I. Agency, . Research, and . Cancer, World Health Organization Estimated cancer Incidence, Mortality, Prevalence and Disability-adjusted life years (DALYs) Worldwide in 2012, 2014.

C. Stock and H. Brenner, Utilization of lower gastrointestinal endoscopy and fecal occult blood test in 11 European countries: evidence from the Survey of Health, Aging and Retirement in Europe Endoscopy, vol.42, issue.7, pp.546-556, 2010.

A. Sonnenberg, S. L. Amorosi, M. J. Lacey, and D. A. Lieberman, Patterns of endoscopy in the United States: analysis of data from the Centers for Medicare and Medicaid Services and the National Endoscopic Database, Gastrointestinal Endoscopy, vol.67, issue.3, pp.489-496, 2008.
DOI : 10.1016/j.gie.2007.08.041

N. Segnan, J. Patnick, and L. V. Karsa, European guidelines for quality assurance in colorectal cancer screening and diagnosis, 2010.

J. C. Van-rijn, J. B. Reitsma, P. M. Stoker, S. J. Van-deventer, and E. Dekker, Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review, The American Journal of Gastroenterology, vol.226, issue.2, pp.343-350, 2006.
DOI : 10.1053/j.gastro.2005.05.012

M. F. Kaminski, C. Hassan, R. Bisschops, J. Pohl, M. Pellise et al., Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline, Endoscopy, vol.46, issue.05, pp.435-449, 2014.
DOI : 10.1055/s-0034-1365348

P. In and P. Workshop, The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach and colon, Gastrointest. Endosc, vol.58, issue.6, pp.3-43, 2003.

S. Kudo, S. Tamura, T. Nakajima, H. Yamano, H. Kusaka et al., Diagnosis of colorectal tumorous lesions by magnifying endoscopy, Gastrointestinal Endoscopy, vol.44, issue.1, pp.8-14, 1996.
DOI : 10.1016/S0016-5107(96)70222-5

A. Parra-blanco, A. Jimenez, B. Rembacken, N. Gonzalez, D. Nicolas-perez et al., Validation of Fujinon intelligent chromoendoscopy with high definition endoscopes in colonoscopy, World Journal of Gastroenterology, vol.15, issue.42, pp.5266-5273, 2009.
DOI : 10.3748/wjg.15.5266

S. Kodashima and M. Fujishiro, Novel image-enhanced endoscopy with i-scan technology, World Journal of Gastroenterology, vol.16, issue.9, pp.1043-1049, 2010.
DOI : 10.3748/wjg.v16.i9.1043

R. Szeliski, Computer Vision: Algorithms and Applications, 2010.
DOI : 10.1007/978-1-84882-935-0

R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2003.
DOI : 10.1017/CBO9780511811685

P. F. Alcantarilla, A. Bartoli, F. Chadebecq, C. Tilmant, and V. Lepilliez, Enhanced imaging colonoscopy facilitates dense motion-based 3D reconstruction, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.7346-7349, 2013.
DOI : 10.1109/EMBC.2013.6611255

S. A. Gross and M. B. Wallace, Hold On Picasso, Narrow Band Imaging Is Here, The American Journal of Gastroenterology, vol.38, issue.12, pp.2717-2718, 2006.
DOI : 10.1117/1.1695563

R. Pittayanon and R. Rerknimitr, Role of digital chromoendoscopy and confocal laser endomicroscopy for gastric intestinal metaplasia and cancer surveillance, World Journal of Gastrointestinal Endoscopy, vol.4, issue.10, pp.472-478, 2012.
DOI : 10.4253/wjge.v4.i10.472

A. Hoffman, F. Sar, M. Goetz, A. Tresch, J. Mudter et al., -Scan is superior in the detection of colorectal neoplasias compared with standard video colonoscopy: a prospective randomized controlled trial, Endoscopy, vol.42, issue.10, pp.827-833, 2010.
DOI : 10.1055/s-0030-1255713
URL : https://hal.archives-ouvertes.fr/hal-01009792

G. Jass and . Triadafilopoulos, Pragmatic classification of superficial neoplastic colorectal lesions, Gastrointest. Endosc, vol.70, issue.6, pp.1182-1199, 2009.

S. Hasegawa, K. Mitsuyama, H. Kawano, K. Arita, Y. Maeyama et al., Endoscopic discrimination of sessile serrated adenomas from other serrated lesions, Oncol. Lett, vol.2, issue.5, pp.785-789, 2011.

