Teresa Abáigar
email: abaigar@eeza.csic.es

Miguel A Domené

Francisco Palomares

Effects of fecal age and seasonality on steroid hormone concentration as a reproductive parameter in field studies

Keywords: Ammotragus lervia, Fecal steroids, Gazella dama mhorr, Hormone degradation, Lynx pardinus

We studied how fecal age (6, 12, 24, 48, 72, and 168 h) and seasonality affect variation in the fecal steroid hormone metabolite concentration in three endangered mammalian species, Mhorr gazelle, Saharan Barbary sheep, and the Iberian lynx. Except for estrogens, concentrations remained stable for at least 48 h in the Mhorr gazelle and the Saharan Barbary sheep. Steroid hormone metabolite concentration remained stable in the Iberian lynx throughout the experiment (1 week). Seasonality was the main factor affecting variation in hormone metabolite concentrations, and although the response was species-and hormone-specific, the dry season resulted in increased hormone metabolite concentrations, while the wet season reduced the concentration.

Introduction

Noninvasive fecal steroid metabolite analysis has been widely developed and used over the last 20 years, and longitudinal data series on reproductive status and welfare of captive, semi-captive, and free-ranging wildlife species have been collected (reviewed in [START_REF] Schwarzenberger | The many uses of non-invasive faecal steroid monitoring in zoo and wildlife species[END_REF]. However, the accuracy of such analyses may be affected by fecal sampling and storage [START_REF] Khan | A matter of time: evaluating the storage of fecal samples for steroid analysis[END_REF][START_REF] Schwarzenberger | The many uses of non-invasive faecal steroid monitoring in zoo and wildlife species[END_REF][START_REF] Herring | Stability of avian fecal corticosterone metabolite levels in frozen avian feces[END_REF], particularly for free-ranging species, as locating fresh feces daily may often not be possible or be inappropriate due to behavioral restrictions [START_REF] Linklater | The robustness of faecal steroid determination for pregnancy testing Kaimanawa feral mares under field conditions[END_REF]. Frequently, the only fecal material available has been exposed to different environmental/weather conditions for an unknown length of time.

Because of microbial metabolism and other biochemical processes that occur over time, changes in humidity and temperature are probably the most influential environmental factors affecting steroid metabolite concentration in feces of free-ranging animals [START_REF] Washburn | Effects of simulated environmental conditions on glucocorticoid metabolite measurements in white-tailed deer feces[END_REF][START_REF] Terio | Comparison of different drying and storage methods on quantifiable concentration of fecal steroids in the cheetah[END_REF]. Moreover, under field conditions, the fecal material undergoes changes in water content derived from its exposure to changing seasonal environmental conditions. To avoid bias derived from changing water content, most studies analyzing fecal steroids use feces that have been lyophilized before analysis and report the steroid concentrations as nanograms per gram dry feces. However, fresh, wet fecal samples are used increasingly (see [START_REF] Pickard | Hormonal characterization of the reproductive cycle and pregnancy in the female mohor gazelle (Gazella dama mhorr)[END_REF][START_REF] Schoenecker | Comparison of three fecal steroid metabolites for pregnancy detection used with single sampling in bighorn sheep (Ovis canadiensis)[END_REF][START_REF] Monclús | Testing the threat-sensitive predator avoidance hypothesis: physiological responses and predator pressure in wild rabbits[END_REF][START_REF] Pelican | Hormone profiles in the iberian lynx: application to "in situ" and "ex situ" conservation[END_REF]. Although the dilutive effect of water content on fecal steroid metabolites has been proven [START_REF] Monclús | Testing the threat-sensitive predator avoidance hypothesis: physiological responses and predator pressure in wild rabbits[END_REF], it has not been demonstrated that bias in results from wet fecal samples not collected daily is due exclusively to water content, or to the microbiological and biochemical processes during the time elapsed until sampling, or a combination of both.

The purpose of this study was to determine the effects of seasonality on the final concentrations of steroid hormones (estrogens, testosterone, and progestagen metabolites) in fresh feces exposed to field conditions for different periods of time in order to determine whether fecal material collected in the field can be used for accurate steroid hormone metabolite analysis.

The study was carried out using captive individuals of three endangered mammalian species: one carnivore, the Iberian lynx (Lynx pardinus), and two herbivores, the Mhorr gazelle (Gazella dama mhorr) and the Saharan Barbary sheep (Ammotragus lervia sahariensis). Two different groups of mammals were used to test the effect of climatic conditions on different types of fecal material since hormone degradation is expected to be related to fecal composition.

Materials and methods

Study areas

Mhorr gazelle (MG) and Saharan Barbary sheep (SBS) were maintained in captivity in the Parque de Rescate de la Fauna Sahariana (Estación Experimental de Zonas Àridas) in Almería (36°45′ N, 3°00′ W) on the Mediterranean coast of southeastern Spain, one of the warmest and most arid areas in Europe. The mean annual temperature is 18°C and the average rainfall is 237 mm [START_REF] Lázaro | Analysis of a 30-year rainfall record (1967-1997) in semiarid SE Spain for implications on vegetation[END_REF]. The Iberian lynx (IL) was kept in the "El Acebuche" breeding center at Doñana National Park, which is close to the Atlantic coast in southwestern Spain (36°56′ N, 6°21′ W). Owing to its proximity to the Mediterranean Sea, the climatological regime is Mediterranean with Atlantic influences, the mean annual temperature is 18°C, and the average rainfall is <600 mm [START_REF] Yánez | Cambios en la vegetación de la franja litoral de las marismas de Doñana (Huelva, España) durante el holoceno reciente[END_REF].

Source of animals

Feces were collected from captive adults of all three species which were living in outdoor conditions under captive breeding programs implemented to prevent their extinction and support reintroduction into their original habitats (see [START_REF] Alados | Captive populations of Northwest African antilopinae and caprinae at the Estación Experimental de Zonas Áridas[END_REF][START_REF] Alados | Aoudad. Ammotragus lervia from Western Sahara[END_REF][START_REF] Barbosa | Internacional studbook. Gazella dama mhorr[END_REF]; http://www.lynxexsitu.es for more details). Captive IL were fed live domestic rabbits daily, MG and SBS were fed commercial pellets, barley, and fresh alfalfa, and SBS also received a daily ration of straw. Water was always available ad libitum for all three species.

Experimental design

Early in the morning, feces from ten adult male and ten adult female MG and SBS were individually collected immediately after voidance; samples were collected off the floor but avoiding those directly in contact with the soil surface. After collection, 12 individual droppings from each MG and eight individual droppings from each SBS were weighed and then stored at -20°C until analysis. The rest of the droppings, previously homogenized, were placed directly on soil and exposed to environmental conditions inside a small open-air enclosure to avoid disturbance. The same number (12 for MG and 8 for SBS) were then taken from these previously collected droppings at 6, 12, 24, 48, 72, and 168 h after voidance, weighed and stored at -20°C.

To test for the effect of different climatic conditions, this sampling procedure was repeated seasonally (spring: April 11-15, 2005;summer: July 4-8, 2005; autumn: November 28 to December 2 2005; winter: February [16][17][18][19][20][21][22][23][24][25][26]2006). In addition, after a winter rainfall event, additional samples were taken 96 h after voidance to test the effect of a complete event of wetting-drying.

Once each season (spring: May 17-24, 2006;summer: August 22-28;autumn: December 13-20, 2006;winter: March 14-21, 2007), fresh feces were also collected early in the morning from 11 adult female and six adult male IL. The fecal material collected in the morning was from depositions left the night before, and the precise time between the deposition/voidance and fecal collection could not be known exactly, but was always <10 h. To account for possible heterogeneous distribution of the steroid hormones in a single fecal bolus, each was subdivided into five sections, and a small portion of each section was immediately frozen until analysis. We thus ensured that differences in hormone concentration within feces did not affect the results. The remaining fecal material was placed directly on soil and exposed to the prevailing environmental conditions in a protected open-air enclosure and samples of each portion were taken and frozen until analysis at 12, 24, 48, 72, and 168 h from collection.

Temperature and relative humidity were recorded at 5min intervals throughout the study using a micrologger (HOBO©, ONSET Computer Corporation, USA) placed 10 cm above the soil. Table 1 shows average temperatures, rainfall, and humidity during the study.

Steroid extraction and assay

Steroid hormone metabolites (progestagens, estrogens, and testosterone) in all fecal samples were extracted and determined following standardized procedures. Steroids were extracted by following a standardized protocol [START_REF] Brown | Comparative aspects of steroid hormone metabolism and ovarian activity in felid measured non-invasively in feces[END_REF]) with minor modifications. Briefly, fecal material was mixed thoroughly and a subsample of 0.18-0.2 g was extracted using ethanol (100%, 4.5 mL) and distilled water (0.5 mL); after 30 min of shaking (Multipulse vortexer, Glas-Col®, USA), samples were centrifuged at 1,076×g for 20 min, the supernatant was transferred to a glass tube, and the pellet resuspended in 4.5 mL of ethanol and 0.5 mL of distilled water, vortexed (1 min), and recentrifuged. Combined supernatants were then dried and evaporated with dry air. One milliliter of methanol was added to the dry extract and placed in an ultrasonic glass cleaner (Branson® 8510) for 20 min. The extracts were diluted in a dilution buffer and stored frozen until analysis.

