13 parameters we added dissimilarity, a measure of homogeneity [START_REF] Pratt | Digital Image Processing[END_REF]. All these 14 parameters are computed over 10 co-occurrence matrices, obtained via the following classical translation vectors:

[0 1], [-1 1], [-1 0], [-1 -1], [0 2], [-1 2], [-1 -2], [-2 1], [-2 0], [-2 -1].
• the SIFT (Scale Invariant Feature Transform ) algorithm, shown to be one of the most efficient for image description [START_REF] Mikolajczyk | A performance evaluation of local descriptors[END_REF]. SIFT consists in extracting robust keypoints in the image, and describing the neighborhood of each of them with a histogram of oriented gradients [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF]. The final feature vector represent a 128-bin histogram of the gradient values in each keypoint neighborhood.

• the LBP (Local Binary Patterns) operator [START_REF] Ojala | Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[END_REF], that consists in computing the distribution of binary patterns in the circular neighborhood of each pixel. These patterns are obtained by thresholding neighboring pixels compared to the central pixel. This neighborhood is characterized by a radius R and a number of neighbors P , as shown on Figure 6(a). The possible patterns obtained for resolution (P = 8, R = 1) are provided in Figure 6(b): for example pattern #4 detects edges, whereas pattern #8 detects flat areas. In some preliminary experiments [START_REF] Saint-Réquier | Characterization of endomicroscopic images of the distal lung for computeraided diagnosis[END_REF], we have shown that a better parameterization of LBP for FCFM images is to concatenate the LBP values for neighborhood set of values (P = 8, R = 1), (P = 16, R = 2) and (P = 24, R = 3).

Table 2 summarizes all the feature vectors tested in this study and their respective length.

Classifier

On the previously cited features an SVM classifier is implemented [START_REF] Vapnik | The nature of statistical learning theory[END_REF]. SVM is one of the best performing and most used classification algorithm. The support vector machine classifier is a binary classifier algorithm that looks for an optimal hyperplane as a decision function in a high-dimensional space. The associated kernel function is classically chosen as a cubic polynomial kernel. In order to improve the prediction performance of the classifier, and to provide faster and more cost-effective decision, variable selection [START_REF] Guyon | An introduction to variable and feature selection[END_REF] can be used. It can also provide a better understanding of which visual features discriminate the data. Support Vector Machine with Recursive Feature Elimination (SVM-RFE) is one way to perform variable selection [START_REF] Rakotomamonjy | Variable selection using SVM-based criteria[END_REF]. The goal is to find a subset of size r among d variables (r < d) which maximizes the performance of the predictor. The method is based on a sequential backward selection. One feature at a time is removed until r features are left. The removed variables are the ones that minimize the variation of the margin.

Experimental results

Experimental protocol

The SVM classifier and SVM-RFE based feature selection [START_REF] Rakotomamonjy | Variable selection using SVM-based criteria[END_REF] are implemented using the SVM and Kernel Methods Matlab Toolbox [START_REF] Canu | SVM and kernel methods Matlab toolbox[END_REF]. The number of images for the smoker and the non-smoker groups is provided in Table 3. Because of the relatively small number of non-smoker and smoker images, a 10-fold cross-validation process is used. The system performance is assessed with the mean recognition rates ± standard deviation on the 10 folds.

Table 3: Number of FCFM images for the smoker and non-smoker databases X X X X X X X X X X X X

Classification results

From the recognition rates reported in Table 4, one can see that texturebased feature vectors outperform the ad hoc feature set. In particular, the LBP operator seems to provide an adequate description for discriminating healthy and pathological FCFM images. Note that results obtained on the smoker group are always better than those obtained on the non-smoker group, but not with the SIFT based feature vector. As a matter of fact, the SIFT algorithm looks for some salient points in the image. In the smoker images, these points can be hidden behind the macrophages, thus preventing a better discrimination of these images (Figure 7).

Since the LBP-based feature vector offers the best performance, it is selected for further analysis. The confusion matrix for the LBP vector, provided in Table 5, shows that the system performs well on healthy images for both groups, since more than 96% of healthy images have been well classified. The false alarm rate, i.e healthy patients mistaken for pathological ones, is quite similar for both groups. However, non detection, i.e. pathological subjects mistaken for healthy ones, is more at risk than false alarm. This non detection rate, which reaches 15.83% for the non-smoker group, leaves room for improvement.

