Narjis Boukli

MPH Anders Boyd
email: anders.boyd@iplesp.upmc.fr

Mariane Collot

Jean-Luc Meynard

Pierre-Marie Girard

Laurence Morand-Joubert

Utility of HIV-1 DNA genotype in determining antiretroviral resistance in patients with low or undetectable HIV RNA viral loads

Utility of HIV-1 DNA genotype during

INTRODUCTION

With potent and well-tolerated ART, plasma viral loads (VL) of HIV can be easily suppressed to undetectable levels for the majority of HIV-infected patients. For example, data from the French Hospital Database on HIV have recently described 90.3% of treated patients from 2009-2011 exhibiting virological success. [START_REF] Delaugerre | Significant reduction in HIV virologic failure during a 15-year period in a setting with free healthcare access[END_REF] Consequently, the evolution towards severe HIVrelated disease has been drastically curtailed in infected individuals undergoing ART. [START_REF] Smith | Trends in underlying causes of death in people with HIV from 1999 to 2011 (D:A:D): a multicohort collaboration[END_REF] Despite these successes, some individuals need to modify their ART regimen due virological failure (VF) at low levels of VL. Meanwhile, other patients with undetectable HIV plasma RNA might change treatment combinations as a result of toxicity to certain agents or to simplify their ART regimen. [START_REF] Collins | Modifying Antiretroviral Therapy in Virologically Suppressed HIV-1-Infected Patients[END_REF] In order to guide the choice of appropriate treatment, resistance testing would then be recommended. Plasma RNA genotype is the current standard to assess resistance to antiretroviral drugs (ARV); 4 however, when VL is low or undetectable, viral sequences from plasma RNA could be difficult to obtain and results on drug resistance mutations (DRM) could be unavailable. For instance, only 60% of samples with VL between 20 and 200 copies/ mL were successfully sequenced in a nation-wide study of on-treatment virological failures conducted in France. [START_REF] Assoumou | Prevalence of HIV-1 drug resistance in treated patients with viral load >50 copies/mL: a 2014 French nationwide study[END_REF] Other ways to determine antiretroviral resistance are needed, specifically in the context of low or undetectable VL.

HIV-1 DNA from samples of PBMC can be sequenced and could provide some information to assess antiretroviral resistance. However, previous studies have shown that DNA genotype does not identify all DRMs compared to RNA genotype obtained after ART-initiation [START_REF] Delaugerre | Comparison of resistance mutation patterns in historical plasma HIV RNA genotypes with those in current proviral HIV DNA genotypes among extensively treated patients with suppressed replication[END_REF][START_REF] Wirden | Historical HIV-RNA resistance test results are more informative than proviral DNA genotyping in cases of suppressed or residual viraemia[END_REF] while others have found good concordance and that mutations on DNA were predictive of virological failure. [START_REF] Derache | HIV drug resistance mutations in proviral DNA from a community treatment program[END_REF][START_REF] Lambert-Niclot | Usefulness of an HIV DNA resistance genotypic test in patients who are candidates for a switch to the rilpivirine/emtricitabine/tenofovir disoproxil fumarate combination[END_REF] In this study, we aimed to investigate whether HIV-1 DNA genotype could provide reliable information to guide ARV choice. DRM and ARV resistance determined from DNA samples were compared to (i) past RNA genotype spanning several years or (ii) same-day RNA genotype in treated patients substituting their current ART regimen. In addition, the net gain or loss in DRM information was examined within classes of antiretroviral agents. We also assessed the determinants for which information on antiretroviral resistance is lost when genotyping DNA.

PATIENTS AND METHODS

Study participants

HIV-1-infected individuals seeking care at the Infectious Diseases Department of Saint-Antoine Hospital (Paris, France) were consecutively included between 2010 and 2014 provided that ≥1 DNA genotype and ≥1 RNA genotype were available during clinical followup. DNA and RNA genotypes were strictly performed at the request of the treating physician and no selection strategy based on sample availability was used. Two groups were then constructed: patients with undetectable VL or experiencing low-level plasma viraemia, defined as VL <500 copies/mL, whose RNA genotype had been performed prior to DNA genotype (group 1) and patients with VL <1000 copies/ml whose DNA and RNA genotypes were performed on the same day (group 2).

Ethics

Written informed consent was obtained from patients for the use of stored samples and personal data when conducting non-interventional research.

Assessing clinical and virological characteristics

The following patient characteristics were obtained from a computerized medical-record database (DIAMM v8.6r0, Villers-lès-Nancy, France): age, sex, mode of transmission, date of HIV-1 diagnosis, AIDS-defining illness, and concomitant and nadir CD4+ T cell count.

Complete history of ART regimens from treatment initiation until DNA genotyping was collected and verified by the treating physician. VLs were obtained from records at the Virology Department of Saint-Antoine Hospital.

HIV-1 RNA was quantified using Cobas AmpliPrep (Cobas Taqman HIV-1 assay, version 2.0; Roche Diagnostic, France). Total HIV-1 proviral DNA was retrospectively quantified using a real-time PCR assay (LTR HIV-1 DNA assay, Biocentrics, Bandol, France) from the same PBMC samples used to obtain DNA genotypes. [START_REF] Avettand-Fènoël | LTR realtime PCR for HIV-1 DNA quantitation in blood cells for early diagnosis in infants born to seropositive mothers treated in HAART area (ANRS CO 01)[END_REF]

Sanger sequencing and assessing DRM on plasma and PBMC samples

HIV-1 resistance testing was done on routine samples at the Virology Department of Saint-Antoine Hospital. Sanger sequencing was performed on both RNA and DNA for reverse transcriptase (RT) and protease sequences with nested-PCRs using primer sequences established by the Agence Nationale de Recherche sur le SIDA (ANRS) consensus method (described in www.hivfrenchresistance.org). RNA was used as template for reverse transcription before amplification. Sequence products were analyzed with the GENETIC ANALYZER 3500xl Dx (Applied Biosystem, Villebon Sur Yvette, France) and sequence alignment was performed using Seqscape software (Applied Biosystem). Sequencing on RT is expected to be successful in 60%, 78%, and 78% of patients with VLs of 51-200, 201-500, 501-1000 copies/mL 5 and 297 of 3430 (8.6%) requests for genotyping at our Virology Department from 2010-2014 were able to be sequenced. Mutations for NRTI, NNRTI and PI described in the ANRS 2014 v24 algorithm considered as resistance-associated mutations were recorded (Table S1). [START_REF]ANRS AC-11 Resistance Study Group. HIV-1 drug resistance genotypic interpretation's algorithm[END_REF] Resistance and susceptibility to each agent pertaining to the three classes of ARV were determined on RNA and DNA sequences.

Statistical analysis

All analysis was stratified on study group. Characteristics of each group were summarized using number and percent for categorical variables and medians and interquartile ranges (IQR) for continuous variables.

To assess the similarity of information between samples, we used an indicator of overall resistance defined herein as the "resistance sum." Resistance sum was defined as the number of drugs where resistance was present and was calculated within classes of ARV (NRTI, NNRTI, and PI) and sequence source (RNA or DNA genotype). Only resistance to ARV was considered in this score, regardless of whether multiple DRM to the same agent was present. Distribution of resistance sums were truncated to >2 agents in analysis due to the sparse distribution of sums above this level. DNA and RNA genotypes were compared using percent agreement and intraclass correlation (ICC). In group 1, data from the most recent RNA genotype was used in analysis for patients with more than one genotype (n=19).