Fecal steroid hormone metabolites were determined following the procedures described by [START_REF] Munro | Relationship of serum estradiol and progesterone concentrations to the excretion profiles of their major urinary metabolites as measured by enzyme immunoassay and radioimmunoassy[END_REF] for enzyme immunoassay. Antibodies for progestin (monoclonal Pregnane CL425, 1:10,000 dilution), estrogen (polyclonal E 2 -R4972, 1:10,000 dilution), and testosterone (polyclonal testosterone R-156/7, 1:7,500 dilutions) were provided by Coralie Munro (University of California, Davis, CA, USA). The CL425 cross-reacts with various progestagen metabolites, including 4-pregnen-3,20-dione (100%), 4-pregnen-3α-ol-20-one (188%), 4-pregnen-3β-ol-20-one (172%), 4-pregnen-11α-ol-3,20-dione (147%), 5αpregnan-3β-ol-20-one (94%), 5α-pregnan-3β,20-dione (64%), 5α-pregnan-3,20-dione (55%), 5β-pregnan-3β-ol-20-one (12.5%), 5-pregnan-3,20-dione (8.0%), 4-pregnen-11β-ol-3,20-dione (2.7%), and 5β-pregnan-3α-ol-20-one (2.5%; [START_REF] Graham | A versatile enzyme immunoassay for the determination of progestogens in feces and serum[END_REF]). The R4972 cross-reacts with estradiol 17B (100%) and estrone (3.3%). The R156/7 cross-reacts with testosterone (100%) and 5αdihydrotestosterone (57.37%). Before analysis, fecal extracts were diluted in phosphate-buffered saline (MG: 1:10 to 1:50 for estrogens, 1:50 to 1:3,000 for progestagens, and 1:10 to 1:20 for testosterone; SBS: 1:10 to 1:20 for estrogens, 1:50 to 1:600 for progestagens, and 1:10 to 1:20 for testosterone; IL: 1:5 to 1:540 for estrogens, 1:50 to 1:4,800 progestagens, and 1:10 to 1:40 for testosterone). Serial dilutions of pooled fecal extracts produced displacement curves parallel to those of the appropriate standard. The correlation coefficients of parallelism test were R 2 =0.985, R 2 =0.983, R 2 =0.992 for progestagens, estrogens, and testosterone, respectively. Inter-assay CVs were (mean ± SEM) 7.98±2.04, 8.25±1.1, and 8.46±1.29 for progestagens, estrogens, and testosterone, respectively; intra-assay CVs were <10%. Assay sensitivities were 1.17 pg/well (estrogens), 1.09 pg/well (progestagens), and 2.05 pg/well (testosterone). Absorbance was measured at 405 nm with an automatic microtiter plate spectrophotometer (Tecan®, sunrise, Austria), and the data were transferred to an interfaced computer (Magellan®, Austria). Hormone concentrations are expressed as nanograms per gram wet feces, except for IL progestagens which is expressed as micrograms per gram wet feces.

Data analysis

General linear models (type III for MG and SBS and type V for IL) were used to test the effect of fecal age and season on steroid metabolite concentration. Multiple comparisons (Bonferroni test) were used to determine significant differences in feces of different ages and seasons.

The change in hormone metabolite concentration was expressed as the percent difference between the original hormone metabolite concentration (C t0 ) and the concentration at the later sampling time (C ti ). Thus, much of the variation in hormone metabolite concentration was eliminated by the reproductive cycle of the individual involved. The individual was treated as a random effect in the analysis. A negative value means lower hormone metabolite concentration than at the starting time (C t0 ). Fecal weight was also included as a variable in the analysis. This might be important since hormone metabolite concentration was expressed by wet fecal weight and might therefore be influenced by variation in temperature and humidity across seasons. This variable was expressed as the percentage difference between the original weight (t 0 ) and weight at the later sampling time (t i ).

As IL feces were subdivided into five sections to test for unequal hormone distribution within the same feces, we used a repeated measure ANOVA to test for uniform distribution of steroid hormone metabolites in individual fecal samples. At time 0, no significant differences were found either for all steroid metabolites together (F=1.69, df=4, p=0.279) or for each individual steroid metabolites (F=1.28, df=8, p=0.251); therefore, the average of the five sections at time 0 (control) was taken to calculate the variation in steroid hormone metabolite concentrations at the different sampling times.

Results

Variation in fecal hormone metabolite concentrations in the two herbivores largely depends on the time of exposure to field conditions and season (Table 2). The seasonal effect on variation in MG progestagens was only significant when tested as an individual effect (ANOVA F=4.6, df=3, p= 0.004), while variation in the IL fecal steroid metabolites concentration was only significant as a function of season (Table 2). Moreover, variation in MG and SBS fecal estrogen concentration showed a weak but significant correlation with variation in fecal weight (MG: F=4.4, df=1, p=0.04, r=0.14; SBS: F=7.2, df=1, p=0.007, r= 0.18). Variation in estrogen concentration decreases as fecal weight rises as a result of hydratation after a winter rainfall event.

In the two herbivores, most variation in fecal steroid metabolite concentration was found in testosterone and progestagens, followed by the estrogens. In the IL, variation in the three hormone metabolites was quite similar between themselves, although slightly higher in testosterone.

MG progestagen and testosterone concentrations remained stable for the first 48 h, but became significantly higher after 72 h with increases in summer and winter (Table 3). Seasonal effects on variation in testosterone concentration were significant in summer when concentrations were highest at 72 and 168 h after voidance and in winter at 12 and 24 h when they were lowest (Table 3). The percentage variation in estrogen concentration in MG was significantly increased 12 h after voidance in summer when concentrations are highest and was lowest in winter 12 and 24 h after voidance (Table 3).

With the exception of the SBS samples during winter, the percent variation of progestagens remained stable throughout exposure. Nevertheless, there were significant differences between seasons; in winter, the values showed little change over the first 24 h of exposure, while in summer, the values were more stable after the first 24 h (Table 3). Testosterone concentrations remained stable for 48 h, with the least variation in autumn and winter. After that, a significant increase was observed at 72 h in autumn and at 168 h in winter (Table 3). The percentage variation in estrogens varied with exposure time, with the least variation recorded in summer after 24 h of exposur, and was also significantly low in autumn after 6 h of exposure and in winter after the first 6 h of exposure (Table 3). In the IL, significant seasonal differences were observed with the least variation in hormone concentration in autumn and winter (Table 3).

Discussion

Variation in progestagens and testosterone concentrations of the two herbivore species studied remained stable for at least 48 h after voidance (168 h for SBS progestagens). However, changes in estrogen concentrations were significant after 6 h of exposure. In the IL, hormone metabolite concentrations remained stable throughout the experiment (1 week). Consequently, if the purpose of a field study is qualitative definition of reproductive events, old fecal samples (48 h after voidance for SBS and MG and 1 week for IL) can be used, unless the study requires estrogen determination (only up to 6 h in MG and SBS feces). However, caution is needed if the study requires hormone determinations for major reproductive events (e.g., onset of puberty, ovulation) to be accurate as the variations in hormone concentration from the fresh feces were very high for all three species and the three hormone metabolites (especially testosterone metabolites in the two herbivore species). This means that in terms of a field study, feces age fast. These results are in agreement with the findings of [START_REF] Millspaugh | Use of fecal glucocorticoid metabolite measures in conservation biology research: considerations for application and interpretation[END_REF] who recommended the collection of only fresh samples (<1 or 2 h old). Variation in fecal weight following exposure of feces to field conditions only had a significant effect on variation in SBS and MG estrogen concentration. This hormone was sensitive to the diluting effect of the winter rainfall event in Almeria. Differences in diet between the two herbivores (MG and SBS) and the carnivore (IL) are likely to be among the causes of these species-specific differences. The higher fiber content in herbivore feces explains their ability to retain (by hydrating) or to eliminate (by drying) water, and these results might therefore only reflect a difference in the water content of the feces. Although this study did not show a significant effect of fecal weight on the other hormones or species studied, we suspect that it could potentially affect feces sampled after a rainfall event. The majority of the negative or small variations in concentration in these three MG and SBS hormones were found in winter and in the IL in autumn and winter during the first 48 h of the experiment related to rainfall events in the study areas at that time. Consequently, collection of fecal material immediately after a rainfall event or in a humiditysaturated atmosphere is not recommended.

These results further support species-specific differences in steroid metabolism [START_REF] Schwarzenberger | The many uses of non-invasive faecal steroid monitoring in zoo and wildlife species[END_REF]. The origin of all these differences is the confluence of multiple causes pointed out by other authors: specificity of microbes in the feces that metabolize the fecal steroids, effect of temperature on steroid metabolite degradation, environmental moisture which allows the growth of microbes and fungi, change in metabolite affinity for antibodies, and the different chemical stability of each hormone [START_REF] Matkovics | In vitro transformation of steroids as a substitute of microbial transformation[END_REF][START_REF] Woods | Chemical and microbiological transformation of steroids[END_REF][START_REF] Neumann | Studies on the stability of progestagens in faeces of several wild animal species kept in a zoological garden[END_REF][START_REF] Terio | Comparison of different drying and storage methods on quantifiable concentration of fecal steroids in the cheetah[END_REF][START_REF] Washburn | Effects of simulated environmental conditions on glucocorticoid metabolite measurements in white-tailed deer feces[END_REF][START_REF] Millspaugh | Use of fecal glucocorticoid metabolite measures in conservation biology research: considerations for application and interpretation[END_REF]. Exhaustive condition-controlled laboratory metabolism studies, followed by precise identification of new metabolites, HPLC mass spectrometry, nuclear magnetic resonance, or infrared spectroscopy, are recommended to find out the precise hormone metabolism and degradation processes and the factors affecting them. These results would permit the development and use of more specific antibodies for precise results when degraded fecal samples are used to study reproductive processes under field conditions.

As a general conclusion, this study shows that the use of fecal samples of unknown age to monitor reproduction in animals under field conditions is of limited use. Although it is suitable for the qualitative determination of reproductive events, it is not as successful for accurate determination of hormone concentrations. Aged fecal samples should not be collected in summer because hormone degradation is likely to change the resultant measurements. Moreover, fecal samples collected in a humid atmosphere or after a rainfall event should be desiccated to avoid errors deriving from fecal water content.