In the next section, the results of a feature selection process applied on the LBP feature vector are presented, to refine the interpretation of FCFM image classification.

Smoking subjects

Non-smoking subjects

Feature selection

The SVM-RFE provides a rank for each feature of the LBP-based feature set, thus allowing the most discriminative ones to be identified. To fairly assess the effect of feature selection, the 10 folds have been divided into a learning set (6 folds), used to rank the features with the SVM-RFE, a validation set (3 folds) to determine the number of features to be obtained, and a test set (last fold) is finally used to evaluate a classical SVM in the selected subspace. Note that since variance on the results is quite high (cf. Table 4) due to the size of the database, the features retained for each validation fold are not always the same. The total selected feature space is thus the union of the feature sets selected for each fold during the validation process.

To determine the adequate number of features that must be provided to the predictor, we have computed a statistical test based on the variance of the test error on the validation folds. This test is used to select, among the list of ranked features provided by the SVM-RFE on the learning folds, those features that do not perturb this variance signicantly. Let X i be the mean recognition rate obtained with the first i ranked features (from the best one to the ith one) on the validation folds. Given a sample X = {X 1 , ...X n } of n observations (n = 54 for our LBP-based feature set) assumed to be i.i.d, and an expected variance s on this sample, the following statistics is computed:

T = (n -1) • S * 2 s 2
where 2 is the unbiased estimation of the real variance and E(X) the expectation of X.

S * 2 = 1 n-1 • n i=1 [X i -E(X)]
T follows a χ 2 distribution with (n -1) degrees of freedom. For a determined level of confidence (1α), χ 2 statistical tables provide the degree of significance of the difference between the real value of the variance S * 2 of our sample and the expected variance s. In our experiments, the level of confidence has been fixed to 1α = 95% and two values for the expected variance have been tested, namely s = 5% and s = 2.5%.

Table 6 shows the results obtained on the test folds (mean ± stdev) with a classical SVM: in the original feature space (no RFE) and in the selected subspace obtained for s = 5% and s = 2.5%. We can observe that as expected, no or few features are discarded for s = 2.5%, whereas for s = 5% the number of features drops down to 40 for non-smoker images, and 25 for smoker images, without decreasing significantly recognition accuracy, at least on the smoker group. These results confirm that a part of the features may be used to discriminate between healthy and pathological images. Investigating those retained features allows us to gain some insight about the usefulness of these features. We have observed that the most discriminating features are mostly the binary patterns that represent edges in the images whereas the discarded ones rather represent flat areas. This phenomenon is illustrated in Figure 8 for one resolution level (namely (P = 24, R = 3)). For each pixel of the original image, the presence of an edge pattern is indicated by a white pixel in Figure 8(b), and the presence of a flat area pattern is similarly indicated in Figure 8(c). Note how edge patterns allow for better representation of both classes (pathological and healthy) and thus, better discrimination.

Conclusion

The present work deals with the classification of a new category of images of the distal lung. The images were acquired using fibered confocal fluorescence microscopy, a technique that enables the observation of in vivo alveolar structures. Such images are not well described so far and are difficult to discriminate by pathologists and respiratory physicians. In this paper, we have proposed several possible descriptions for these images, one based on their visual analysis, and the other ones texture-based. These are incorporated into a classification system, so as to aid the clinician to discriminate between healthy and pathological subjects. Best performance is obtained using the LBP based description of the images, on both non-smoker and smoker groups. A feature selection process has allowed to extract the most discrimi-native features, which mostly represent the images edges, without decreasing significantly classification accuracy. This result could guide the clinician to focus on the image parts that are the most discriminant. Other potential applications include the automated recognition of the area to biopsy during endoscopy.

However, the current database should be extended to confirm these first results. Because the clinical trial is ongoing, this will be feasible in the near future. In order to improve the reliability of the system, the non detection rate could be reduced by introducing for instance a reject rule. Classification methods could also give information about which part of the alveolar structure might be more altered by pathologies. A future goal will also be to discriminate between different interstitial lung diseases (abestosis, systemic sclerosis, fibrosis, sarcoidosis).