We also conducted a sensitivity analysis among individuals with >1 prior RNA genotype whereby mutation presence was defined whenever it was observed on past RNA genotyping results.

To quantify the amount of information lost or gained with DNA genotype, we calculated a net difference in resistance sum-score comparing the number of agents where resistance was observed from DNA to RNA genotypes. Each agent was assigned the following: -1=resistance identified on RNA but not DNA, 0=resistance identified (or not identified) on both RNA and DNA, and 1=resistance identified on DNA but not RNA. The sum within individuals was calculated to create a score, which was provided in the overall group and within classes of ARV. Any values <0 and >0 were interpreted, respectively, as loss of and gain in information from DNA genotype compared to RNA. Score distributions were assessed using frequency histograms.

Finally, to understand risk-factors associated with net difference in resistance information, we modeled mean differences in resistance sum-score between levels of determinates (Δ) and its 95% CI using linear regression. In a separate analysis, we defined a resistance sum-score <0, representing loss in information, as an end-point. Odds ratios and 95% CIs for various demographic, virological, and treatment correlates were estimated using logistic regression.

For both end-points, multivariable analysis was performed in which all covariates with p<0.1 were placed in a full model and removed in backwards-stepwise fashion if the p-value was no longer above this threshold.

Statistical analysis was carried out using STATA statistical software (v12.1, College Station, TX) and significance was determined using a p-value <0.05.

RESULTS

Study population

A total of 100 patients met study inclusion criteria for one of the two cohorts: 74 with previous RNA genotypic results prior to DNA genotype (group 1) and 26 with concomitant RNA and DNA genotypes (group 2).

For group 1, 52 patients (71.6%) had undetectable VL (<50 copies/mL). The remaining 22 patients had low level viraemia with a median VL at 1.88 log 10 copies/mL. RNA genotypic results were obtained a median 5.8 years prior to DNA genotype in this group, whose distribution was fairly uniform from 0.4 to 10.4 years. Fifteen (20.3%) patients from this group had their RNA genotype performed before initiating ART. In addition, 19 patients had >1 RNA genotype prior DNA genotype with the number of retrospective RNA genotypes distributed as follows: two (n=10), three (n=7), four (n=1) and five (n=1). For group 2, median VL was at 2.51 log10 copies/mL for the 24 of 26 patients (92.3%) with detectable VL. Two (7.7%) patients from this group had RNA and DNA genotypes prior ART-initiation.

Patient characteristics for each group are given in Table 1. Patients in group 1 and 2, respectively, were predominately male (75.7% and 65.4%), harbored HIV-1 subtype B virus

Distribution of DRM in DNA and RNA genotypes

Wild-type genotype on both RNA and DNA was found in 23 patients (31.1%) from group 1 and 9 (34.6%) from group 2. Of those with RNA genotype prior to ART-initiation, 11/15 (73.3%) and 1/2 (50.0%) patients from groups 1 and 2, respectively, harbored strains without any antiretroviral resistance.

For patients with DRM, in group 1, the percentage of DRMs was consistently higher on the most recently performed RNA than on DNA genotype, which was observed for each ARVclass (Figure S1A). In contrast, this proportion was similar between sequence sources for all three ARV-classes in group 2 (Figure S1B).

Of note, mutations E138K, M184I and M230I on DNA, associated with APOBEC3G/F editing, were found in 4 patients for group 1 and 6 for group 2. Among them, one patient from each group harboring M184I on DNA had the M184V mutation detected on RNA, while another from group 2 had the mutation E138K on both DNA and RNA. Defective viruses with inframe-stop codons, which are considered unable to replicate, were detected in DNA sequences in 9 patients for group 1 and 5 for group 2.

Distribution of ARV resistance

Prevalence of resistance to individual ARV is summarized in Figure 1A for group 1 and Figure 1B for group 2. When examining ARV-classes, resistance to at least one of the following was observed in group 1 from RNA and DNA sequences, respectively: NRTI, 38 (51.4%) and 30 (40.5%), NNRTI, 24 (32.4%) and 16 (21.6%), PI, 23 (31.1%) and 15 (20.3%).

In the 19 patients with ≥ 2 RNA genotypic results prior to DNA genotype, a slightly lower proportion of drug resistance was observed using results from DNA compared to all previous RNA, respectively : NRTI, 8 (42.1%) and 15 (79.0%); NNRTI, 7 (36.8%) and 13 (68.4%); and PI, 8 (42.1%) and 10 (52.6%). In group 2, resistance to at least one of the following was observed from respectively RNA and DNA sequences: NRTI, 9 (34.6%) and 10 (38.5%); NNRTI, 9 (34.6%) and 10 (38.5%); PI, 5 (19.2%) and 6 (23.1%).

Similarity in resistance information between DNA and RNA genotyping

In Table 2, the concordance in number of agents for which resistance was observed, within each class of antiretroviral, was compared between DNA and RNA genotypes. Percent agreement was similar between groups 1 and 2 for NNRTI and PI classes, whereas group 1 had a roughly 20% lower percent agreement than group 1 for NRTIs. Nevertheless, ICC was much higher in group 2 compared to group 1 across all classes, with the smallest difference observed for PIs. There were no remarkable differences in percent agreement and ICC when patients harboring defective viruses on DNA genotype were excluded from analysis (data not shown).

Net change in resistance information between DNA and RNA genotypes

The majority of patients did not have a net difference in overall number of ARV where resistance was observed: 46 (62.2%) in group 1 and 24 (88.9%) in group 2.

In group 1, more patients appeared to have more loss than gain in resistance information with respect to NRTI (31.1% versus 10.6%, Figure 2A In patients from group 1, net difference in resistance sum-score was lower in those with longer duration of HIV infection (p=0.006), longer ART-duration (p=0.002), and number of ARV (p=0.01). Only longer ART-duration was significantly associated with net difference in resistance sum-score during multivariable analysis (Table 3). Loss of information was associated with older age (p=0.01), longer time since nadir CD4+ T cell count (p=0.004), longer known duration of HIV-infection (p=0.002), longer ART-duration (p<0.001), and number of ARV (p=0.003). Longer ART-duration was the only determinant significantly associated with loss of information in multivariable analysis (Table 3). These results did not substantially differ when examining only patients harboring drug-resistant strains (Table S2).

In group 2, any risk-factor analysis on the differences in resistance information between RNA and DNA genotypes was precluded by the lack of distribution in the resistance sum-score.

DISCUSSION

In a population of HIV-1 infected patients of which roughly two-thirds had viral strains with drug resistance to at least one ARV, there was some degree of information loss of antiretroviral resistance on current DNA compared to retrospective RNA genotypes when VL was low or undetectable. However, for some patients, DNA genotype does appear to give similar numbers of agents to which the strain is resistant compared to RNA. Higher concordance in DNA resistance information was also observed with RNA sequences from the same day versus several years in the past.

In the absence of RNA genotype due to low-levels of replication, resistance testing on DNA is sometimes the only means by which clinicians are able to select appropriate ART. [START_REF]Prise en charge médicale des personnes vivant avec le VIH. Optimisation d'un traitement antirétroviral en situation de succès virologique[END_REF] DNA genotype has been retrospectively compared to RNA genotype in other studies, demonstrating somewhat good concordance between past-RNA and DNA genotype in patients without viral failure [START_REF] Lambert-Niclot | Usefulness of an HIV DNA resistance genotypic test in patients who are candidates for a switch to the rilpivirine/emtricitabine/tenofovir disoproxil fumarate combination[END_REF] and adequate comparability with respect to number of DRM between past-RNA and non-defective DNA genotype. [START_REF] Allavena | Interest of proviral HIV-1 DNA genotypic resistance testing in virologically suppressed patients candidate for maintenance therapy[END_REF] We build on these works by demonstrating only marginally good comparability in the degree of antiretroviral resistance and whether information on resistance is lost with DNA genotyping, giving clearer guidance on the utility of this approach in identifying resistant drugs.