Table 1

 1 Temperature (T, °C), humidity (H, %), and rainfall (mm, mean, range) in the study areas,Almería and Doñana National Park (Spain)

	Almería

Table 2

 2 Effect of fecal age and season on steroid metabolite concentration analysis

Table 3

 3 Time course of average percentage of change (±SEM) of fecal steroid hormone metabolite concentration in the mhorr gazelle, Saharan Barbary sheep, and Iberian lynx Different letter series for significant differences between time sampling; different number series for seasonal differences; p<0.05. Sample size in parentheses. A negative value means lower hormone concentration than at the starting time Spr spring, Sum summer, Aut autumn, Win winter

		Win		2.8±8 a13	-10.0±3.5 a23	-7.6±4.9 a2	52.8±12.4 b2	50.1±8.9 b2	60.3±14 b	55.3±14.4 b13		55.5±15.3 a2	22.1±8.2 a2	27.2±9.1 a2	54.6±9.6 a2	74.6±15.2 2	119.9±23.8 b	133.6±17.3 b		Win (5)	23.3±12.7 1	14.2±8 2	37.6±10.9	3.9±27.9	22.3±12.3
		Aut		67.5±6.1 12	65.8±11.9 13	77.9±11.4 1	76.3±1.2 1	67.0±6 2		63.7±11.3 13		48.9±11.4 a2	82.1±13.7 a1	91.0±32.9 a1	85.8±13.1 a2	118.1±22.9 b1		72.6±18.9 a		Aut (4)	-7.9±12.5 2		-11.7±28.7	16.1±19.6	2.01±21.9
	Testosterone	Spr Sum		53.0±9 1 110.5±19.2 a2	90.2±11.7 1 117.8±17.8 a1	92.2±16.4 1 104.9±22.4 a1	90.8±15.9 1 122.0±13.9 a1	105.9±14.2 1 192.5±52.5 b1		96.7±15.5 1 179.4±30.2 a2		126.8±16.2 1 126.9±15.1 1	146.4±29.5 1 130.9±17.2 1	158.4±33.7 1 113.8±16.5 1	168.4±24.9 1 114.2±19.9 1	162.1±18.4 1 129.2±19.5 1		141.9±26.6 144.5±19.6		Spr (4) Sum (4)	27.9±12.7 1 38.4±11.6 1	40.5±5.9 1 41.5±12.5 1	62.6±9.6 24.9±26.3	44.6±9.4 26.7±12	49.8±6.7 22.4±9.3
		Aut Win		18.3±7.2 11.8±16.6	33.7±8.1 1 -30.5±3.9 a3	21.5±8.9 2 -17.2±5.5 1	27.8±7.8 12 -4.9±4.5 13	40.1±9.7 12 -8.2±9.2 1	-7.8±5.6	22.8±10.9 1 18.3±16.2 b1		29.9±11.8 1 -9.9±7.2 2	11.4±7 2 -33.8±3.8 a3	8.6±9.5 23 -25.5±6.6 24	8.8±8.5 2 -16.7±4.5 3	13.4±7.5 2 -13.2±7.4 2	-5.8±10	27.9±11.9 1 0.9±9.7 b2		Aut (6) Win (6)	-19.1±11.1 -11.6±10.9	-9.3±13.4 -19.8±13.1	-22.1±4.7 23 -12.3±9.8 2	5.1±9.4 -3.6±6.8	-0.3±10.5 -2.3±10.1
	Estrogens	Spr Sum		15.1±4.6 32.9±7.5	7.4±3.4 1 40.5±10.8 2	5.7±5.3 1 31.8±9.5 2	10.9±6.9 1 36.0±3.9 2	12.6±6.1 1 41.2±7.3 12		8.0±6.2 1 40.7±7.2 2		45.6±6 1 42.8±7.9 a1	36.4±6.7 1 41.9±6.1 a1	46.5±9.4 1 23.9±7.7 a13	51.6±8.6 1 16.3±5 a2	51.8±9.1 1 6.9±5.3 b2		56.6±8.6 1 8.8±7 b2		Spr (5) Sum (6)	36.6±18.8 31.6±15.6	16.3±12.3 9.4±12.1	29.0±12.9 1 20.3±9.5 13	12.8±14.2 28.4±4.8	23.7±14.3 1.12±23.1
		Aut Win		6.6±8.3 18.4±10.9 a	19.6±15.8 10.5±23.3 a	14.7±13.6 33.1±21.9	23.6±8.7 40.3±15.7	23.8±13.6 112.8±45.2	79.5±30.4	0.6±6.8 1 184.3±73.4 b2		105.9±35.3 1 2.24±6.3 a2	87.6±30.5 1 -22.1±7.2 b2	78.3±29.2 1 -11.6±4.6 b2	124.7±48.5 1 14.4±11.2 a2	85.1±35.9 1 44.8±14.8 a1	37.4±13.3 a	98.6±39.3 1 39.5±9.5 a1		Aut (6) Win (6)	19.4±8.5 1 -9.5±7.4 2	-7.8±23.2 -14.8±15.8	2.5±17.1 16.8±7.6	6.7±14.8 -9.4±18.6	32.3±8.5 15.7±3.4
	Time Progestagens	Spr Sum	Mhorr gazelle (10)	6 11.8±6.9 35.1±12.3	12 9.2±16 a 82.9±30	24 31.6±8.5 39.7±35.5	48 58.4±9.8 b 51.4±28.4	72 30.4±13 129.6±61.2	96	168 52.6±18.1 93.8±31.4 a	Saharan Barbary sheep (10)	6 92.0±22 1 50.6±28.8 1	12 80.0±27.6 1 40.5±11.4 1	24 104.2±31.3 1 45.5±14.2 1	48 108.1±32.4 1 19.3±15.4 2	72 100.9±30.8 1 6.6±9.1 2	96	168 90.0±17.4 1 1.76±15.5 2	Iberian lynx	Spr (5) Sum (5)	12 35.0±17.7 1 32.1±7.2 1	24 27.8±5.5 28.3±5.7	48 37.7±8.4 16.8±9.5	72 24.7±15.1 23.1±5.7	168 35.2±11.4 11.7±19.5

Eur J Wildl Res (2010) 56:781-787

Acknowledgments We thank the Parque de Rescate de la Fauna Sahariana (Estación Experimental de Zonas Áridas, Almería) and the Centro de Cría del Lince Ibérico "El Acebuche" (Parque Nacional de Doñana, Huelva) for access to the animals and logistical support. We also thank Ch. Petronella, A. López, J. Cassinello, and J.C. Rivilla for field assistance. W.V. Holt and two anonymous reviewers made useful comments of this manuscript. This work was funded by the Spanish Ministry of Education and Research (grant CGL2004-00603) and the European Regional Development Fund.


Sonali Wayal

Frances M Cowan

Pamela Warner

Andrew Copas

David Mabey

Maryam Shahmanesh

Contraceptive practices, sexual and reproductive health needs of HIV

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Contraceptive practices, sexual and reproductive health needs of HIV positive and negative female sex workers in Goa, India

Sonali Wayal MA, MSc [START_REF]Maternal Mortality in 2005: Estimates developed by WHO, UNICEF[END_REF][START_REF]Prevention of Parent to Child Transmission[END_REF] , Frances Cowan MSc MD [START_REF]Prevention of Parent to Child Transmission[END_REF] , Pamela Warner PhD [START_REF] Chandrasekaran | Containing HIV/AIDS in India: the unfinished agenda[END_REF] , Andrew Copas PhD 2 , David Mabey PhD [START_REF]HIV Data[END_REF] , Maryam Shahmanesh MSc, PhD [START_REF]Maternal Mortality in 2005: Estimates developed by WHO, UNICEF[END_REF][START_REF]Prevention of Parent to Child Transmission[END_REF] Positive People, Goa, India 1 ; Center for Sexual Health and HIV Research, University College London, UK [START_REF]Prevention of Parent to Child Transmission[END_REF] ; Centre for Population Health Sciences, University of Edinburgh, Scotland, UK [START_REF] Chandrasekaran | Containing HIV/AIDS in India: the unfinished agenda[END_REF] ; London School of Hygiene and Tropical Medicine, London, UK [START_REF]HIV Data[END_REF] Total words Abstract: 250 Email address: sonaliwayal@yahoo.com, s.wayal@ucl.ac.uk Fax: +44 (0)20 7380 9681

Introduction

In India, maternal mortality and HIV/AIDS continue to be major public health challenges. In 2005, India had the largest number of maternal deaths globally i.e. 117,000. [START_REF]Maternal Mortality in 2005: Estimates developed by WHO, UNICEF[END_REF] Of the 27 million pregnancies annually, 189,000 are estimated to occur in HIV positive women resulting in 56,700 HIV infected infants. [START_REF]Prevention of Parent to Child Transmission[END_REF] Approximately 0.6%-1.27% adult urban females engage in sex work. [START_REF] Chandrasekaran | Containing HIV/AIDS in India: the unfinished agenda[END_REF] The HIV epidemic is concentrated among high risk groups like female sex workers (FSWs), with HIV prevalence of 5.1%. [START_REF]HIV Data[END_REF] However, there are pockets of high HIV prevalence among FSWs with variation in mean HIV prevalence between states (0-17.9%). [START_REF]HIV Data[END_REF] Since 2004 Goa, a state in the West of India, has been excluded from the surveillance due to the demolition of its red-light area Baina. [START_REF]HIV Data[END_REF][START_REF] Shahmanesh | Targeting commercial sex workers in Goa, India: Time for a strategic rethink?[END_REF] Our study conducted in Goa in the aftermath of demolition showed HIV prevalence among FSWs was 25%. [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF] Prevalence of other sexually transmitted infections (STI) among FSWs is also high. [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF][START_REF] Pal | A study of HIV/STD infections amongst commercial sex workers in Kolkata (India). Part-IV laboratory investigation of STD and HIV infections[END_REF] STI/HIV can cause adverse pregnancy outcomes including stillbirth, congenital syphilis and mother-to-child-transmission of HIV. 8 Besides, multiple sexual partners increases FSWs vulnerability to unintended pregnancies and abortions. 9-10 Unsafe abortions in the presence of STIs can lead to sepsis, pelvic inflammatory disease and infertility. 11 In 2006 the National AIDS Control Organisation's (NACO) started the Prevention of Parent-to-Child Transmission (PPTCT) program which focuses on prevention of HIV positive births i.e. prevention of unintended pregnancies, prenatal HIV transmission and providing care and treatment to HIV-infected women, their infants and families. [START_REF]Prevention of Parent to Child Transmission[END_REF] However the coverage of PPTCT is limited. 12 Contraception is a highly cost-effective HIV prevention strategy. [13][14] Effective contraception can not only prevent vertical HIV transmission but also avert abortion and pregnancy-related morbidity and mortality. 15 Condoms, if used consistently and appropriately, offer dual protection from HIV and pregnancy. Currently the NACO's HIV prevention interventions among FSWs promotes condoms primarily for HIV prevention. [START_REF]HIV Data[END_REF] In India, contraceptive use among the general population women is 56%. [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] The national Reproductive and Child Health Programme (RCHP) focuses on increasing access to contraception and improving child health. [START_REF]Reproductive and Child Health Programme[END_REF] The national household survey (NFHS) has shown that socio-demographic factors like age, education etc affects contraceptive use among general population women. [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] However, there is limited evidence on factors affecting contraceptive use among FSWs.