Figure 1 :

 1 Figure 1: FCFM images of non-smoking healthy subjects

Figure 2 :

 2 Figure 2: FCFM images of non-smoking pathological subjects

Figure 4 :

 4 Figure 4: FCFM images of smoking pathological subjects. Notice the presence of macrophages (white spots).

Figure 5 :

 5 Figure 5: From left to right: original FCFM image, 16 subwindows, binarized image, skeleton on binarized image

( a )Figure 6 :

 a6 Figure 6: Local binary patterns (Figure (b) is from [4], with kind permission from Springer Sci- ence+Business Media)

Figure 7 :

 7 Figure 7: (a) Original FCFM images and (b) their corresponding relevant SIFT keypoints. Note how the presence of macrophage prevent from correctly highlighting the underlying network.

  FCFM images (b) Edge patterns (c) Flat area pattern

Figure 8 :

 8 Figure 8: Original FCFM images with a representation of the presence of flat area and edge LBP patterns

Table 1 :

 1 Ad hoc feature set

		Features	Number
		Global histogram statistics	5
		Local histogram statistics	80
	Contrast	Density	1
		Local densities	16
		Sum of image gradient	1
	Shape	Number of junction points in skeleton	1
	Texture	Haralick parameters	16
		Total	120

Table 2 :

 2 Feature vectors used to characterize FCFM images

	Feature vector name	Number of features
	Ad hoc description	120
	Haralick parameters on GLCM	140
	LBP	54
	SIFT	128

Table 4 :

 4 Recognition rates obtained with the different feature vectors

	Non-smoker	Smoker

Table 5 :

 5 Confusion matrices of pathological (P) vs. healthy (H) classification for the LBP based feature vector

		Non-smoker	Smoker
		Predicted labels	Predicted labels
		P	H	P	H
	True P 84.17±16.01% 15.83±16.01% 93.33±11.06% 6.67±11.06%
	labels H	4±4.90%	96.00±4.90%	3.33±10.00% 96.67±10.00%

Table 6 :

 6 Recognition rates obtained with the LBP feature vectors using SVM-RFE (in brackets the number of retained features)

			Non-smoker	Smoker
		no RFE 90.08± 8.47% (54) 95.00± 6.67% (54)
	LBP	2.5%	90.08± 8.47% (54) 91.67± 10.54% (31)
		5%	84.75± 12.79% (40) 94.


Damien Grosgeorge
email: damien.grosgeorge@univ-rouen.fr

Caroline Petitjean

Su Ruan

Segmentation d'images par coupe de graphe avec a priori de forme

Keywords: Image segmentation, graph cut, shape prior, principal component analysis (PCA)

La segmentation d'images par coupe de graphe permet d'obtenir très rapidement, par minimisation globale d'une fonction de coût appropriée, une partition binaire objet/fond d'une image 2D. Quand la forme de l'objet est connue et que les contours de l'objet sont mal définis, le processus de segmentation peut être efficacement guidé par un a priori. Dans cet article, nous proposons une approche de segmentation par coupe de graphe, s'appuyant sur un a priori de forme obtenu par analyse en composantes principales. Nous introduisons dans la fonctionnelle d'énergie un terme supplémentaire prenant en compte cet a priori. Notre approche a été testée et évaluée quantitativement sur une application difficile d'imagerie médicale, la segmentation du ventricule droit en IRM cardiaque, pour laquelle nous disposons d'une base d'images importante et de la vérité terrain (segmentation manuelle). Les résultats obtenus sont encourageants et plus précis que ceux obtenus par l'approche classique de coupe de graphe utilisant uniquement les informations des niveaux de gris.

Mots Clef

Segmentation d'image, coupe de graphe, a priori de forme, analyse en composantes principales (ACP).