In our analysis, we considered a statistic by which the extent of information loss from DNA to RNA genotype could be assessed. This approach is somewhat analogous to comparing genotypic susceptibility scores between sample sources, as employed in other studies. [START_REF] Garcia | Predicting antiretroviral drug resistance from the latest or the cumulative genotype[END_REF][START_REF] Barth | Accumulation of drug resistance and loss of therapeutic options precede commonly used criteria for treatment failure in HIV-1 subtype-C-infected patients[END_REF][START_REF] Beerenwinkel | The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients[END_REF] A score of "zero" is the optimal goal and suggests that DNA genotype would provide the same type of information without considering specific ARV. A negative score would indicate loss of information on the DNA sample compared to RNA, with lower values representing a greater degree of loss of information.

Our univariable analysis identified a number of risk-factors associated with loss-ofinformation from DNA genotype, which included longer time since nadir CD4+ T cell count, known duration of HIV-infection, known duration of ART and number of cumulative ARV since ART initiation. As these determinants are closely intertwined with ART-duration, it is unsurprising that longer ART-duration was the only factor associated with loss-of-information in multivariable analysis. ART-duration could be a simple proxy for extensive HIV-infection and potentially more complicated therapeutic history, lending to exposure to many ARV and selective pressures to develop DRM. [START_REF] Clutter | HIV-1 drug resistance and resistance testing[END_REF] Agents will be switched as treatment progresses and the selective pressures of acquiring certain mutations might no longer present. In turn, previous DRM could be naturally purged from the reservoir during longer periods of treatment. [START_REF] Kabamba-Mukadi | HIV-1 proviral resistance mutations: usefulness in clinical practice[END_REF] For instance, M184I/V on DNA is known to disappear in the absence of NRTIs pressure, which could explain the effectiveness of lamivudine-containing dual therapies in patients with a history of M184I/V mutation. [START_REF] Charpentier | HIV-1 DNA ultra-deep sequencing analysis at initiation of the dual therapy dolutegravir + lamivudine in the maintenance DOLULAM pilot study[END_REF] Nevertheless, there are other explanations for the information loss on DNA genotype observed in our study. Our study population with longer HIV-infection and ART-exposure could have had a higher frequency of virological failure, which has also been associated with poor concordance between RNA and DNA genotypes in other studies. [START_REF] Delaugerre | Comparison of resistance mutation patterns in historical plasma HIV RNA genotypes with those in current proviral HIV DNA genotypes among extensively treated patients with suppressed replication[END_REF][START_REF] Wirden | Historical HIV-RNA resistance test results are more informative than proviral DNA genotyping in cases of suppressed or residual viraemia[END_REF] Higher personyears of viral replication, representing in part less effective ART combinations when levels are higher, did not correlate with loss-of-information. However, it is difficult to differentiate virological failure than poor treatment adherence as an explanation for replicating HIV. Furthermore, the detection threshold from Sanger sequencing only allows detection of mutant variants as majority quasi species and could miss DRMs representing less than 15-25% of the viral population. [START_REF] Palmer | Multiple, linked human immunodeficiency virus type 1 drug resistance mutations in treatmentexperienced patients are missed by standard genotype analysis[END_REF] It could be that DRM found on previous RNA samples could have been archived as minority variants, creating further loss of information. Ultra-deep sequencing detecting low-frequency variants harboring DRMs has been found to aid prediction of antiviral resistance, but was not performed in our study. [START_REF] Mohamed | Comparison of ultra-deep versus Sanger sequencing detection of minority mutations on the HIV-1 drug resistance interpretations after virological failure[END_REF][START_REF] Vandenhende | Prevalence and evolution of low frequency HIV drug resistance mutations detected by ultra deep sequencing in patients experiencing first line antiretroviral therapy failure[END_REF] Mutation profiles in archived DNA are also known to evolve despite long periods of sustained virological control [START_REF] Gantner | Drug resistance and tropism as markers of the dynamics of HIV-1 DNA quasispecies in blood cells of heavily pretreated patients who achieved sustained virological suppression[END_REF][START_REF] Michelini | HIV-1 DNA dynamics and variations in HIV-1 DNA protease and reverse transcriptase sequences in multidrug-resistant patients during successful raltegravir-based therapy[END_REF] and hence could affect interpretation and comparability of sequences determined on DNA versus RNA genotypes. As the loss of resistance in DNA was associated with longer duration of HIV-infection and longer history of being on ART, our results indirectly indicate that increased diversity of proviral DNA could be the reason for loss of resistance information. Clearance of archived DRM in proviral DNA after long periods of virological control, lacking therapeutic selective pressures, have been recently reported with reverse transcriptase, which could imply residual replication of more fit viruses within some reservoirs. 25 For some patients, resistance to ARV was able to be detected on DNA but not on RNA genotype. APOBEC3 cytidine deaminases are known to induce G to A hypermutations in GG or GA dinucleotides on DNA, which could explain some of these cases. [START_REF] Kieffer | G-->A hypermutation in protease and reverse transcriptase regions of human immunodeficiency virus type 1 residing in resting CD4+ T cells in vivo[END_REF] Seven patients harbored APOBEC mutations E138K, M184I and M230I on HIV DNA without DRM on RNA, consistent with other studies. [START_REF] Derache | HIV drug resistance mutations in proviral DNA from a community treatment program[END_REF][START_REF] Charpentier | HIV-1 DNA ultra-deep sequencing analysis at initiation of the dual therapy dolutegravir + lamivudine in the maintenance DOLULAM pilot study[END_REF] The potential significance of these mutations is still unknown; nevertheless, others have shown that recombination could occur between hypermutated sequences and circulating viral populations, eventually leading to the emergence of resistant viral strains. [START_REF] Kim | Human APOBEC3G-mediated editing can promote HIV-1 sequence diversification and accelerate adaptation to selective pressure[END_REF] Others have also found that concordance is affected in DRM between DNA and RNA genotypes in case of defective proviruses. [START_REF] Allavena | Interest of proviral HIV-1 DNA genotypic resistance testing in virologically suppressed patients candidate for maintenance therapy[END_REF] We were unable to corroborate these results, yet only a small proportion of patients in our study harbored stop-codon mutations on DNA sequences and the study population was different.

For other cases in group 1, DRM might have developed due to a switch to another treatment after the most recent RNA genotype and thus the resistance observed on DNA might have captured selective pressures incongruent with those from past RNA genotypes. 28 Importantly, the intraclass correlation comparing resistance information from DNA to RNA genotype was much lower for NRTIs than NNRTIs or PIs, as demonstrated in another study evaluating detected resistance. 28 These results would imply that any susceptibility of losing DRM might occur more frequently with NRTI agents. NRTI is historically an older class of ARV and were likely given during early infection among those with longer ART-duration. The risk of observing archived NRTI resistance mutations that disappear over time could then be considered higher for these individuals.