As represented in the conceptual framework in Figure 1, we hypothesize that among the FSWs, in addition to the factors known to be associated with contraceptive use among general population women in India (i.e. 'a priori factors') (Figure 1: column 1), contraceptive use is also likely to be associated with sex work (SW) related factors (Figure 1: column 2), and the effect of the known factors is likely to be mediated by the SW-related factors. In this paper, we describe the contraceptive practices of HIV positive and negative FSWs in Goa, India and investigate the extent to which contraceptive use is associated with SW-related factors, after adjusting for socio-demographic factors.

Materials and Methods

Study setting

This study was conducted in Goa, India. FSWs largely operated in the Baina red-light area in the southern Goa until its demolition in June 2003. [START_REF] Shahmanesh | Targeting commercial sex workers in Goa, India: Time for a strategic rethink?[END_REF] This study was conducted in collaboration with Positive People; a non-governmental organisation working with FSWs in Goa.

Recruitment of FSWs took place from December 2004-December 2005.

Study population

FSWs were defined as women currently providing sexual services in exchange for goods or money.

Recruitment procedure

Post-demolition rapid ethnographic mapping, participant observation and key informant interviews were used to describe the re-organisation of SW in Goa. Emerging sites and networks of FSWs were identified. FSWs were recruited using respondent driven sampling (RDS), a variant of snow-ball method where the recruitment chain begins with "seeds" (members of the target population). [START_REF] Magnani | Review of sampling hard-to-reach and hidden populations for HIV surveillance[END_REF] The research staff purposively selected seeds, identified from various networks during mapping, to ensure representation based on key variables (age, ethnicity, type and sites of SW). All seeds were asked to recruit three other FSWs from their network to the study. All seeds and subsequent recruits were given three pre-numbered coupons (i.e. questionnaire id and network size mentioned by the FSWs during her interview) to refer other FSWs. FSWs were asked to give one coupon to each recruit they referred for study participation. An interviewer documented the referred recruits questionnaire id and relationship with the FSWs on the returned coupons. This enabled documenting network details of the FSWs. Participants recruited further participants in successive "waves". In RDS, the recommendation is to allow sufficient waves of recruitment to achieve "equilibrium" in the composition of the sample with respect to key variables. We undertook 6-8 waves, usually sufficient in such research. [START_REF] Magnani | Review of sampling hard-to-reach and hidden populations for HIV surveillance[END_REF] Incentives were provided both for participating and for recruiting others, with a limit of three recruits per study participant [100Rs ($2.50)] for participating and a further sum of 50Rs ($1.25) for each successfully recruited referral]. Details of data collection are described elsewhere. [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF][START_REF] Shahmanesh | HIV prevention while the Bulldozers roll: Exploring the effect of the demolition of Goa's red-light area[END_REF][START_REF] Shahmanesh | Suicidal Behaviour amongst Female Sex Workers in Goa, India; the silent epidemic[END_REF]

Data collection

Structured Administered Interviews

The questionnaire was developed, translated and piloted in four Indian languages (Hindi, Konkani, Kannada, and Telegu). It covered five domains: socio-demographic factors; SW and sexual risk factors; gender disadvantage (experience of violence, freedom to quit sex trade and financial autonomy); reproductive and mental health; and suicidal behaviour. [START_REF] Buve | Multicentre study on factors determining differences in rate of spread of HIV in sub-Saharan Africa: methods and prevalence of HIV infection[END_REF][START_REF] Patel | Why do women complain of vaginal discharge? A population survey of infectious and pyschosocial risk factors in a South Asian community[END_REF][START_REF] Cowan | The appropriateness of core group interventions using presumptive periodic treatment among rural Zimbabwean women who exchange sex for gifts or money[END_REF][START_REF] Kessler | Short screening scales to monitor population prevalence and trends in non-specific psychological distress[END_REF][START_REF] Wilson | HIV / AIDS Rapid assessment. Family Health International[END_REF] The reproductive health section explored details about pregnancy, therapeutic abortions (henceforth referred to as abortions), children, and contraceptive behaviour. During the formative stages of the study, abortion, sexual assault, and rape were identified as sensitive issues with a risk of desirability bias in responses. To reduce misclassification bias, a confidential voting method was introduced to collect responses to sensitive questions. [START_REF] Gregson | Informal confidential voting interview methods and temporal changes in reported sexual risk behaviour for HIV transmission in sub-Saharan Africa[END_REF] Trained female interviewers administered the questionnaire to the participants in a variety of settings such as hired rooms, drop-in centers, project vehicle and clinics. Interviews lasted for approximately 60 minutes. The first and last authors closely supervised and trained the interviewers, and checked all the questionnaire responses for consistency. Data was double entered into Microsoft Access database (Microsoft, Redmond, Washington, USA) and underwent range and consistency check.

Laboratory Data

Participants self-sampled for vaginal specimen using a Dacron swab. One vaginal swab was inserted into a sterile universal container for testing of Chlamydia trachomatis and Neisseria gonorrhoea by polymerase chain reaction (Amplicor, Roche Molecular Systems, Alameda, California, USA) and the other was inserted into an InPouch TV culture kit (Biomed Diagnostic, San Jose, CA, USA) to screen for Trichomonas vaginalis by culture (InPouch, Biomed Diagnostics, San Jose, California, USA). Five dried blood spots were taken onto filter paper and tested for HIV antibody according to the WHO protocol and herpes simplex virus 2 (HSV-2) using ELISA test HerpeSelect (Focus Technologies, Cypress, California, USA). A further blood spot was placed into the testing well in the syphicheck (Qualpro, Diagnostics, Goa, India) rapid treponema specific test. The reagent was added and the rapid test was read after 5 minutes. This test is an indicator of lifetime exposure to syphilis. Details of the laboratory tests have been described in detail elsewhere. [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF] The laboratory had external quality control.

Ethical considerations

Ethical approval was obtained from the Independent Ethics Commission, Mumbai and University College London's Ethics Committee. A community advisory board comprising of FSWs, members close to the sex worker community, gharwallis (brothel keepers), local NGO members and peer educators was set up. All the study participants and their partners were treated presumptively for bacterial STIs and provided treatment based on laboratory tests results. HIV results were anonymous. However, voluntary counseling and testing was made available to all participants.

Analysis

Weights for analysis, the inverse of approximate selection probabilities, were calculated to reduce potential recruitment bias due to social network size, age, ethnicity using RDS Analysis Tool 5.4.0 (Cornell University, USA). Analyses incorporating these weights were performed using Stata 10 (STATA Corp. Tx USA), through survey analysis functions.

Contraceptive use was defined as the use of any modern method of contraception. FSWs who reported using only condoms for contraception, but did not use or were inconsistent (used sometimes) in condom use with their clients and intimate non-paying male partner (henceforth referred to as intimate partner) were considered non-users of contraception.

Women not using contraception because of pregnancy or plans to have a child, were menopausal or had undergone hysterectomy were excluded from analysis of factors associated with contraception use.

We conducted chi-square tests to explore the association between contraceptive use (no contraception, female sterilisation (referred to henceforth as sterilisation) or reversible contraception) and 'a priori factor' [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] (Figure 1: 1st column) and SW-related factor (Figure 1: 2 nd column). We treated variables 'intimate partner' and 'financial autonomy' as SW-related factors because FSWs' financial autonomy could be affected due to their family or pimps/brothel-owners. During the formative stages of our study, key informants reported that the FSWs' intimate partner is likely to be involved with sex trade, offering FSWs protection from their clients or pimps. We then calculated the odds ratios (OR) separately for sterilisation and reversible contraception relative to no contraception.

Multiple adjusted logistic regression models were built based on the conceptual framework (Figure 1). Due to small numbers of FSWs using reversible contraceptives, these analyses were performed only for the outcome variable 'sterilisation relative to no contraception'. The a priori factors that were significant with p<0.2 in univariate analysis were included in a logistic regression model selection procedure using stepwise backward selection procedure to form our base model. The interaction terms between age and marital status, and age and number of children were explored but were not significant and were dropped from the model.

Next, the association of the outcome variable with each SW-related factor (Figure 1: 2 nd column) was assessed after adjusting for the base model. Finally, all the SW-related factors were included in logistic regression model selection procedure using stepwise backward selection procedure (p<0.2), including the base model with certainty.

Results

A total of 326 FSWs from 35 different networks identified during mapping were recruited throughout Goa. Of the 59 purposively selected seeds, 35 seeds recruited FSWs into the study.

Through our extensive mapping we became aware that we were unable to recruit FSWs from 4-5 networks because they did not self identify as sex workers. We recruited up to 6 waves, with recruitment networks comprising 2 to 30 participants. The study sample was representative of the key sites and types of SW identified during mapping.