Introduction

En vision par ordinateur, de nombreux problèmes peuvent se mettre sous la forme de la minimisation d'une fonctionnelle d'énergie : la segmentation, la restauration, le recalage, etc. En segmentation d'images, les méthodes variationnelles bien connues sont par exemple les contours actifs [START_REF] Kass | Snakes : Active contour models[END_REF][START_REF] Cohen | Finite element methods for active contour models and balloons for 2D and 3D images[END_REF] et les approches par courbes de niveaux [START_REF] Sethian | Level Set Methods and Fast Marching Methods[END_REF][START_REF] Osher | Geometric Level Set Methods in Imaging, Vision and Graphics[END_REF]. Dans ce domaine, ces fonctionnelles d'énergie intègrent en général un terme d'attache aux données incorporant des informations sur les contours de l'image (par exemple basées sur le gradient de l'image) et un terme de régularisation permettant de maîtriser l'aspect du contour. Cependant, l'optimisation peut poser problème lorsque la fonctionnelle d'énergie est non-convexe et présente des minima locaux. D'autre part, notons que dans ces approches, l'image est considérée comme un domaine continu et la nécessaire discrétisation de l'espace et du temps peut poser des problèmes de stabilité. Offrant des solutions aux problèmes cités précédemment, les méthodes fondées sur les coupes de graphe ont suscité un fort intérêt depuis l'article fondateur de Boykov et Jolly [START_REF]Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images[END_REF]. En s'appuyant sur des algorithmes issus de la communauté combinatoire, elles permettent d'optimiser des fonctions de coût de manière globale, et ainsi d'éviter les minima locaux. D'autre part, en considérant l'image comme un graphe, la stabilité numérique est assurée. La segmentation d'images par coupe de graphe tel que présentée dans [START_REF]Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images[END_REF] autorise également de manière très souple une interaction avec l'utilisateur et permet d'obtenir une convergence rapide en 2D. Elle a donné lieu à de nombreux développements théoriques [START_REF] Kolmogorov | What metrics can be approximated by geo-cuts, or global optimization of length area and flux[END_REF] et applicatifs [START_REF] Boykov | Interactive organ segmentation using graph cuts[END_REF][START_REF] Pundlik | Iris segmentation in non-ideal images using graph cuts[END_REF][START_REF] Aslan | A novel 3D segmentation of vertebral bones from volumetric CT images using graph cuts[END_REF][START_REF] Garcia-Lorenzo | Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts[END_REF]. Dans les applications où la forme à segmenter est connue a priori (par exemple un organe en imagerie médicale), le processus de segmentation peut être guidé par un modèle de forme ou des contraintes sur celle-ci. Pour des images ayant un contraste faible ou un taux de bruit élevé, une telle contrainte permet d'améliorer la précision de la segmentation. Dans le cadre des modèles déformables par exemple, l'intégration de contraintes de forme a été largement étudiée [START_REF] Cootes | Active shape models -their training and application[END_REF][START_REF] Rousson | Shape priors for level set representations[END_REF][START_REF] Cremers | A review of statistical approaches to level set segmentation : Integrating color, texture, motion and shape[END_REF], avec des modèles basés sur une analyse en composantes principales [START_REF] Tsai | A shapebased approach to the segmentation of medical imagery using level sets[END_REF] ou sur des atlas [START_REF] Zhuang | Robust registration between cardiac MRI images and atlas for segmentation propagation[END_REF]. L'avantage de la méthode des coupes de graphes est sa capacité à donner efficacement une solution optimale pour l'utilisation conjointe de différentes informations sur l'image. A ce jour, les travaux de segmentation par coupe de graphe en prenant en compte des formes a priori sont encore peu nombreux dans la littérature. Deux problèmes se posent en effet : la modélisation de la forme de l'objet à segmenter et son intégration dans l'algorithme de coupe de graphe. Dans cet article, nous proposons une méthode de segmentation basée sur les coupes de graphe binaires avec un a priori de forme, se déroulant en deux étapes : d'une part la construction d'une forme-modèle, et la segmentation par coupe de graphe utilisant ce modèle. Plus précisément, nous supposons que nous disposons d'un ensemble de formes représentatives de l'objet à segmenter, d'où peuvent être extraits un modèle moyen ainsi que des variabilités autour de ce modèle moyen par une analyse en composantes principales (ACP). Cette analyse permet de synthétiser un certain nombre de formes-modèles. Pour une image à segmenter, la forme-modèle la plus proche au sens d'un certain critère est sélectionnée. Un terme d'énergie prenant en compte ce modèle de forme est introduit dans la formulation de l'énergie du graphe. Nos contributions portent donc sur l'établissement du modèle ainsi que sur le terme énergétique l'intégrant au graphe. Dans la suite de cet article, nous présentons, dans la Section 2, le principe de la segmentation binaire par coupe de graphe ainsi qu'un bref état de l'art relatant les possibilités d'incorporation d'un a priori de forme dans ce cadre. Notre méthode de segmentation basée sur un modèle de forme obtenu par une ACP est exposée dans la Section 3. Nous proposons une validation de notre approche en l'appliquant à la segmentation d'images médicales dans la Section 4. Les conclusions et perspectives de ces travaux sont décrits dans la Section 5.