A. B. Farris, J. Misdraji, A. Srivastava, A. Muzikansky, V. Deshpande et al., Sessile Serrated Adenoma, The American Journal of Surgical Pathology, vol.32, issue.1, pp.30-35, 2008.
DOI : 10.1097/PAS.0b013e318093e40a

J. Young, M. Jenkins, S. Parry, B. Young, D. Nancarrow et al., Serrated pathway colorectal cancer in the population: genetic consideration, Gut, vol.56, issue.10, pp.1453-1459, 2007.
DOI : 10.1136/gut.2007.126870

F. Bosman, WHO classification of tumours of the digestive system. International Agency for Research on Cancer Press, 2010.

E. E. Torlakovic, J. D. Gomez, D. K. Driman, J. R. Parfitt, C. Wang et al., Sessile Serrated Adenoma (SSA) vs. Traditional Serrated Adenoma (TSA), The American Journal of Surgical Pathology, vol.32, issue.1, pp.21-29, 2008.
DOI : 10.1097/PAS.0b013e318157f002

M. Liedlgruber and A. Uhl, Computer-Aided Decision Support Systems for Endoscopy in the Gastrointestinal Tract: A Review, IEEE Reviews in Biomedical Engineering, vol.4, pp.73-88, 2011.
DOI : 10.1109/RBME.2011.2175445

K. Takai, T. Kin, H. Oyama, A. Iijima, M. Shojima et al., The use of 3D computer graphics in the diagnosis and treatment of spinal vascular malformations, Journal of Neurosurgery: Spine, vol.15, issue.6, pp.654-659, 2011.
DOI : 10.3171/2011.8.SPINE11155

S. M. Friedewald, E. A. Rafferty, S. L. Rose, M. A. Durand, D. M. Plecha et al., Breast Cancer Screening Using Tomosynthesis in Combination With Digital Mammography, JAMA, vol.311, issue.24, pp.2499-2507, 2014.
DOI : 10.1001/jama.2014.6095

L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, IEEE Transactions on Neural Networks, vol.18, issue.3, 2004.
DOI : 10.1109/TNN.2007.897478

L. I. Kuncheva and J. J. Rodríguez, Classifier ensembles for fMRI data analysis: an experiment, Magnetic Resonance Imaging, vol.28, issue.4, pp.583-593, 2010.
DOI : 10.1016/j.mri.2009.12.021

E. L. Allwein, R. E. Schapire, and Y. Singer, Reducing multiclass to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res, vol.1, pp.113-141, 2001.

L. I. Kuncheva and C. J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, vol.51, issue.2, pp.181-207, 2003.
DOI : 10.1023/A:1022859003006

L. I. Kuncheva, C. Whitaker, C. Shipp, and R. Duin, Limits on the majority vote accuracy in classifier fusion, Pattern Analysis & Applications, vol.6, issue.1, pp.22-31, 2003.
DOI : 10.1007/s10044-002-0173-7

E. Bauer and R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning, vol.36, issue.1/2, pp.105-139, 1999.
DOI : 10.1023/A:1007515423169

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

N. Snavely, Bundler: Structure from motion for unordered image collections, 2010.

J. Shuttleworth, A. Todman, R. N. Naguib, B. Newman, and M. Bennett, Colour texture analysis using co-occurrence matrices for classification of colon cancer images, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373), pp.1134-1139, 2002.
DOI : 10.1109/CCECE.2002.1013107

S. Mcdonagh, R. B. Fisher, and J. Rees, Using 3D information for classification of non-melanoma skin lesions, Proc. Medical Image Understanding and Analysis, pp.164-168, 2008.

S. Manivannan, R. Wang, E. Trucco, and A. Hood, Automatic normalabnormal video frame classification for colonoscopy, Procs. of International Symposium Biomedical Imaging (ISBI), pp.644-647, 2013.

M. Hafner, M. Liedlgruber, A. Uhl, A. Vcsei, and F. Wrba, Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy, Computer Methods and Programs in Biomedicine, vol.107, issue.3, pp.565-581, 2012.
DOI : 10.1016/j.cmpb.2011.12.012

J. Bernal, J. Sánchez, and F. Vilariño, Towards automatic polyp detection with a polyp appearance model, Pattern Recognition, vol.45, issue.9, pp.3166-3182, 2012.
DOI : 10.1016/j.patcog.2012.03.002

S. Hwang, J. Oh, W. Tavanapong, J. Wong, and P. De-groen, Polyp Detection in Colonoscopy Video using Elliptical Shape Feature, 2007 IEEE International Conference on Image Processing, pp.465-468, 2007.
DOI : 10.1109/ICIP.2007.4379193

J. Yao, J. Li, and R. M. Summers, Employing topographical height map in colonic polyp measurement and false positive reduction, Pattern Recognition, vol.42, issue.6, pp.1029-1040, 2009.
DOI : 10.1016/j.patcog.2008.09.034