Certain limitations of our study need to be addressed. First, the large heterogeneity of the studied population does make it difficult to generalize to other populations, such as those with less exposure to ART or HIV-infection duration, or to more specific treatment regimens. This heterogeneity could have also increased variation, which likely affected some of our analyses. Second, the low sample size could have limited the power to detect certain riskfactors associated with differences in genotype information between RNA and DNA. Third, genotypic results were obtained retrospectively and hence selection of patients was based on sample availability. Fourth, unlike many genotypic resistance scores, we considered intermediate resistance test results as resistance, which could be considered extreme for certain ARV. Finally, resistance testing of INSTI was not available at the time of study and was not included in our analysis. Other studies would be needed to confirm similar observations with this treatment class.

In conclusion, genotyping DNA has some degree of concordance compared to previous RNA genotypes, specifically when determining the extent of drug resistance, in a real-life setting of patients with low or undetectable VL during ART. It is already recommended that DNA genotype could be carried out for patients who have no RNA genotype or for those patients without any knowledge of their ARV history. 4,[START_REF] Barth | Accumulation of drug resistance and loss of therapeutic options precede commonly used criteria for treatment failure in HIV-1 subtype-C-infected patients[END_REF] Our results would contend that if neither ARThistory nor RNA genotype is available, DNA genotype could provide some useful information on drug resistance to the major classes of antiretrovirals. Nevertheless, patients having extensive years of treatment with multiple lines of ARV regimens might have more loss-ofinformation on DNA genotype, making it more difficult to rule out ARV resistance.

  (75.3 and 69.2%), and had a long duration of known HIV-1 infection (median 17.4 and 14.2 years). Patients underwent ART for a median of 15.3 years (group 1) and 13.5 years (group 2), while receiving a median 9 (IQR=6-12) and 6 (IQR=4-11) ARV respectively, from ART initiation until DNA genotype. At the time of DNA genotype, ART was mostly prescribed in 3drug-regimen (n=53, 52.5%): 2 NRTIs+1 NNRTI (n=17), 2 NRTIs+1 PI (n=20), 2 NRTIs+1 INSTI (n=6), or other combinations (n=10; 7 of which contained an INSTI). Non-3-drugregimen consisted of monotherapy with ritonavir-boosted darunavir (n=2), dual therapy (n=9), or >3 agents from at least three different classes (n=27; 21 of which contained an INSTI).

  left panel), NNRTI (17.6% versus 10.6%, Figure 2B left panel) and PI classes (18.9% versus 2.7%, Figure 2C left panel). In group 2, few patients exhibited loss of information in NRTI (3.7%, Figure 2A right panel) and NNRTI (7.4%, Figure 2B right panel) classes and no net change in resistance information was observed for PIs (Figure 2C right panel).

Figure 1 .

 1 Figure 1. Comparing percentage of resistance to individual antiretroviral agents between HIV-1 RNA versus HIV-1 DNA genotypes

Figure 2 .

 2 Figure 2. Histogram of net change in resistance information between HIV-1 RNA and DNA genotypes.

Figure 1

 1 Figure 1. A

Table 1 . Description of the study population

 1 resistance test in proviral DNA can identify resistance mutations never detected in historical genotypic test in patients with low level or undetectable HIV-RNA. J Clin Virol IQR) is given when indicated (*). Otherwise, all statistics are reported in n (%).

	2016; 82:94-100.
	TABLES
		Cohort
		DNA genotype	DNA and RNA
		and past RNA	genotype
		genotype	together
		(group 1)	(group 2)
		(n=74)	(n=26)
	Male/Female (% male)	56/18 (75.7)	17/9 (65.4)
	Age (years)* ‡	48 (42-53)	47 (41-53)
	Mode of transmission
	MSM	40 (54.0)	12 (48.0)
	Heterosexual	20 (27.0)	11 (44.0)
	IDU	5 (6.8)	-
	Other	9 (12.2)	2 (8.0)
	AIDS-defining disease	27 (36.5)	8 (33.3)
	CD4 cell count (/mm 3 )* ‡	517 (363-772)	384 (190-558)
	Nadir CD4 cell count (/mm 3 )*	169 (48-246)	167 (79-226)
	HIV-1 subtype
	B	55 (75.3)	18 (69.2)
	Non-B	18 (24.3)	8 (30.8)
	Years since ART-initiation* ‡	15.3 (6.5-18.6)	13.5 (6.9-17.0)
	Number of antiretroviral agents since ART-initiation* ‡	9 (6-12)	6 (4-11)
	Number of agents within class since ART-initiation* ‡
	NRTI	5 (3-6)	2 (0-5)
	NNRTI	1 (1-2)	1 (0-1)
	PI	3 (2-4)	2 (2-3)
	INSTI	0 (0-1)	0 (0-1)
	Number of current antiretroviral agents* ‡	3 (3-4)	3 (3-3)
	HIV-1 RNA viral load >50 copies/mL ‡	21 (28.4)	24 (92.3)
	HIV-1 RNA viral load (log10 copies/mL)* † ‡	1.88 (1.80-2.16) 2.51 (2.18-4.23)
	Cumulative years <50 copies/mL*	7.3 (4.1-10.6)	9.6 (5.3-12.3)
	Total known number of virological failures* ¶	2 (0-5)	2 (0-4)
	HIV-1 proviral DNA >40 copies/10 7 cells * ‡	51/63 (81.0)	20/4 (83.3)
	HIV-1 DNA viral load (log10 copies/10 7 cells)* ‡	2.90 (2.18-3.11) 2.99 (2.37-3.69)
	Years between HIV-1 RNA and DNA ‡ genotypes*	5.8 (2.6-7.4)	-
	Median (

†

Only among patients with detectable HIV-1 RNA.

ACKNOWLEDGEMENTS

None.

FUNDING

This study was carried out as part of our routine work. A post-doctoral fellowship from SIDACTION was awarded to A.B. for part of the submitted work providing paid salary under a temporary contract with the Institut National de la Sante et de la Recherche Medicale (Inserm).

TRANSPARENCY DECLARATIONS

None to declare. ‡ Data obtained at the time of DNA genotype, for patients of group 1(DNA genotype with past RNA genotype). ¶ Virological failure was defined as two consecutive visits with a pVL >50 copies/mL or one pVL>200 copies/mL. Analysis conducted on patients from group 1 only. Units represented in: *per year; # per 100/mm 3 ; † per log10 copies/mL; ¶ Virological failure was defined as two consecutive visits with a pVL >50 copies/mL or one pVL>200 copies/mL. **End-point is absolute difference in number of agents where drug resistance was observed on DNA compared to past RNA genotyping results.

In the multivariable model, the following variables were excluded as their p-value was no longer below the pre-specified threshold: time since nadir CD4+ cell count (p=0.883), duration of HIV infection (p=0.672), number of antiretroviral agents (p=0.609), and cumulative years <50 copies/mL (p=0.453).

##

End-point is whether HIV-1 DNA provides less resistance information than past HIV-1 RNA genotyping results. In the multivariable model, known HIV-infection duration was not included due to collinearity with ART duration. The following variables were excluded as their p-value was no longer below the pre-specified threshold: time since nadir CD4+ cell count (p=0.791), age (p=0.514), number of antiretroviral agents (p=0.431), and duration of HIV-infection (p=0.149). Virological failure versus tx switch -1.3 (-3.6, 0.9) 0.2 1.30 (0.36-4.68) 0.7 Analysis conducted on patients from group 1 with identified drug resistance mutations from past HIV-1 RNA genotyping. Units represented in: *per year; # per 100/mm 3 ; † per log10 copies/mL. ¶ Virological failure was defined as two consecutive visits with a pVL >50 copies/mL or one pVL >200 copies/mL. The same multivariable models from Table 3 were applied a priori. **End-point is absolute difference in number of agents where drug resistance mutations were observed on DNA compared to past RNA genotyping results. ## End-point is whether DNA provides as much or more resistance information than past RNA genotyping results.