R p (ω) = -ln P r(I p |ω) (1)
où P r(I p |ω) est la probabilité que le pixel p appartienne à la classe ω, généralement obtenue par un histogramme sur les niveaux de gris de l'objet et du fond. Les n-links sont pondérés par un terme de régularisation, ou énergie contour, conçu pour assurer la cohérence spatiale dans un voisinage de pixels. Ce terme noté B p,q et est classiquement défini par :

B p,q ∝ exp - (I p -I q ) 2 2σ 2 . 1 dist(p, q) (2)
où I p et I q sont les niveaux de gris des pixels p et q, dist(p, q) la distance euclidienne entre p et q et σ une constante, généralement liée au bruit d'acquisition. Dans une région uniforme, I p et I q sont similaires et on a alors |I p -I q | < σ. Ceci implique une forte valeur pour B p,q , décourageant toute coupe du graphe dans ce voisinage. Réciproquement, dans une zone de contours, I p et I q sont différents et |I p -I q | > σ, B p,q a ainsi une valeur faible.

L'énergie totale d'une coupe C dans le graphe est alors définie par :

E(C) = p∈V R p (ω p ) + λ p,q∈N B p,q .δ(ω p = ω q ) (3)
où δ(ω p = ω q ) vaut 0 si p et q ont la même étiquette, 1 sinon.

La segmentation consiste finalement à rechercher la coupe d'énergie E(C) minimum, ce qui est équivalent à rechercher le flot maximum de la source au puit, un problème soluble en un temps polynomial selon le théorème de Ford et Fulkerson [START_REF] Ford | Flows in networks[END_REF]. Dans le cas où l'image est un graphe, l'algorithme le plus efficace est basé sur les chemins augmentants (augmenting paths). Il est décrit dans [START_REF] Boykov | An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision[END_REF].

Ajout d'un a priori de forme : état de l'art

Afin de guider le processus de segmentation, des contraintes ou des modèles concernant l'objet à segmenter peuvent être introduits, au travers d'un terme supplémentaire dans la formulation de l'énergie de l'Eq. 3. La manière d'incorporer ces informations a priori dépend des informations disponibles : soit les contraintes sont faibles et sont de simples hypothèses sur la forme générale de l'objet (convexe par exemple), soit les contraintes sont fortes et concernent une forme précise à retrouver dans l'image.

Contraintes faibles. Dans la littérature, les contraintes sur des formes grossières sont spécifiées au travers des n-links, modifiant l'étiquetage dans le voisinage des pixels selon l'hypothèse effectuée. Dans [START_REF] Das | Semiautomatic segmentation with compact shape prior[END_REF], les valeurs de l'énergie frontière B p,q sont modifiées en interdisant certaines positions relatives de p et q, favorisant ainsi les formes compactes. La même méthodologie est utilisée dans [START_REF] Veksler | Star shape prior for graph-cut image segmentation[END_REF] pour des formes plus générales que des formes convexes, définies en imposant que, si C est le centre de la forme et p un point dans la forme, tout point q situé sur la droite (C, p) après p soit également dans la forme. Notons que cette méthode présente un effet intéressant contrant le biais de rétrécissement généralement observé dans la segmentation par coupe de graphe, mais pose des problèmes importants de discrétisation. Imposer que le résultat de la segmentation soit convexe peut également être fait au travers d'un terme d'énergie supplémentaire de la forme 1 -cos(α) où α est l'angle entre (p, q) et (p, C) où C est le centre de l'objet, désigné par un clic de l'utilisateur [START_REF] Funka-Lea | Automatic heart isolation for CT coronary visualization using graph-cuts[END_REF]. On voit ainsi comment des angles importants sont pénalisés par de fortes valeurs de l'énergie, encourageant ainsi les coupes grossièrement convexes dans le graphe (Figure 2).

Contraintes fortes. Lorsqu'un modèle de l'objet à segmenter est disponible, il est en général imposé à la segmentation au travers des t-links, ce qui inclut, dans la formula- Lorsqu'il est préalable à la segmentation, le processus repose sur une intervention de l'utilisateur [START_REF] Freedman | Interactive graph cut based segmentation with shape priors[END_REF][START_REF] Song | Adaptive graph cuts with tissue priors for brain MRI segmentation[END_REF] 3 Segmentation avec a priori de forme basé sur l'ACP