Contraceptive Practices

Of the total 326 FSWs, we excluded 66 FSWs from the contraceptive use analysis because they were not in need of contraception: 59 were planning pregnancy, 4 were pregnant, 1 had undergone hysterectomy and 2 were menopausal. Compared to the other FSWs they were more likely to be young (61.5% vs 31.4%, p<0.001), without children (45.5% vs 12.4%, p<0.001), literate (31% vs 19.5%, p=0.05) and originally from Andhra Pradesh, a neighbouring state of Goa (21% vs 7.3%, p=0.006). The socio-demographic characteristics and reproductive health indicators of the FSWs are presented in Table 1, separately for entire sample, and those included in the analyses of contraceptive use. The majority of the FSWs were aged between 20-35 years, illiterate, married, and had experienced pregnancy. Twentyfive percent of the entire sample had experienced abortion. Consistent condom use with clients was reported by 74% FSWs.

Among the 260 FSWs, 39% did not use any form of contraception. Half the FSWs had undergone sterilisation; use of reversible contraceptives was low (8%) (Table 1). The majority (73%) of FSWs had an intimate non-paying male partner and 67% of these never used condoms with intimate non-paying male partner. Although 26% FSWs reported using condoms 'for contraception', only a small proportion of FSWs (5%) 'always' used condoms with their intimate non-paying male partner and clients and were considered users of condoms for contraception.

Older age, illiteracy, being married, and having children were univariately associated with sterilisation (relative to no contraception) (Table 2). There was weak evidence that reversible contraceptives use was greater in non-Kannada FSW and those with non-SW-related income (Table 2). Several SW-related factors were univariately associated with sterilisation (Table 3).

The only SW-related factor associated with reversible contraception use (relative to no contraception) was having an intimate partner.

Factors associated with sterilisation relative to no contraception

Age, literacy, marital status, number of children, and debt formed our base model (Table 2).

After adjusting for the base model, sterilisation was associated, both singly (Table 3) and together (Table 4), with SW-related factors like having an intimate partner and financial autonomy. Exposure to HIV prevention interventions was reported by 34% FSWs and was not significantly associated with sterilisation, OR adjusting for base model [1.4 (95% CI 0.7-2.9)].

Perceptions towards Contraception and Condom Use

When asked about condom use with clients for HIV prevention, 102 (39%) women responded that there was no need to use condoms for HIV prevention if using other contraceptives. Among these women, 15% FSWs who were not using condoms with their clients had undergone sterilisation.

HIV, STI and pregnancy

The HIV prevalence among 259 women was 24% (95%CI: 18-29) [Table 1]. 45% of these HIV positive women were not using contraception, 42% had undergone sterilisation and 13% were using reversible contraceptives. The prevalence of other STI was high: 29% (95%CI:22-35) tested positive for lifetime syphilis and 54% (95%CI:48-60%) for HSV-2 antibodies, 8% (95%CI: 4-11) had Chlamydia trachomatis, 10% (95%CI:6-14) had Neiserria gonorrhoea, 8% (95%CI: 5-12) had Trichomonas vaginalis,.

The prevalence of HIV and other STI among the 59 FSWs who were planning to have a child was also high: 33% were HIV positive, 6% had Chlamydia trachomatis, 4% had Neiserria gonorrhoea, 7% had Trichomonas vaginalis, 25% tested positive for lifetime syphilis and 65% tested positive for HSV-2 antibodies.

Discussion

To our knowledge, this is the first study in India that examines contraceptive practices among HIV positive and negative FSWs, and factors associated with contraceptive use among FSWs.

HIV prevalence was high and contraceptive use among young FSWs (under 25 years) and HIV positive FSWs was low (36% and 55% respectively). Similar to another FSWs study, 9-10 lifetime abortions were high. Half of the FSWs had undergone sterilisation and few used reversible contraceptives. Sterilisation was independently associated with older age, illiteracy, having an intimate partner or children and financial autonomy. Several FSWs planning pregnancy were HIV positive or had other STI indicating the need for integrating HIV treatment and care programs with HIV prevention interventions among FSWs.

The contraceptive use among FSWs was higher (61%) in comparison to the general population women in Goa (48.2%) and India (56.3%). [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] Sterilisation use among FSWs at 50% was much higher than that reported in Goa (25.8%) and India (37.3%). The greater use of contraception in our study may be due to the higher rate of sterilisation reported among the Kannada FSWs (i.e. women of Karnataka ethnic origin) who formed the majority of our sample. In India, the median age for women to undergo sterilisation is 25.5 years [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] and use of sterilisation is particularly high (65%) in the State of Karnataka. [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] The use of reversible contraceptives among FSWs was low, perhaps explaining the high rate of abortions. Sterilisation was largely associated with socio-demographic factors, although association was seen with SW-related factors like having intimate partner and financial autonomy.

Sterilisation was associated with similar socio-demographic factors as among general population women i.e. older age, children and illiteracy. Similar to the situation in Goa, our study showed an inverse relationship between literacy and contraceptive use because illiterate women are more likely to undergo sterilisation. [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] Having an intimate partner was associated with sterilisation. Our study has also shown that FSWs with an intimate partner were less likely to have HIV [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF] suggesting that having an intimate partner could be a marker of social support for some FSWs. The association of financial autonomy and sterilisation indicates that FSWs' ability to make reproductive health choices is associated with their freedom to spend money as they wish. Lifetime exposure to HIV prevention interventions was not associated with sterilisation. However, exposure to HIV prevention was associated with increased knowledge of HIV and condom use with clients in our study population. [START_REF] Shahmanesh | The burden and determinants of HIV and sexually transmitted infections in a population-based sample of female sex workers in Goa, India[END_REF] Peer-led FSWs interventions and community mobilization [START_REF] Beattie | Declines in risk behaviour and sexually transmitted infection prevalence following a community-led HIV preventive intervention among female sex workers in Mysore, India[END_REF][START_REF] Jana | The Sonagachi Project: a sustainable community intervention program[END_REF][START_REF] Halli | The role of collectives in STI and HIV/AIDS prevention among female sex workers in Karnataka, India[END_REF] increases FSWs access and utilization of health services. [START_REF] Beattie | Declines in risk behaviour and sexually transmitted infection prevalence following a community-led HIV preventive intervention among female sex workers in Mysore, India[END_REF][START_REF] Halli | The role of collectives in STI and HIV/AIDS prevention among female sex workers in Karnataka, India[END_REF] Promoting contraceptives through peer-led interventions could improve contraceptive use among FSWs. The involvement of pimps and brothel keepers improves condom use among FSWs. [START_REF] Jana | The Sonagachi Project: a sustainable community intervention program[END_REF] Efforts should be made to involve these gatekeepers to promote contraception among FSWs and its effects must be explored.

Similar to the NFHS data, [START_REF]National Fact sheet: National Family Health Survey (NFHS-3) 2005-2006[END_REF] condoms were the most used reversible contraceptive among the FSWs. HIV prevention interventions should promote condoms for dual protection from HIV and abortions, especially among young FSWs. However, our qualitative work indicates that FSWs do not use condoms with their intimate partner to differentiate their relationship from clients. Among women at-risk of HIV, the World Health Organisation recommends promotion of hormonal contraceptives (HC) but emphasizes the need to advice consistent condom use to prevent HIV/STI. [START_REF] Mohllajeea | Hormonal contraceptive use and risk of sexually transmitted infections:a systematic review[END_REF] HIV prevention interventions should collaborate with the RCHP to improve FSWs' access to contraceptives, particularly HC. Condom use for HIV prevention should be emphasised among FSWs who have undergone sterilisation.

Peer-led HIV prevention interventions should encourage HIV positive FSWs planning

pregnancy to seek treatment and care to prevent neo-natal HIV transmission. They should encourage pregnant FSWs to screen for HIV and syphilis in the antenatal period. Same-day screening and treatment for syphilis with single dose of benzathine penicillin is clinically and cost-effective. [START_REF] Watson-Jones | Antenatal syphilis screening in sub-Saharan Africa: lessons learned from Tanzania[END_REF] Besides, FSWs undergoing abortions, IUD insertion, planning pregnancy should be given presumptive antibiotics prophylaxis for treatable STI.

A potential limitation of our study is that it was conducted in the aftermath of demolition of the red-light area. However, we conducted extensive mapping post-demolition to identify the re-organisation of SW. We used chain sampling to recruit participants and used weights to give an approximately unbiased analysis. We were able to recruit FSWs engaged in different types of SW and from different areas identified during mapping. However, this is not a true probability sample survey and the confidence intervals and p-values derived through RDS should be viewed as approximate. We were unable to thoroughly explore if any factors were associated with the use of reversible contraceptives due to their low use in this setting. The lack of significant associations with reversible contraceptive use might simply reflect low power. Associations with sterilisation should not be interpreted as its predictors as our study is cross-sectional thus making it difficult to determine the direction of effect.

India has a large group of women who engage in sex work. [START_REF] Chandrasekaran | Containing HIV/AIDS in India: the unfinished agenda[END_REF] The HIV prevention

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The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd to permit this article (if accepted) to be published in the journal Sexually Transmitted Infections and any other BMJPGL products and sublicences such use and exploit all subsidiary rights, as set out in your licence.