Smaine Mazouzi
email: mazouzi@leri.univ-reims.fr

Zahia Guessoum
email: zahia.guessoum@lip6.fr

Fabien Michel
email: fmichel@leri.univ-reims.fr

Mohamed Batouche
email: batouche@wissal.dz

An Agent-Based Approach for Range Image Segmentation

Keywords: Image segmentation, Multi-agent systems, Range image, Artificial potential field

In this paper an agent-based segmentation approach is presented and evaluated. The approach consists in using a high number of autonomous agents for the segmentation of a range image in its different planar regions. The moving agents perform cooperative and competitive actions on the image pixels allowing a robust extraction of regions and an accurate edge detection. An artificial potential field, created around pixels of interest, allows the agents to be gathered around edges and noise regions. The results obtained with real images are compared to those of some typical methods for range image segmentation. The comparison results show the potential of the proposed approach for scene understanding in range images regarding both segmentation efficiency, and detection accuracy.

Introduction

The segmentation of an image is often necessary to provide a compact and convenient description of its content, suitable for high level image analysis and understanding. It consists in assigning image pixels to homogenous and disjoint subsets, which form an image partition. The pixels which belong to the same region share a common feature called the region homogeneity criterion. In range images, segmentation methods can be divided into two distinct categories: edgebased segmentation methods and region-based segmentation methods. In the first category, pixels which correspond to discontinuities in depth or in surface normals are selected and chained in order to delimit the regions in the image [START_REF] Inokuchi | A three dimensional edge-region operator for range pictures[END_REF][START_REF] Fan | Segmented description of 3-D surfaces[END_REF][START_REF] Jiang | Edge detection in range images based on Scan Line approximation[END_REF]. Edge-based methods are well known for their low computational cost, however they are very sensitive to noise.

Region-based methods use geometrical surface descriptors to group pixels with the same proprieties in disjoint regions [START_REF] Yang | Determination of the identity, position and orientation of the topmost object in a pile[END_REF][START_REF] Kang | The complex EGI: A new representation for 3-D pose determination[END_REF][START_REF] Li | Gradient-based polyhedral segmentation for range images[END_REF][START_REF] Ding | Range image segmentation based on randomized hough transform[END_REF][START_REF] Hadiashar | Range image segmentation using surface selection criterion[END_REF]. Compared to edge-based methods, region-based methods are more stable and less sensitive to noise. However, their efficiency depends strongly on the selection of the region seeds. Most of the time, the segmentation results in an over-partition of the image. So, it is necessary to perform an iterative fusion of homogenous regions. Such an approach does not facilitate the distribution of the used algorithms, and leads to high computational costs [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF].

Furthermore, most of the proposed methods model surface proprieties by computing image derivatives of different orders. Such techniques result in a highly noise-sensitive detection. It is then necessary to perform some preprocessing tasks, which consist mostly in image smoothing and noise filtering. However, in the case of highly noisy images such as range images [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF], a strong noise smoothing can erase the roof edges (at discontinuities of surface normals), and the smooth edges (at discontinuities of curvature) whose detection remains a challenge. Moreover, if the noise is under-smoothed the distortions, which remain in the image, result in inaccurate or erroneous segmentation results. This difficulty, which is an open issue in image segmentation [START_REF] Li | Roof-edge preserving image smoothing based on MRFs[END_REF], results from the restriction of computation and decision to the local neighborhood of the processed pixel. In range images, several recent segmentation methods fail because they do not correctly address and resolve this problem [START_REF] Jiang | Some further results of experimental comparison of range image segmentation algorithms[END_REF].

To deal with this problem, some authors have proposed agent-based solutions for 2-D image segmentation. Agent-based solutions inherit the advantages of the agent-oriented systems for collective problem solving. In such systems a single agent has a limited perception and limited capabilities, and it is not designed to solve an entire problem. Agents cooperate thus in order to provide a collective solution. Contrary to conventional systems, solutions in agent-based systems emerge from collective action of interactive agents [START_REF] Krishnamurthy | Distributed agent paradigm for soft and hard computation[END_REF].

In this paper, a new agent-based solution for range image segmentation is presented and discussed. It consists in the use of reactive agents, which move over the image, and act on the visited pixels. While moving over the image, an agent adapts to the planar region on which it moves, and memorizes its proprieties. When an agent encounters a pixel which does not belong to its current region, an agent alters this pixel in order to align it to its current region. At the boundaries between the regions the agents will be in competition to align the pixels of the boundaries to their respective regions. The resulting alternative alignment of the boundary pixels preserves the region boundaries against erasing. Noise regions that are characterized by small sizes or by aberrant depths (outliers) prevent the agents from adapting. Thus, these regions continuously contract by aligning their pixels to the planar regions which surround them.

Our aim is to overcome the difficulty related to the local perception around the processed pixel. A pixel is therefore processed according to both its neighborhood, and the agents that visit this pixel. An agent acts on pixels with more certainty, acquired from its move on large areas on the image regions. The combination of the global information memorized within the agent, and the local information, provides more reliable decisions. To optimize the agent movements, an artificial potential field inspired from the electrostatic potential field is used. It allows to rationalize the movements of the agents by directing them to be gathered around the regions of interest (edges and noise regions) and to concentrate their actions around these regions. The utilization of a large number of reactive and weakly coupled agents provides a massively multi-agent system, allowing a parallel and distributed image segmentation. Extensive experimentations have been performed using real images from the ABW database [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF]. The obtained results show the high potential of the proposed approach for an efficient and accurate segmentation of range images.

The remainder of the paper is organized as follows: In Section 2, we review the agent-based approaches for image segmentation. Section 3 introduces the surface proprieties modeling. Section 4 is devoted to the proposed approach. It describes the behavior of the agents and shows the underlying collective mechanism to deal with the edge detection and the noise removal. The experimental results are introduced in Section 5, in which we discuss the selection of the used parameters, and we analyse and comment the obtained results. Finally, a conclusion summarizes our contribution.

Related Work

Several agent-based systems have been proposed for image analysis and object recognition. They propose interesting solutions to deal with several problems, such as multiple domain knowledge handling, control automation over the image interpretation tasks, collective segmentation, and distributed and parallel image processing. In this review we consider only works which have addressed a solution in image segmentation.

Liu et al. [START_REF] Liu | Adaptive image segmentation with distributed behavior-based agents[END_REF] introduce a reactive agent-based system for brain MRI segmentation. They underline that the employed agents are more robust and more efficient than the classical region-based algorithms. Four types of agents are used to label the pixels of the image according to their membership grade to the different regions. When finding pixels of a specific homogenous region an agent creates offspring agents into its neighborhood. In this system, the agents neither interact directly between them nor act on the image. Their actions depend only on their local perception. Nevertheless, each agent is created so that it becomes more likely to meet more homogenous pixels. For the same type of images, Richard et al. [START_REF] Richard | Automated segmentation of human brain MR images using a multi-agent approach[END_REF] propose a hierarchical architecture of situated and cooperative agents for brain MRI segmentation. Three types of agents have been used: global control agent, local control agent, and tissue dedicated agent. The role of the global control agent is to partition the volume of data into adjacent territories and to assign one local control agent to each territory. The role of a local control agent is to create the tissue dedicated agents which perform a local region growing. The statistical parameters of the image data distribution, needed to perform region growing are updated according to the interaction between neighboring agents. Using several types of agents has allowed to deal with both the control over the high-level segmentation tasks and the low-level image processing tasks.