Construction du modèle de forme

Synthèse des formes modèles. A partir d'un ensemble de formes représentatives de l'objet, une ACP permet d'ob-tenir les axes de variation des formes et la forme moyenne [START_REF] Tsai | A shapebased approach to the segmentation of medical imagery using level sets[END_REF]. Considérons un ensemble de N images binaires des formes (1 : objet ; 0 : fond), préalablement alignées par un recalage affine sur une image de référence. Pour chaque image binaire est calculée une carte des distances signées au contour de l'objet. Cette carte de distance est formalisée dans un vecteur Γ i pour tout i dans [1, N ]. La forme moyenne est définie par Φ = 1

N N i=1 Γ i . Chaque carte des distances est centrée selon Γ i = Γ i -Φ avec i ∈ [1, N ].
Pour capturer les variabilités des formes de l'objet, nous définissons la matrice S = Γ 1 . . . Γ N . La matrice de covariance définie par SS T peut alors être décomposée en N valeurs propres ρ i et vecteurs propres Φ i . Les vecteurs propres définissent les axes de variation (ou composantes principales). Retenir l'ensemble des vecteurs propres équivaut à conserver toute l'information initiale. En pratique, conserver un nombre N es ≤ N de vecteurs propres se révèle suffisant pour décrire une forme hors de l'ensemble initial. Celle-ci peut alors s'écrire comme une combinaison linéaire des vecteurs propres, ceux-ci étant pondérés par w i définissant l'intensité de la déformation selon l'axe Φ i . Afin de chercher la forme adéquate, la plupart des méthodes utilisent des algorithmes d'optimisation pour trouver les paramètres w i optimaux [START_REF] Tsai | A shapebased approach to the segmentation of medical imagery using level sets[END_REF]. Ici, nous proposons de construire un ensemble de formes créées selon différentes paramétrisations. A partir de chaque forme propre Φ i , les formes modèles sont définies par :

Φ k i = Φ + w k i Φ i (4)
avec w k i = ±k √ ρ i . L'ensemble de formes complet φ est alors créé à l'aide de :

φ = Φ k i , i ∈ [1 ; N es ] (5)
Afin de ne conserver que des déformations représentatives [START_REF] Tsai | A shapebased approach to the segmentation of medical imagery using level sets[END_REF], la valeur maximale de k est fixée à m = 3. k étant choisi entier, la taille de l'ensemble est alors de :

|φ| = 2mN es + 1 (6)
L'ensemble de formes est créé. L'étape suivante consiste à choisir le meilleur modèle de forme en considérant l'image à segmenter.

Sélection du meilleur modèle de forme. La première étape consiste à recaler de manière rigide les formes de φ sur l'image à segmenter. Nous définissons ensuite un score permettant de mesurer l'adéquation entre une image à segmenter et une forme de φ. Dans ce but, nous utilisons une fonction probabiliste d'appartenance à l'objet P r(I p |O), obtenue à l'aide d'un histogramme de l'objet sur les formes d'apprentissage de l'ACP. Considérons deux zones de l'image B O et B B obtenues par seuillage sur ces probabilités et définies par :

B O = {p ∈ V | P r(I p |O) ≥ t} (7) B B = {p ∈ V | P r(I p |O) < t} (8
Cette forme ϕ * est utilisée comme a priori dans la construction du graphe, décrite dans la section suivante.

Construction du graphe

La fonctionnelle d'énergie du graphe est définie en ajoutant à la formulation de l'Eq. 3 un terme E s dépendant de l'a priori de forme :

E(C) = λ p,q∈N B p,q .δ(ω p = ω q ) (12) + γ p∈V R p (ω p ) + (1 -γ) p∈V E s (ω p )
avec λ et γ des pondérations. B p,q est le terme contour pondérant les n-links défini par l'Eq. 2 et R p le terme région défini par l'Eq. 1. Ce dernier terme dépend d'un modèle de niveaux de gris de l'objet. Un histogramme des niveaux de gris de l'objet P r * modélisé par une gaussienne est calculé à partir de l'image à segmenter, en utilisant l'a priori de forme ϕ recalé sur l'image. On a ainsi :

R p (O) = -ln P r * (I p |O) (13) R p (B) = -ln(1 -P r * (I p |O)) (14)
Le terme E s incorpore l'a priori de forme construit précédemment. Une carte des distances aux contours de la forme ϕ est créée. A l'intérieur de ϕ, il est pratiquement certain que les pixels appartiennent à l'objet contrairement à ceux situés loin de lui. Afin de prendre en compte cette zone d'incertitude autour des contours, considérons ϕ α , la forme ϕ erodée par un disque de diamètre α (cf. Figure 3). α est un paramètre dépendant de l'application, définissant la zone de certitude de l'objet. On définit ainsi une probabilité d'appartenance à l'objet prenant en compte l'a priori P r S , maximale à l'intérieur de ϕ α et décroissante à l'extérieur selon une gaussienne d'écart type α :