TotalFunding:

  The study and salary of Maryam Shahmanesh and Sonali Wayal were funded through a Wellcome Trust clinical training fellowship grant received by Maryam Shahmanesh.

  interventions should promote contraceptives, especially among HIV positive and young FSWs. The integration of NACO's HIV prevention interventions with its PPTCT program can avert HIV positive births. The impact of integration of the RCHP and PPTCT programs with HIV prevention interventions could enhance their public health impact and should be evaluated for feasibility, and effectiveness in reducing reproductive ill-health and HIV positive births among FSWs. 8. Mullick S, Watson-Jones D, Beksinska M and Mabey D. Sexually transmitted infections in pregnancy: prevalence, impact on pregnancy outcomes, and approach to treatment in developing countries. Sex Transm Infect 2005; 81: 294-302. 9. Chakraborthy AK, Jana S, Das A. Khodakevich L, Chakraborthy MS and Pal D. Community based survey of STD/HIV infection among commercial sex workers in Calcutta (India). Part I. Some social features of commercial sex workers. J Commun Dis 1994; 26:161-67. 10 . Pal D, Raut D and Das A. A study of HIV/STD infections amongst commercial sex workers in Kolkata (India). Part-I: some socio-demographic features of commercial sex workers. J Commun Dis 2003; 35:90-95. 11. Wijgert J, and Elias C. Defining reproductive tract infections and other gynaecological morbidities. In: Investigating reproductive tract infections and other gynaecological disorders: a multidisciplinary research approach. Jejeebhoy S, Koenig M, and Elias C (editors). Cambridge University Press; 2003.pp.11-27. 12. UNICEF. Children and AIDS Programme Update 2007. New Delhi. 13. Reynolds HM, Steiner MJ and Cates Jr W. Contraception's proved potential to fight HIV. Sex Transm Infect 2005; 81: 184-5. 14. Reynolds HW, Janowitz B, Wilcher R, Cates W. Contraception to prevent HIV-positive births: current contribution and potential cost savings in PEPFAR countries. Sex Transm Infect 2008; 84(Suppl_2): ii49-53. 15. Collumbien M, Gerressu M and J Cleland. Non-use and use of ineffective methods of contraception. In: Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Ezzati M, Lopez AD, Rodgers A, Murray CJL (Editorss). Geneva: WHO; 2004.pp.1255-1320.

Figure 1 .

 1 Figure 1. Framework of the Factors Associated with Contraceptive Use Among Female Sex Workers Socio-demographic factors known to be associated with contraceptive use Demographic factors Age Marital Status Number of children

Table 1 . Demographic and reproductive health indicators among the female sex workers in a cross- sectional study conducted in Goa, India ‡

 1 N is 326 when entire sample is included in the analysis and N is 260 when women not using contraceptives for reasons like want to have a child, hysterectomy, menopausal or pregnant are excluded from the analysis and are the N's for the above-mentioned columns unless specified otherwise. † Only for those women who reported having a regular male partner.

		Entire Sample	Excluding pregnant women, those trying for
	Factor	Number (n=326) ‡	Weighted % [95% CI]	pregnancy, menopausal or had hysterectomy Number (n=260) ‡ Weighted N % [95% CI]
	Age
	</=25	135	38 [32-43]	93	32 [26-37]
	26-35	122	39 [34-45]	108	44 [37-50]
	>/=36	69	23 [18-28]	59	25 [19-30]
	Ethnicity
	Kannada (Karnataka)	200	72 [67-76]	163	73 [68-78]
	Telegu (Andhra Pradesh)	37	10 [7-13]	21	7 [4-10]
	Goa	54	11 [8-14]	47	12 [8-16]
	Other	35	7 [5-10]	29	7 [4-10]
	Religion
	Hindus	225	73 [68-78]	175	71 [66-77]
	Christians	41	10 [7-13]	35	11 [7-14]
	Muslims	60	17 [13-21]	50	18 [13-23]
	Literacy
	Literate	87	22 [18-26]	63	19 [15-24]
	Illiterate	239	78 [74-82]	197	81 [76-85]
	Number of Children
	None	72	19 [15-23]	43	12 [8-16]
	1-3	229	73 [68-78]	192	78 [73-83]
	>3	25	8 [5-11]	25	9 [6-14]
	Marital Status
	Previously married	101	32 [26-37]	88	35 [29-41]
	Married	134	40 [35-46]	102	38 [32-44]
	Never Married	91	28 [23-33]	70	27 [21-32]
	Intimate non-paying male
	partner
	Yes	247	76 [71-81]	189	73 [67-78]
	Ever pregnant
	Yes	298	91 [88-95]	240	94 [92-97]
	Lifetime abortion (n=326)
	Yes	90	25 [20-30]	72	26 [20-31]
	Contraceptive Use
	None	103	29 [24-34]	103	39 [34-46 ]
	Oral Pills	9	2.0 [0.3-3.0]	9	2.1 [0.3-3.8]
	Intrauterine Device	3	0.6 [.09-1.3]	3	1.1 [0.1-2.4]
	Condom	14	4.0 [2-6.5]	14	5.3 [2.6-8.0]
	Female Sterilisation	131	43.0 [37-49]	131	50 [44-57]
	Pregnant/planning child	66	20 (16-25)	-	-
	Condom use with intimate	(n=247) †		(n=189) †
	non-paying male partner
	Never	181	74 [68-79]	126	67 [60-74]
	Always	27	11 [6-15]	27	14 [9-19]
	Sometimes	39	16 [11-20]	36	19 [13-25]
	HIV positive	(n=325)		(n=259)
		77	26 [21-31]	55	24 [18-29]
	Consistent condom use with
	clients
		237	74.4 [69.3-78.9]	192	73.8 [68.2-77.8]

Table 2 : Association of type of contraception used with socio-demographic, reproductive and sexual health indicators, among female sex workers in Goa India, reporting unadjusted estimates of association (OR=odds ratios) for each of sterilisation and reversible methods versus no contraception, and for sterilisation, multivariate (adjusted) estimates derived from Base Model (n=260)

 2 Percentages presented in the table 2 are weighted row percentages † Oral pills, Intra-uterine device, condom, were categorized as reversible contraceptives. ‡ Univariate analysis of a priori factors with type of contraceptive used.

	Factor	N	*No	*Sterilisation	* † Reversible	‡ Unadjusted OR	‡ Unadjusted OR	§ Adjusted OR
			Contraception	Contraceptives	Contraceptives	(95% CI)	(95% CI)	(95% C I
			%	%	%	( † Reversible vs.	(Sterilisation vs.	(Sterilisation vs.
						No	No	No Contraception)
						Contraception)	Contraception)	N=234
						N=129	N=234
	Age (years)			p<0.001		p=0.4	p<0.001	p<0.001
	</=25	93	64	24	12	1	1	1
	26-35	108	26	64	10	1.9 (0.7-5.0)	5.8 (2.9-11.5)	4.6 (2.1-10.0)
	>/=36	59	27	68	5	0.9 (0.2-3.8)	5.9 (2. 7-12.9)	3.9 (1.6-9.6)
	Ethnicity			p<0.001		p=0.13	p=0.002	-
	Kannada	163	32	63	5	1	1	-
	(Karnataka)
	Telegu	21	33	38	29	4.6 (1.2-17.4)	0.7 (0.2-1.9)	-
	(Andhra
	Pradesh)
	Goa	47	53	32	15	1.7 (0.6-5.3)	0.4 (0.2-0.8)	-
	Other	29	65	21	14	1.1 (0.3-3.9)	0.2 (0.07-0.5)	-
	Religion			p=0.31		p=0.6	p=0.1	-
	Hindus	175	38	51	11	1	1	-
	Christians	35	54	37	9	0.6 (0.1-2.2)	0.5 (0.2-1.1)	-
	Muslims	50	36	56	8	0.7 (0.2-2.3)	1.1 (0.6-2.3)	-
	Literacy			p<0.001		p=0.5	p<0.001	p=0.001
	Literate	63	67	20	13	1	1	1
	illiterate	197	31	60	9	1.4 (0.5-3.8)	5.2 (2.5-10.6)	4.1 (1.7-9.9)
	Marital			p=0.0006		p=0.7	p=0.0004	p=0.02
	Status
	Married/	190	32	59	9	1	1	1
	Previously
	Married
	Never	70	61	26	13	0.9 (0.3-2.3)	0.3 (0.2-0.6)	0.4 (0.2-0.9)
	Married
	Children			p<0.001		p=0.8	p<0.001	p=0. 004
	None	43	74	9	16	1	1	1
	1+	217	31	61	8	1.1 (0.4-3.1)	15.4 (4.5-52.9)	5.9 (1.7-19.6)
	Current			p=0.22		p=0.6	p=0.09	p=0.1
	debt
	No	121	46	43	11	1	1	1
	Yes	139	34	57	9	1.2 (0.5-3.1)	1.6 (0.9-2.7)	1.7 (0.9-3.3)
	Non-sex			p=0.24		p=0.1	p=0.1	-
	work
	related
	income
	No	114	45	48	7	1	1	-
	Yes	146	36	52	12	2.1 (0.8-5.6)	1.4 (0.8-2.3)	-

* § All socio-economic variables explored in univariate analysis were included in a stepwise backward model selection procedure (p<0.2 for inclusion with outcome variable coded as no contraception and sterilisation) and the selected model presented forms our base model.