The two previous systems are well optimized to brain MRI segmentation. They can provide interesting results because region characteristics are regular in the different brain anatomic parts. In addition, most of the edges in such images are jump edges (at discontinuities of image data) which are easy to detect, compared to roof and smooth edges.

Rodin et al. [START_REF] Rodin | An immune oriented multi-agent system for biological image processing[END_REF] using the oRis langauge [START_REF] Harrouet | oRis: un environnement de simulation interactive multi-agents[END_REF], have presented a reactive agentbased system for edge detection in biological images. According to some prior on image content, the system provides an edge detection which is better than that provided by traditional detectors. Two groups of agents, called darkening agents and lightning agents follow respectively the dark regions and the light regions. Their actions aim at reinforcing regions by stressing their contrast, allowing a reliable detection of these regions. In this system, agents are fully independent from each other, and never interact. The system can be considered as a parallel segmentation algorithm which was well optimized for the detection of roof edges in some biological images. However, agents were not designed to detect discontinuities of image data. So, the system may fail to detect jump edges. Furthermore, the number and the topology of the expected regions must be known, and hard coded within the agents.

Based on the cognitive architecture Soar [START_REF] Newell | Unified theories of cognition[END_REF], Bovenkamp et al. [START_REF] Bovenkamp | Multi-agent segmentation of IVUS images[END_REF] have developed a multi-agent system for IntraVascular UltraSound (IVUS) image segmentation. They aim to elaborate a high knowledge-based control over the algorithms of low-level image processing. In this system, an agent is assigned to every expected object in the image. Agents cooperate and dynamically adapt the segmentation algorithms, according to contextual knowledge, local information and their personal believes. In this work, the problem of the control over segmentation algorithms seems to be well resolved. However, no agent or even behavior has been proposed to deal with the problem of uncertain and noisy data.

The proposed agent-based systems for image segmentation are specific to image contents. Following a supervised approach, these systems aim at segmenting images in known and previously expected regions. The system proposed in this paper claims to be general and unsupervised. It aims to segment an image into its several regions by using some invariant surface proprieties. The adaptive and competitive behavior of the agents allow overcoming the constraint related to the restriction of the treatments to the local neighborhood of pixels. We show in this work that despite the simplicity of the model used to represent surfaces, the obtained results are better than those provided by conventional approaches. We believe that interactions between agents provide an alternative way for image segmentation to that of methods based on complicated and costly models [START_REF] Li | Markov random field modeling in image analysis[END_REF].

Surface Modeling

A range image is a discretized two-dimensional array where at each pixel (x, y) is recorded the distance Z(x, y) between the range finder and the corresponding point of the scene. Regions in such an image represent the visible patches of object surfaces. To attenuate the white and the impulsive noise contained in the image, a Gaussian filter and a median filter are applied to the depth raw data. A new image Z * (x, y), called plane image is then derived from the range image.

Each pixel (x, y) of the new image records the tangent plane to the surface at (x, y). The best tangent plane at (x, y) is obtained by the multiple regression method using the set of neighboring pixels χ(x, y). The neighborhood χ(x, y) is made up of pixels (x , y ) situated within a 3 × 3 window centred at (x, y), and whose depths Z(x , y ) are close, according to a given threshold (T r D ).

The plane equation in a 3 -D coordinate system may be expressed as follows:

ax + by + cz = d (1)
where (a, b, c) T is the unit normal vector to the plane (a 2 + b 2 + c 2 = 1; c < 0) and d is the orthogonal distance between the plane and the coordinate origin. First, Parameters α, β and γ of the surface z = αx + βy + γ at (x 0 , y 0 ) are obtained by the minimization of the function Φ, defined as follows:

Φ(α, β, γ) = (xi,yi)∈χ(x0,y0) [αx i + βy i + γ -Z(x i , y i )] 2 (2)
with

χ(x 0 , y 0 ) = {(x 0 + i, y 0 + j); (i, j) ∈ {-1, 0, +1} and |Z(x 0 + i, y 0 + j) - Z(x 0 , y 0 )| < T r D }
Parameters a, b, c and d are thus obtained as follows:

(a, b, c, d) T = 1

α 2 + β 2 + 1 (α, β, -1, γ) T (3)
The tasks performed on the plane image are based on the comparison of planes. Indeed, we consider that two planes ax+ by + cz = d and a x+ b y + c z = d are equal if they have, according to given thresholds, the same orientation and the same distance to the coordinate origin. Let θ be the angle between the two normal vectors, and D the distance between the two planes: sin(θ) = (a, b, c) T ⊗ (a , b , c ) T and D = |dd |. So, the two planes are considered equal if sin(θ) ≤ T r θ and D ≤ T r D , where T r θ and T r D are respectively the angle and the distance thresholds. Plane comparison is first used to test if a given pixel belongs to a planar region, given its plane equation. It is also used to test if the pixel is, or is not, a pixel of interest (edge or noise pixel). In this case, the pixel in question is considered as a pixel of interest if at least one of its neighbors has a different plane equation, according the previous thresholds.

Multi-agent Range Image Segmentation

The plane image is considered as the environment in which agents are initialized at random positions. An agent checks if it is situated within a planar region, and adapts this region if planar, by memorizing its plane equation. Next, the agent performs actions which depend on both its state and the state of the pixel on which it is located. At each time t, an agent is characterized by its position (x t , y t ) over the image, and by its ability A t to act on the encountered pixels. At the beginning of the process, all the agents are unable to alter the image pixels. After having been adapted to a planar region, an agent becomes able to modify the first pixel not belonging to this region (A t =true). When an agent alters a pixel, it loses its alteration ability (A t =false) and starts again searching for a new planar region. An agent having modified a pixel records in an appropriate array I at the position (x t , y t ) the last state of the visited pixel: I(x t , y t ) ∈ {smoothed, aligned, unchanged}. We show next, that this simple behavior of the agents allow both the detection of the image edges, and the removal of the noise regions.

Agent Behavior

An agent adapts to the region of the image on which it is moving by computing and storing the proprieties of this region, and by adopting the suited behavior to the local image data. Fig. 1 depicts the behavior of an agent according to its state and the state of the pixel on which it is located. Searching for a Planar Region. After its creation, an agent randomly moves within the image and searches for a planar region around its current position. The seed of the searched region is formed of the last L pixels visited by the agent. L is called the adaptation path-length. It represents the confidence degree that the agent is situated within a planar region. The agent considers that it is within a planar region if the pixels of the seed form a planar surface. The agent memorizes the new region and considers it as its current planar region. It agent becomes then able to alter the first encountered pixel which does not belong to its planar region (A t =true).

Moving on a Planar Region. While moving inside a planar region, an agent smoothes the pixel on which it is located by updating the equations of both the memorized plane and the plane at the pixel position. This is done by replacing the two equations by their weighted average. Let (a , b , c , d ) and(a, b, c, d) be the parameters respectively of the memorized plane and the plane at the current pixel. Resulting parameters of the weighted average plane, before normalization, are obtained as follows:

(a , b , c , d ) = 1 1 + l (a + la , b + lb , c + lc , d + ld ) ( 4
)
where l is the length of the path crossed by the agent on the planar region.

Pixel Alignment. Pixels of interest are edge pixels or pixels within noise regions. When an agent meets a pixel of interest (i.e. not belonging to its current planar region) it alters it in order to partially align it to the planar region on which it is moving. Parameters (a , b , c , d

) = 1 1 + ξ (a + ξa , b + ξb , c + ξc , d + ξd ) ( 5
)
where ξ is the alteration strength.