P r S (p|O) = 1 si p ∈ ϕ α exp -dist(p,ϕ α ) 2 2α 2 si p / ∈ ϕ α (15)
Finalement, E s est défini par :

E s (O) = -ln P r S (p|O) (16) E s (B) = -ln(1 -P r S (p|O)) (17)

Segmentation

La méthode de segmentation proposée peut se résumer par les étapes suivantes : construction de formes modèles à partir d'une ACP, sélection du meilleur modèle de formes, définition du graphe prenant en compte l'a priori de forme. L'utilisation de l'algorithme de coupe minimale / flot maximal permet de trouver la partition optimale globale entre l'objet et le fond, en une seule passe [START_REF] Boykov | An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision[END_REF]. Dans la section suivante, des résultats de segmentation obtenus sur des images réelles sont présentés.

Résultats expérimentaux

Notre méthode a été appliquée à la segmentation d'images IRM cardiaques (Imagerie par Résonance Magnétique). L'objectif, dans ces images, est de segmenter le ventricule droit (VD) (cf. Figure 4). La difficulté de cette tâche réside dans la grande variabilité inter et intra-patient de la forme du VD, les similitudes en terme de niveaux de gris entre le VD et les tissus voisins, les irrégularités et le faible contraste du contour [START_REF] Petitjean | A review of segmentation methods in short axis cardiac MR images[END_REF]. 3). Afin de calculer la coupe minimale du graphe créé, nous utilisons l'implémentation 1 de l'algorithme de coupe minimale/flot maximal de Boykov et Kolmogorov [START_REF] Boykov | An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision[END_REF].

-3 √ ρ 1 Φ 1 -2 √ ρ 1 Φ 1 - √ ρ 1 Φ 1 Φ + √ ρ 1 Φ 1 +2 √ ρ 1 Φ 1 +3 √ ρ 1 Φ 1
Evaluation de la segmentation. La méthode de segmentation a été appliquée à 18 images provenant de patients différents et n'ayant pas été utilisées pour la création des modèles. Nous avons également appliqué à ces images l'algorithme de segmentation par coupe de graphe original, sans a priori de forme. Afin d'estimer de manière quantitative les résultats de segmentation obtenus, trois indices sont calculés, permettant de comparer deux contours A et B. Le coefficient de Dice D(A, B) mesure le recouvrement entre A et B et est défini par [START_REF] Lynch | Segmentation of the left ventricle of the heart in 3D+t MRI data using an optimised non-rigid temporal model[END_REF] :

D(A, B) = 2|A ∩ B| |A| + |B| (
1. Disponible en ligne à http://pub.ist.ac.at/~vnk/software.html.

où |A| désigne le nombre de points du contour A. Notons que ces deux derniers indices sont donnés en mm, en tenant compte de la résolution spatiale de l'image.

Les résultats moyens sont résumés dans le Tableau 1 et des résultats typiques de segmentation sont illustrés à la figure 6. Sur cette dernière, on observe les échecs de la méthode sans a priori. L'utilisation de l'a priori de forme compense les variations de niveaux de gris à l'intérieur de l'objet et le faible contraste entre le ventricule droit et les tissus voisins. Notre méthode donne des résultats encourageants et comparables à l'état de l'art pour cette application [START_REF] Petitjean | A review of segmentation methods in short axis cardiac MR images[END_REF].

Conclusion et perspectives

Dans cet article, nous avons présenté une méthode basée sur les coupes de graphe avec incorporation d'un a priori de forme via les t-links. Le modèle de formes est construit à partir d'une ACP. La méthode proposée ne requiert aucune itération puisque le modèle de forme est directement intégré au graphe. Elle a été appliquée pour la segmentation du ventricule droit en IRM cardiaque. C'est une tâche difficile à cause de la forme particulièrement variable de cet organe, des irrégularités et du faible contraste par endroit sur ses contours. Des résultats obtenus ont permis de montrer l'apport de l'a priori de forme dans la segmentation. La méthode proposée fournit des résultats encourageants pour cette application. Les perspectives de ces travaux incluent (i) une extension de la validation de cette méthode sur d'autres types d'images, (ii) l'amélioration du modèle de formes, (iii) l'automatisation de l'algorithme de segmentation.