Table 3 . Association of type of contraception used with sex work related factors (reporting unadjusted estimates of association (ORs) for each of sterilisation and reversible methods versus no contraception), and for sterilisation, estimates (ORs) adjusted for Base Model [N=260]

 3

	Factors	N	* No	* Sterilisation	†* Reversible	‡ Unadjusted OR	‡ Unadjusted OR	£ Adjusted OR
			Contraception	Contraceptives	Contraceptives	(95% CI)	(95% CI)	(95% CI)
			%	%	%	( † Reversible vs. No	(Sterilisation vs.	(Sterilisation
						Contraception)	No	vs. No
						N=129	contraception)	contraception )
							N=233	N=233
	Age at start			p=0.003		p=0.76	p=0.003	p=0.5
	of SW
	<=20	119	50	38	12	1	1	1
	>20	141	30	61	9	0.9 (0.4-2.2)	2.3 (1.3-4.0)	1.2 (0.6-2.5)
	Mode of SW			p=0.001		p=0.97	p=0.001	p=0.7
	Independent	233	36	54	10	1	1	1
	$ Contract	27	70	15	15	1.0 (0.3-3.6)	0.1 (0.04-0.5)	0.7 (0.1-4.5)
	Exposure to			p=0.8		p=0.8	p=0.6	p=0.3
	HIV
	prevention
	Intervention
	Yes	82	40	49	11	1	1	1
	No	177	39	51	10	0.9 (0.3-2.4)	1.1 (0.6-2.0)	1.4 (0.7-2.9)
	Intimate			p=0.002		p=0.003	p=0.002	p=0.003
	non-paying
	male
	partner
	No	71	56	41	3	1	1	1
	Yes	189	33	54	13	9.9 (2.2-46.0)	2.5 (1.3-4.6)	3.0 (1.4-6.3)
	Free to quit			p=0.05		p=0.4	p=0.02	p=0.04
	trade
	No	24	63	29	8	1	1	1
	Yes	236	37	53	10	1.9 (0.4-8.9)	3.1 (1.2-8.2)	3.8 (1.1-13.3)
	Financial			p=0.1		p=0.5	p=0.04	p=0.003
	autonomy
	No	86	46	44	10	1	1	1
	Yes	174	36	54	10	1.3 (0.5-3.5)	1.8 (1.0-3.2)	2.9 (1.4-5.8)
	Violence by			p=0.4		p=0.3	p=0.63	p=0.2
	intimate non-
	paying male
	partner
	No	175	40	51	9	1	1	1
	Yes	85	39	48	13	1.5 (0.5-3.8)	0.8 (0.5-1.5)	0.6 (0.3-1.3)
	Violence			p=0.01		p=0.89	p=0.32	p=0.2
	from others
	(pimps,
	brothel
	keepers)
	No	204	37	54	9	1	1	1
	Yes	56	48	36	16	0.95 (0.46-1.96)	0.73 (0.39-1.36)	0.6 (0.3-1.4)
	§ Health care			p=0.79		-	p=0.6	p=0.8
	accessed
	Government	76	42	49	9	-	1	1
	clinics
	Private clinics 176	38	51	11	-	1.3 (0.7-2.3)	1.9 (0.9-3.9)
	None	8	50	50	0	-	1.5 (0.3-6.9)	4.5 (0.4-47.5)

Table 4 . Final composite model of the association of sterilisation vs. no contraception with socio-demographic base model factors and sex work related factors [N=233]

 4 Adjusted odds ratio for the final model which includes the base model and variables related to sex work selected in a stepwise backward model selection procedure, with p<0.2 for inclusion.

	Factors	Final Model
		‡ Adjusted
		OR (95% CI)
	AGE	p=0.001
	</=25	1
	26-35	4.4 (1.9-10.1)
	> 35	8.0 (2.8-23.0)
	Literacy	p=0.001
	Illiterate	4.6 (1.9-11.4)
	Marital Status	p=0.1
	Never married	0.5 (0.2-1.3)
	Number of Children	p=0.001
	None	1
	1+	9.9 (2.6-37.4)
	FSWs who were in debt	p=0.05
	currently
	Yes	1.9 (0.97-3.96)
	Intimate non-paying	p=0.01
	male partner
	Yes	2.6 (1.2-5.7)
	Violence by Intimate	p=0.1
	non-paying male
	partner
	Yes	0.5 (0.2-1.2)
	Free to quit trade	p=0.1
	Yes	2.8 (0.8-9.4)
	Financial autonomy	p=0.009
	Yes	2.8 (1.3-6.1)
	‡

Acknowledgements

The authors are grateful to the Wellcome Trust for supporting this work through a fellowship grant given to Dr Maryam Shahmanesh. The authors would like to thank Anil Pandey and Beethoven Fonesca for administrative support to the research project and Sushila Mendoza for cleaning the data; also the board members and staff of Positive People for supporting them in their work and in particular the research team for all their tireless work under gruelling conditions. They also wish to thank the laboratory staff in Sangath for processing the samples and rapidly adapting to the changing requirements of field-based sampling; the sex workers of Baina and Goa for receiving them with open arms despite the harsh circumstances, for participating in this study, for implementing the findings and providing constant and stimulating critical feedback through the community advisory board and peer educators; Beryl West for training the laboratory staff and setting up the standard operating procedures and quality control monitoring and Rosanna Peeling for initial PCR training; Dr Risbud of the National AIDS Research Institute, Pune, for quality control of the samples; Bharat Parekh for advice on the BED assay for HIV; and Douglas Heckathorne and Cyprain Wejnert for guidance with RDS analysis. We would also like to thank the peer reviewers of the STI journal for their feedback.

Contributors

SW participated in the implementation of the study, data collection, had full access to all the data for the study, interpretation of the data, wrote the manuscript drafts, and took the final responsibility for the decision to submit for publication. FC participated in the design of the study, interpretation of the data and critical appraisal of all the drafts of the manuscripts. AC guided and PW supported the statistical analysis and were involved in the critical appraisal of all the drafts of the manuscripts. DM participated in the design of the study, interpretation of the data and critical appraisal of all the drafts of the manuscripts. MS designed and implemented the study, was involved in interpretation of the data, and the critical appraisal of the drafts of the manuscripts. The design and implementation of the study was independent of the funding body and the findings do not reflect the opinions of Wellcome Trust.


Ke Wu

Carole Garnier
email: carole.garnier@univ-rennes1.fr

Fellow, IEEE Jean-Louis Coatrieux
email: jean-louis.coatrieux@univ-rennes1.fr

Huazhong Shu

A preliminary study of moment-based texture analysis for medical images

A moment-based approach is proposed for texture analysis of medical images. The neighborhood of texture pixel is calculated by moments for texture feature extraction. After verified on Brodatz textures, the moment-based texture analysis method is applied on CT liver scan classification and prostate ultrasound segmentation. A support vector machine and a multi-channel active contour model are used in our application. The result shows that our method works well but still have some limitations.

I. INTRODUCTION

N the past decades, the major advance of medical imaging such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound imaging techniques provides great assistant for medical diagnosis. The medical image analysis and processing became an active research field. Since many medical images do not exhibit regions of uniform and smooth intensities but homogeneous structures and patterns, texture plays an important role in medical image processing. Texture analysis presents the possibility of automatic image processing and quantification of the pathology information which is detected visually by the radiologist. It also increases in the amount of information taken from the image by adding the information that cannot be perceived visually by the observer.

The application of a texture analysis method generally consists in extracting a set of parameters to characterize texture, each texture parameter expresses a special property of the texture such as coarseness, homogeneity, or the local contrast. The texture feature extraction is carried out either at the level of each pixel by calculating the textural parameters on very small neighborhood, or at the level of Region of Interest (ROI) defined by the user which corresponds in practice to a large number of pixels. Many texture feature extraction methods have been reported in the literature. Among them, the statistical measurement methods calculate simple statistical properties (e.g. first order parameters) or sophisticated properties (e.g. co-occurrence matrices [START_REF] Peckinpaugh | An improved method for computing gray-level Cooccurrence Matrix based texture measures[END_REF] or gray level run length [START_REF] Dasarathy | Image characterizations based on joint gray level-run length distribution[END_REF]) of image. Other texture feature extraction approaches are based on models (Markova random field and fractals [START_REF] Mandelbort | The fractal geometry of natural[END_REF]), mathematical morphology [START_REF] Serra | (%) Set1 Set2 Set1 Set2 Set1 Set2 Legendre -5[END_REF] or classical transform methods (Fourier, Gabor, wavelet transform [START_REF] Mallat | A theory for mulitresolution signal decomposition: the wavelet representation[END_REF]). These texture analysis methods have been applied to image segmentation and disease detection.

In this paper, we propose a moment-based approach for medical texture analysis. Moments are statistical measures used to obtain the relevant information of an object. Since Hu introduced moments in image analysis [START_REF] Hu | Visual pattern recognize by moment invariants[END_REF], moment functions are widely applied in many scientific areas. In 1994, Tuceryan discussed texture feature extraction and texture analysis based on geometric moment [START_REF] Tuceryan | Moment based texture segmentation[END_REF]. Begum applied the complex moments to texture segmentation [START_REF] Bigun | N-folded symmetrics by complex moments in Gabor space and their application to unsupervised texture segmentation[END_REF]. However, both geometric moments and complex moments contain redundant information and are sensitive to noise. This is due to the fact that the kernel polynomials are not orthogonal. Teague suggested the use of orthogonal moments such as Legendre moment and Zernike moment [START_REF] Teague | Image analysis via the general theory of moments[END_REF]. Mukundan introduced the discrete Tchebichef moments [START_REF] Steve | Support Vector Machines for Classification and Regression[END_REF]. Yap presented another set of discrete moments known as Krawtchouk moments [START_REF] Duda | Texture-Based Classification of Hepatic Primary Tumors in Multiphase CT[END_REF]. The medical image texture analysis based on the orthogonal moments is discussed in this paper.

The organization of the paper is as follows. In section 2, we review the definition of moments. Then we give the details of moment-based texture feature extraction. In section 3, the proposed method is applied to texture analysis from Brodatz database. And a classifier is proposed to evaluate feature characterization. The characterization of moments is evaluated. Then we apply the method to real medical image. The discussion is provided in section 4.