The agent loses then its alteration ability (A t =false) and starts again to search a new planar region. Further to the alteration of a pixel, the agent can pass into another region, or remain in the current region. If the altered pixel is an edge pixel, the agent likely pass in an other planar region. However, if the altered pixel is on the boundary of a noise region, the agent crosses the noise region and most likely end up in the previous planar region, except if the noise region is situated between two planar regions. The alteration strength ξ is a critical parameter which affects the quality of the results and the time of computation. Indeed, high values of ξ lead to a fast detection of regions. However the resulting region boundaries are distorted and badly localized (Fig. 2c). Low values of ξ result in a slow detection, but region boundaries in this case are well detected and correctly localized (Fig. 2d). In order to speed up the segmentation process, and avoid edge distortion, an agent chooses the alteration strength among ξ min and ξ max according to the information recorded by other agents in the array I. So, an agent assumes that the current planar region is adjacent to a noise region and thus uses ξ max as alteration strength, if the number of "unchanged" pixels (situated in a noisy region) around the agent is greater than a given threshold (fixed to 3 in our experimentations). Indeed, pixels labeled "unchanged" in the adjacent region mean that this latter is a noise region for which agents have not adapted and consequently have not smoothed its pixels. Otherwise the agent assumes that the current planar region is adjacent to another one, where other agents have labeled the pixels as "smoothed" or "aligned". In this case the agent uses the alteration strength ξ min .

Agent Coordination by Artificial Potential Field

To endow the agents with a self-organization mechanism, an artificial electrostatic like potential field is used. It is created and updated around the aligned pixels. It allows agents to be gathered around pixels of region boundaries, and concentrate their actions at these pixels. Contrary to other works, where the potential field is created at known positions of objects (goals and obstacles) [START_REF] Ferber | Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence[END_REF][START_REF] Tsuji | Bio-mimetic trajectory generation of robots via artificial potential field with time base generator[END_REF][START_REF] Simonin | Construction of numerical potential fields with reactive agents[END_REF], the potential field in our case results from the interaction of agents with the objects in the environment (pixels). The intensity Ψ (x, y) of the potential field at position (x, y) created by a set of P pixels beforehand aligned {(x i , y i ),i = 1..P ∧ I(x i , y i )=aligned} is given by:

Ψ (x, y) = P i=1 k (x -x i ) 2 + (y -y i ) 2 , k ∈ R + ( 6
)
where k is the constant of the electrostatic force, set to 1. An agent which is able to alter pixels (A t =true) and situated at position (x t , y t ) undergoes an attractive force -→ F . This force is expressed by the gradient vector of the potential field:

-→ F = ⎧ ⎨ ⎩ - -→ ∇Ψ (x t , y t ) if A t =true -→ 0 o t h e r w i s e
So, the agent movements, which are stochastic in nature, are weighted by the attractive force applied by the potential field. Agents are influenced to head for the pixels of interest, while keeping random moves. The random component of the agent moves allows the exploration of all regions of the image. A Relaxation mechanism of potential field is also introduced. It allows the agents gathered around pixels of interest to be released and thus to explore other regions of the image. Around a given pixel, the field intensity decreases after every alteration of this pixel. The equation of the relaxation dynamic is expressed as follows:

Ψ t+1 (x, y) = μ × Ψ t (x, y), μ < 1 ( 7
)
Ψ 0 (x, y) corresponds to the created field after the first alteration of the pixel. The constant μ set to 0.9, represents the decrease rate of the field intensity.

After several alignements of a given pixel, the field intensity around this pixel decreases, and tends to zero. This situation represents the final state of the process, after which the system can be stopped.

Edge Detection and Noise Removal

While moving over the image, agents smooth pixels that approximatively belong to their respective planar regions. An agent considers pixels that do not belong to its current region as noise pixels. The agent aligns thus automatically these pixels to its current planar region (Fig. 3b). However, pixels on the boundaries of planar regions are true-edge pixels and thus should not be aligned. Nevertheless, the competition between agents preserves these pixels against an inappropriate smoothing. Indeed, around the edge between two adjacent planar regions, two groups of agents are formed on the two sides of this edge. Each group is formed of agents passing from one region to the other. Agents of each group align the pixels of the boundary to their respective region. So, pixels of the boundary are continuously swapped between the two adjacent regions. This allows these pixels to remain emergent in the image (Fig. 3c). This pattern of competitive actions between agents allows the emergence of image edges. The edge map is not coded in any agent, it results from the collective actions of the agents. Unlike true regions of the image, which remain preserved against erasing, noise regions continuously narrow, and they finally disappear. Borders of these regions are continuously aligned to the true planar regions, that surround them. An agent, having aligned a pixel which belongs to the border of a noise region and having moved inside this region, will not be able to adapt. Consequently, it cannot align any pixel when leaving the noise region. This occurs in two distinct situations: 1) when a region is planar but insufficiently large to allow agents to cross the minimal path-length L necessary to be able to adapt; 2) when a region is sufficiently large but not planar, or made of random depths (noise). In both situations, the agent leaves the noise region and will adapt inside the surrounding planar region. The true regions have large sizes sufficient to allow agent to adapt and then align boundary pixels when leaving these regions. However, noise regions, which are non planar, or having weak size, prevent agents from adapting. Consequently, agents will be unable to align pixels on the boundaries of these regions when leaving them. As a result, boundaries of these regions are continuously aligned from outside by including their pixels in the true surrounding regions. After several execution steps, these regions will be completely erased.

After Several iterations, all image regions are well delimited by the detected boundaries. A simple region growing steered by the detected boundaries allows to extract the regions of the image.

Experiments and Analysis

Evaluation Framework

Hoover et al. have proposed a dedicated framework for the evaluation of range image segmentation algorithms [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF], which has has been used in several related works [START_REF] Jiang | Edge detection in range images based on Scan Line approximation[END_REF][START_REF] Jiang | Some further results of experimental comparison of range image segmentation algorithms[END_REF][START_REF] Li | Gradient-based polyhedral segmentation for range images[END_REF][START_REF] Ding | Range image segmentation based on randomized hough transform[END_REF][START_REF] Hadiashar | Range image segmentation using surface selection criterion[END_REF]. The framework consists of a set of real range images, and a set of objective performance metrics. It allows to compare a machine-generated segmentation (MS) with a manually-generated segmentation, supposed ideal and representing the ground truth (GT). The most important performance metrics are the numbers of instances respectively of correctly detected regions, oversegmented regions, under-segmented regions, missed regions, and noise regions. Region classification is performed according to a compare tool tolerance T ; 50% < T ≤ 100% which reflects the strictness of the classification. The 40 real images of ABW database are divided into two subsets: 10 training images, and 30 test images. The training images are used to estimate the parameters of a given segmentation method. Using these parameters, the method is applied to the test images. The Performance metrics are computed and stored in order to be used to compare the involved methods. In our case, four methods, namely USF, WSU, UB and UE, cited in [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF] are involved in the result comparison.