FIGURE 2 -

 2 FIGURE 2 -Exemples de contrainte faible imposée à la segmentation par coupe de graphe avec calcul de l'angle α, permettant au contour (rouge) d'évoluer vers une forme convexe (vert) (d'après [12])

( 1 -

 1 ) où t est le seuil. Nous proposons le score suivant, calculé pour chaque forme modèle ϕ ∈ φ recalée sur l'image :S(I, ϕ) = p∈ϕ O ,p∈B O P r(I p |O)(9)+ p∈ϕ O ,p∈B B P r(I p |O))(10) où ϕ O = {p ∈ V | ϕ(p) = 1} représente la zone de l'objet. La forme modèle ϕ * correspondant au mieux à l'image est choisie comme étant celle de score maximal, sur l'ensemble des formes modèles possibles φ : ϕ * = arg max φ S(I, ϕ)

FIGURE 3 -

 3 FIGURE 3 -Construction de ϕ α

FIGURE 4 -

 4 FIGURE 4 -Images du coeur obtenue par IRM. En superposition, le VD délimité en rouge (tracé manuel d'un expert).

FIGURE 5 -

 5 FIGURE 5 -Premier axe de variation donné par l'ACP Les paramètres ont été obtenus empiriquement, donnant le jeu suivant : σ = 25, λ = 1000, γ = 0.5 et α = 1 4 d (cf. figure3). Afin de calculer la coupe minimale du graphe créé, nous utilisons l'implémentation 1 de l'algorithme de coupe minimale/flot maximal de Boykov et Kolmogorov[START_REF] Boykov | An experimental comparison of min-cut/maxflow algorithms for energy minimization in vision[END_REF].

  18) D(A, B) vaut 1 en cas de correspondance parfaite entre les deux contours, et 0 si le recouvrement est nul. En complément, deux indices mesurant la distance moyenne et maximum (distance de Hausdorff [15]) entre les contours sont calculés, selon : d m (A, B)

d

  H (A, B) = max a∈A {min b∈B d(a, b)}

FIGURE 6 -

 6 FIGURE 6 -Résultats de segmentation par coupe de graphe (a) sans et (b) avec a priori de forme (contour vert) et segmentation manuelle (contour rouge). Cette figure sera mieux appréciée si elle est visualisée en couleurs. La figure 7 montre les différentes étapes de la méthode : la sélection du meilleur modèle de forme, la probabilité d'appartenance à l'objet en fonction de P r * , les poids des tlinks par rapport à l'objet γR p (B)+(1-γ)Es(B) ainsi que les résultats de segmentation obtenus automatiquement et avec la vérité terrain. Cette illustration permet d'apprécier la contribution de l'a priori de forme à cette segmentation.

FIGURE 7 -

 7 FIGURE 7 -Résultats issus des différentes étapes de la methode, chaque ligne correspondant à un exemple. (a) Modèle de forme choisi, (b) probabilité d'appartenance à l'objet P r * (plus le rouge est intense, plus la probabilité est forte), (c) pondérations des t-links par rapport à l'objet γR p (B) + (1 -γ)Es(B) (moins le rouge est intense, plus l'appartenance à l'objet est certaine), (d) résultat (contour vert) et segmentation manuelle (contour rouge). Cette figure sera mieux appréciée si elle est visualisée en couleurs.

  . Le modèle est défini dans ce cas par une carte des distances ou un atlas. Notons que ces modèles sont limités pour représenter judicieusement les variabilités de forme. Dans la méthode que nous proposons, nous utilisons une contrainte forte via les t-links. A partir d'un ensemble de formes de l'objet à segmenter, nous effectuons une ACP et construisons différentes formes modèles permettant de représenter les variabilités de l'objet. Pour une image à segmenter, le meilleur modèle de forme est sélectionné, au sens d'un certain critère, parmi les différentes formes modèles synthétisées. Nous détaillons par la suite la méthode proposée au travers de ses deux étapes : la construction du modèle de forme et son intégration dans l'énergie du graphe.

TABLE 1 -

 1 Résultats moyens et écarts types sans et avec a

	priori de forme
		Avec a priori	Sans a priori
	Dice	0.86 ± 0.06	0.70 ± 0.23
	d

m (mm) 1.54 ± 1.14 3.49 ± 4.18 d H (mm) 5.53 ± 4.49 10.54 ± 10.71