II. METHOD

A. Definition of moments

Legendre moment

The (n+m)th order of Legendre moment, L nm , is defined as [START_REF] Teague | Image analysis via the general theory of moments[END_REF] 1 1

1 1 (2 1)(2 1) ( ) ( ) ( , ) 4 nm n m n m L P x P y f x y dxdy        , (1)

I

where P n (x) is the nth order of Legendre polynomial given by

/ 2 2 0 1 ( 2 2) ! ( ) ( 1) !( )!( 2 )! 2 n k n n n k n k P x x k n k n k         k . ( 2
)

Zernike moment

The 2D Zernike moments, Z nm , of order n with repetition m, are defined in polar coordinates (r, q) inside the unit circle as [START_REF] Teague | Image analysis via the general theory of moments[END_REF] 1 2

0 0 1 ( ) ( , ) jm nm nm n Z R r e f r rdrd           0 |m|  n, n -|m| is even, (3)
 where R nm (r) is the nth order of Zernike radial polynomial given by

( ) / 2 2 0 ( )! ( ) ( 1) ! ( 2 ) / 2 ! ( 2 ) / 2 ! n m k nm k n k R r r k n k m n k m                  n k  (4)

Tchebichef moments

For a digital image f(x, y) with size NN, the (n+m)th order Tchebichef moments are defined as [START_REF] Serra | (%) Set1 Set2 Set1 Set2 Set1 Set2 Legendre -5[END_REF] 1 1

0 0 1 ( ) ( ) ( , ) ( , ) ( , ) N N nm n m x y T tx n N m N             ty f x y , (5)
where the scaled Tchebichef polynomials { ( are given by

)} n t x  ( ) ( ) ( , ) n n t x t x n N    (6) 3 2 ( ) (1 ) ( , ,1 ;1,1 ;1)
n n t x N F n x n N       (7) 2
( , ) ( , ) ( , ) n N n N n N      , ( 8
) ( , ) (2 )! 2 1 N n n N n n          
Here, (n, N) is a suitable constant which is independent of

x and 3 2 is the hypergeometric function defined as ( )

F  1 2 3 3 2 1 2 3 1 2 0 1 2 ( ) ( ) ( ) ( , , ; , ; ) ( ) ( ) ! k k k k k k k a a a z F a a a b b z b b k     (9)
with (a) k the Pochhammer symbol given by (

) ( ) ( 1) ( 1) ( ) k a k a a a a k a          ( 10
)

Krawtchouk moment

The Krawtchouk moment of order (n+m) of an image f(x, y) with size NN is defined as [START_REF] Sandberg | A Level-Set and Gabor-based Active Contour Algorithm for Segmenting Textured Images[END_REF] 1 1

1 2 0 0 ( ; , 1) ( ; , 1) ( , ) N N nm n m x y Q K xp N K yp N f           xy (11)
where the set of weighted Krawtchouk polynomials { ( ; , )

n K x p N  } is defined by ( ; , ) ( ; , ) ( ; , ) ( ; , ) n n w x p N K x p N K x p N n p N    (12) 2 1 ( ; , ) ( , ; ;1/ ) n K x p N F n x N p     , 0  x, n  N -1. ( 13
) ( ; , ) (1 )
x

N x N w x p N p p x          , 0 < p < 1, (14) 1
( ; , ) ( 1) ( ) n n n p n n p N p N            ! ( 15
)
Here, 2 1 ( ) F  is the hypergeometric function defined as Because Legendre and Zernike moments are defined on [-1,1], the image must be transformed to this range. However, such a process is not required for Tchebichef moments and Krawtchouk moments.

2 1 0 ( ) ( ) ( , ; ; ) ( ) ! k k k k k a b z F a b c z c k     (
The moments of order up to four are respectively calculated by ( 1), (3) ( 5) and ( 11) to get texture parameters. For each moment, texture features consist of 15 parameters for texture characterization.

The first step in applying any classification is preparation of the learning set, which will be used to generate the classifier. The learning set is created from some samples which are labeled by our priori knowledge. When the classifier is built it can be applied in prediction of new samples. Figure 1 presents the schematic process of classifier induction based on previously described samples. For a sample image, the ROIs from texture 1 and texture 2 are labeled before classification. Then the sets of texture parameters that will create feature vectors of a learning set are calculated for texture characterization of each ROI. Finally, the classifier is induced from the learning set composed of the labeled feature vectors. After its generation, the classifier can be used in recognition of unknown texture regions. For a given texture image, a ROI is traced but now the class of the concerned ROI is not known. The same set of features as it was utilized during the process of classifier induction is then calculated for this image region. Applying a set of previously generated classifiers allows us to make a decision and associate one of the classes with the analyzed ROI.

In our work, support vector machine (SVM) is chosen as classifier for texture characterization. The purpose of SVM is to find an optimal separating hyper plane by maximizing the margin between the separating hyper plane and the features. More details of SVM can be found in [START_REF] Duda | Texture Characterization for Hepatic Tumor Recognition in Multiphase CT[END_REF]. Features of class labeled texture ROIs are entered to SVM for training. After that, the features of unknown textures can be characterized

III. EXPERIMENTAL RESULTS

A. Moment-based texture analysis for Brodatz texture

Before considering medical image data, the proposed moment-based algorithm has been tested on the Brodatz database. Nine 256×256 images with very different patterns have been selected: D1, D11, D84, D57, D112, D8, D15, D4, and D9. They were analyzed by pairs. These images were decomposed into non-overlapping windows (with size going from 5 to 15) onto which the 15 moment coefficients were computed. 800 samples have been randomly selected to build the SVM training set, 2000 of the remaining ones being used for estimating the performance of the method. The results show that the classification rates, for the whole moments described above, are higher than 95% in all cases.

In order to prove the efficiency of moments for texture characterization, we needed image data sets for which a reference is available in the literature. We selected a work published in [START_REF] Duda | Texture-Based Classification of Hepatic Primary Tumors in Multiphase CT[END_REF] and more detailed in [START_REF] Duda | Texture Characterization for Hepatic Tumor Recognition in Multiphase CT[END_REF] to carry out this study: it concerns CT images of the liver at different contrast medium injection phases (i.e. multiphase CT). We also completed this analysis by adding a second example on the segmentation of ultrasound images of the prostate.

B. Moment-based texture analysis on liver CT images

The approach reported in [START_REF] Duda | Texture Characterization for Hepatic Tumor Recognition in Multiphase CT[END_REF] was based on various sets of texture parameters such as Run-Length Matrix, Co-occurrence and Laws' filters. We applied our moment-based approach by following the same experimental conditions. For each tissue type (normal liver, hepato-carcinoma and cholangiocarcinoma confirmed by a histopathological analysis) and each acquisition phase (non-enhanced images and, after contrast product injection, in arterial and portal phases), 150 ROIs from 13 different patients have been chosen. The four above moments were used as previously for texture feature extraction. The window size is chosen as 21. Four experiments have been conducted for each of the aforementioned sets of features. In the first three ones, feature vectors corresponding to three acquisition phases (N, A, P) were analyzed separately. In the last one, "multiphase" feature vectors (created from three regions of interest corresponding to the three images in subsequent acquisition instants) were analyzed simultaneously. The texture features were classified by Support Vector Machine (SVM). The learning sets used for classifier induction were composed of 75 observations corresponding to each acquisition phase (225 observations in total). Test sets of equal number of feature vectors were used for the validation. The training ROIs and testing ROIs are from different patients. The results are summarized in TABLE I. They show that the classification performance is rather high. The classification rate achieved by using moments is higher than the best result reported in [START_REF] Duda | Texture Characterization for Hepatic Tumor Recognition in Multiphase CT[END_REF]. It also confirms not only the capability to discriminate between normal and pathological tissues but also the interest to use multiphase CT.

C. Moment-based texture analysis for ultrasound image

The prostate images have been acquired on the Ablatherm High Intensity Focused Ultrasound (HIFU) device. The pixel is 0.154×0.15 mm 2 and the size of the images size corresponds to 500×490 pixels. Two images selected among different patients and different plane positions have been used and the window size was set to 15.

The results displayed in Figure 2 have been obtained by using a multi-channel active contour model for segmentation [START_REF] Sandberg | A Level-Set and Gabor-based Active Contour Algorithm for Segmenting Textured Images[END_REF]. An initial contour is given by interpolating four edge points of the prostate defined by a radiologist. Then, the moments are used for texture extraction. All these features are entered into the model without edge information. A level-set method is then applied to compute the contour evolution. A detailed description of this method can be found in [START_REF] Chan | Active Contours without Edges[END_REF].

Three standard area-based metrics have been defined based on the true positive (TP) area (the common region between the manual outline and the algorithm outline), the false positive (FP) area (the region enclosed by the algorithm outline but outside of the manual outline) and the false negative (FN) area (the region enclosed by the manual outline that is missed by the algorithm).

They are fractional area difference (FAD): /

d d P A A  , ( 17

IV. DISCUSSION

Our objective in this paper was to show that moment-based texture characterization could bring some positive cues to tissue discrimination. The first example confirms that moments can offer high performance and compare well with previous attempts. The second example, however, deals with a much more complex problem. The ultrasound images are highly non-stationary, inside and outside the prostate, and the boundaries at the extremities are badly defined. In those areas, the moment features do not provide enough relevant information to obtain a precise prostate segmentation. We performed other studies not reported here on MRI data sets [START_REF] Mahmoud-Ghoneim | The impact of image dynamic range on texture classification of brain white matter[END_REF] for which no improvement was observed in comparison to classical methods. As mentioned above, a few important points have to be considered and in particular the window size and the moment order. They have not been addressed here due to space limitations.

V. CONCLUSIONS

In this paper, a moment-based texture analysis method is introduced in medical images. Texture features are extracted by calculating moments in texture pixel neighborhood. Their capability to discriminate different textures has been verified on Brodatz textures. The method is applied to liver tumor recognition and prostate image segmentation. It has been shown that high performance can be achieved on multiphase CT images of the liver and ultrasound prostate images by using different families of moments. Although more experiments are required to assess their general interest in medical imaging, we think that they offer a sound alternative to the well established methods applied up to now.

  16) B. Texture feature extraction and classification In our work, texture features are extracted by computing the moments in neighborhood of each pixel. Small NN local windows around pixel are taken for computation. The size N of is important in feature extraction. As the window size increases, more global features are detected. So the choice of the window size should be tied to the contents of image.

Fig. 1 .

 1 Fig. 1. The overall process. (a) classifier training, (b) classifier application.

  Fig. 2. (a) Examples of original images (sets 1 and 2) with expert contours, (b) and (c) Contours of set 1 and set 2 respectively, obtained with Legendre, Zernike, Krawtchouk and Tchebichef moments

  m

ACKNOWLEDGMENT

The authors are indebted to J Bezy and to EDAP-TMS for providing the data sets used in this study.

Manuscript received This work was supported by the National Natural Science Foundation of China under Grant 60911130370 and the National Basic Research Program of China under Grant 2010CB732503. It has been realized thanks to the French-Chinese cooperation framework associating SouthEast University, Inserm and the University of Rennes 1.