Parameter Selection

Since the evaluation framework provides a set of training images with ground truth segmentation (GT), we have opted to a supervised approach for the selection of parameters. For our System, named 2ARIS for Agent-based Approach for Range Image Segmentation , six parameters should be set: ξ min , ξ max , T r θ , T r D , N , and L. These parameters are divided into two subsets: 1) ξ min , ξ max , T r θ , and T r D represent respectively the two alignment strengths, the angle threshold, and the depth threshold. These parameters are used for testing and aligning image pixels, and 2) N and L represent respectively the number of agents, and the adaptation path-length. These two parameters control the dynamic of the multiagent system. For the first parameter subset, 256 combinations namely (ξ min , ξ max , T r θ , T r D ) ∈ {0.5, 0.3, 0.1, 0.05} × {1.0, 3.0, 5.0, 7.0} × {15, 18, 21, 24} × {12, 16, 20, 24} were run on the training images. The performance criterion for this parameters is the average number of correctly detected regions with the compare tool tolerance T set to 80%. The two alignment strengths ξ min and ξ max are set respectively to 0.3 and 5.0. These values have provided a good edge detection in a reasonable execution time. The threshold T r θ was set to 20. We have observed that higher values of this parameter under-differentiate regions regarding their orientations, and lead to an under-segmentation of the image. However, lower values over-differentiate regions, and lead to an over-segmentation. It results in a high number of false and small regions, which should be merged in the true neighboring regions. Finally, the threshold T r D is set to 16. Note that values significantly greater than 16 can lead to wrongly merge some parallel overlapped regions. However, if T r D is significantly less than 16, highly sloping regions cannot be detected as planar regions [START_REF] Jiang | Edge detection in range images based on Scan Line approximation[END_REF]. This results in a high rate of missed regions.

The number of employed agents N depends on the size of the image, while the adaptation path-length L depends on the level of detail of the image. These two parameters are critical and must be carefully selected. Inappropriate values of these two parameters can lead to a high rate of segmentation errors. Indeed, an insufficient number of agents lead to an under-processing of the image. So, resulting regions are deprived of a set of pixels which should be included in these regions. A low value of the adaptation path-length L leads to take into account small planar regions which should be considered as noise regions. However, higher values of L can result in missing some true planar regions which are insufficiently large (see section 4.3). In order to set the parameters N and L, 25 combinations of these parameters, namely (N, L) ∈ {1500, 2000, 2500, 3000, 3500} × {3, 5, 7, 9, 11} were run on the training set. In this case, the performance criterion is the average number of noise regions, with the compare tool tolerance set to 80%. Obtained optimal values of N and L are respectively 2500 and 7.

Experimental Results

Fig. 4 shows an instance of segmentation progression within time. The time t represents the number of steps performed by each agent since the beginning of the process. Displaying a range image by a simple rendering algorithm (Fig. 4a), allows observing the high level of noise in the used images. Figures 4b, 4c, 4d and4e show the set of pixels of interest (edge or noise pixels) respectively at t=1000, 5000, 9000 and 13000. Regions are progressively smoothed by aligning noise pixels to the surrounding planar regions. Edges between adjacent regions are also progressively thinned. At the end of the process, region borders consist of thin lines of one pixel wide (Fig. 4e). Fig. 4f shows the segmentation result obtained by displaying the borders of the extracted regions.

Fig. 5 shows the segmentation results of the image abw.test.8, with the compare tool tolerance T set to 80%. This image was considered as a typical image to compare the involved methods [START_REF] Hoover | An experimental comparison of range image segmentation algorithms[END_REF], [START_REF] Ding | Range image segmentation based on randomized hough transform[END_REF]. Fig. 5a shows the range image, and Fig. 5b shows the ground truth segmentation (GT). Fig. 5c, 5d 5e and 5f are segmentation results obtained respectively by USF, WSU, UB and UE methods. Fig. 5g presents the segmentation result obtained by our method. Metrics in table 1 show that all image regions detected by the best-referenced segmenter (UE) were detected by our method. Except the shadowed region, where all methods fail to detect, all object regions were detected. The incorrectly detected regions are those with small sizes, and situated on the horizontal support. Compared to the other methods, values of incorrect detection metrics are also good. Our method is equivalent to UE, and scored higher that the others. Table 2 contains the average results obtained with all test images, and for all performance metrics. The compare tool tolerance was set to the typical value 80%. By considering both correct detection and incorrect detection metrics, obtained results show the good efficiency of our method. Fig. 6 shows the average numbers of correctly detected regions for all test images, and according to the compare tool tolerance T ; T ∈ {51%, 60%, 70%, 80%, 90%, 95%}. Results show that the number of correctly detected regions by our system is in average better than those of USF, UB and WSU. For instance, our system scored higher than WSU for all the values of the compare tool tolerance T . It scored higher than USF for T ∈ {80%, 90%, 95%}, and better than UB for T ∈ {50%, 60%,70%, 80%}. For all incorrect detection metrics (instances of Oversegmentation, Under-segmentation, Missed Region, Noise Region), our system has equivalent scores to those of UE and USF. The two latter scored higher than UB and WSU.

Conclusion

In this paper we have introduced an agent-based approach for range image segmentation. Indirect interaction between autonomous agents moving over the image allows reliable edge detection and efficient noise removal. Competitive actions between agents that are self-gathered around region boundaries have allowed the emergence of image edges. Image edges, for which no explicit detection was coded in any agent, result from the collective action of all the agents. The proposed approach aims to improve efficiency and to deal with the problem of result accuracy. Indeed, obtained results are better than those provided by traditional algorithms, based on region growing techniques. Moreover, employed agents are weakly coupled, and indirectly communicate via the environment (image). This allows parallel or distributed implementations, necessary to obtain a high computational efficiency. Experimental results obtained with real images from ABW database were compared to those provided by four typical algorithms for range image segmentation. Comparison results show a good potential of the proposed method for both segmentation efficiency and accuracy. The proposed approach can be extended to deal with more complex surfaces by defining their specific proprieties, and endowing the agents with the appropriate behavior.

Fig. 1 .

 1 Fig. 1. Agent behavior according to its state and position

  (a , b , c , d ) of the new plane equation at the pixel position are obtained by linear combination of the current parameters (a, b, c, d) and the parameters of the memorized plane equation (a , b , c , d ):

Fig. 2 .

 2 Fig. 2. The impact of the alteration strength on the segmentation results: (a) Range image (abw.test.8); (b) Rendered range image; (c) Segmentation results with ξmin = ξmax = 4 at t=2500; (d) Segmentation results with ξmin = 0.3 and ξmax = 5 at t=13000

Fig. 3 .

 3 Fig. 3. Underlying edge detection (image abw.test.22): (a) Rendered range image; (b) Aligned pixel at t=800 ; (c) Only edge pixels are emergent at t= 8000

Fig. 4 .Fig. 5 .

 45 Fig. 4. Segmentation progression. (a) Rendered range image (abw.test.6); (b) at t=1000, (c) at t=5000 ; (d) at t=9000 ; (e) at t=13000 ; (f) Segmentation result (Extracted regions)

Fig. 6 .

 6 Fig. 6. Average results of correctly detected regions of all methods, according to the compare tool tolerance T ; 0.5 < T ≤ 1.0

Table 1 .

 1 Comparison results with abw.test.8 image for T =80%

	Method GT Correct	Over-	Under-	Missed Noise
		region detection segmentation segmentation
	USF	21	17	0	0	4	3
	WSU	21	12	1	1	6	4
	UB	21	16	2	0	3	6
	UE	21	18	1	0	2	2
	2ARIS 21	18	2	0	1	1

Table 2 .

 2 Average results of the different involved methods with T =80%

	Method GT Correct	Over-	Under-	Missed Noise
		region detection segmentation segmentation
	USF 15.2	12.7	0.2	0.1	2.1	1.2
	WSU 15.2	9.7	0.5	0.2	4.5	2.2
	UB	15.2	12.8	0.5	0.1	1.7	2.1
	UE	15.2	13.4	0.4	0.2	1.1	0.8
	2ARIS 15.2	13.0	0.5	0.1	1.4	0.9