R. Shi, P. Schraedley-desmond, S. Napel, E. W. Olcott, B. R. Jeffrey et al., CT Colonography: Influence of 3D Viewing and Polyp Candidate Features on Interpretation with Computer-aided Detection, Radiology, vol.239, issue.3, pp.768-776, 2006.
DOI : 10.1148/radiol.2393050418

T. A. Chowdhury, P. F. Whelan, and O. Ghita, The use of 3D surface fitting for robust polyp detection and classification in CT colonography, Computerized Medical Imaging and Graphics, vol.30, issue.8, pp.427-436, 2006.
DOI : 10.1016/j.compmedimag.2006.06.004

K. Wehrmann and P. Fruhmorgen, Evaluation of a New Three-Dimensional Magnetic Imaging System for Use During Colonoscopy, Endoscopy, vol.34, issue.11, pp.905-908, 2002.
DOI : 10.1055/s-2002-35302

V. Parot, D. Lim, G. Gonzalez, G. Traverso, N. S. Nishioka et al., Photometric stereo endoscopy, Journal of Biomedical Optics, vol.18, issue.7, p.76017, 2013.
DOI : 10.1117/1.JBO.18.7.076017.2

K. Oka, T. Seki, T. Akatsu, T. Wakabayashi, K. Inui et al., Clinical study using novel endoscopic system for measuring size of gastrointestinal lesion, World Journal of Gastroenterology, vol.20, issue.14, pp.4050-4058, 2014.
DOI : 10.3748/wjg.v20.i14.4050

A. N. Burnett-hartman, M. N. Passarelli, S. V. Adams, M. P. Upton, L. C. Zhu et al., Differences in Epidemiologic Risk Factors for Colorectal Adenomas and Serrated Polyps by Lesion Severity and Anatomical Site, American Journal of Epidemiology, vol.177, issue.7, pp.625-637, 2013.
DOI : 10.1093/aje/kws282

K. J. Spring, Z. Z. Zhao, R. Karamatic, M. D. Walsh, V. L. Whitehall et al., High Prevalence of Sessile Serrated Adenomas With BRAF Mutations: A Prospective Study of Patients Undergoing Colonoscopy, Gastroenterology, vol.131, issue.5, pp.1400-1407, 2006.
DOI : 10.1053/j.gastro.2006.08.038

V. P. Bauer and H. T. Papaconstantinou, Management of Serrated Adenomas and Hyperplastic Polyps, Clinics in Colon and Rectal Surgery, vol.21, issue.04, pp.273-279, 2008.
DOI : 10.1055/s-0028-1089942

R. Nava, G. Cristóbal, and B. Escalante-ramírez, Invariant texture analysis through local binary patterns, 1111.

F. Riaz, F. B. Silva, M. Dinis-ribeiro, and M. T. Coimbra, Invariant Gabor Texture Descriptors for Classification of Gastroenterology Images, IEEE Transactions on Biomedical Engineering, vol.59, issue.10, pp.2893-2904, 2012.
DOI : 10.1109/TBME.2012.2212440

S. Stefanou and A. Argyros, Efficient Scale and Rotation Invariant Object Detection Based on HOGs and Evolutionary Optimization Techniques, Advances in Visual Computing, ser, pp.220-229, 2012.
DOI : 10.1007/978-3-642-33179-4_22

F. Bianconi and A. Fernández, Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform, Pattern Recognition Letters, vol.48, pp.34-41, 2014.
DOI : 10.1016/j.patrec.2014.04.006

A. Artusi, F. Banterle, and D. Chetverikov, A Survey of Specularity Removal Methods, Computer Graphics Forum, vol.2, issue.5, pp.2208-2230, 2011.
DOI : 10.1111/j.1467-8659.2011.01971.x

A. Blake and G. Brelstaff, Geometry From Specularities, [1988 Proceedings] Second International Conference on Computer Vision, pp.394-403, 1988.
DOI : 10.1109/CCV.1988.590016

R. W. Fleming, A. Torralba, and E. H. Adelson, Specular reflections and the perception of shape, Journal of Vision, vol.4, issue.9, p.10, 2004.
DOI : 10.1167/4.9.10

A. Zisserman, P. Giblin, and A. Blake, The information available to a moving observer from specularities, Image and Vision Computing, vol.7, issue.1, pp.38-42, 1989.
DOI : 10.1016/0262-8856(89)90018-8

J. Van-de-weijer, C. Schmid, J. Verbeek, and D. Larlus, Learning Color Names for Real-World Applications, IEEE Transactions on Image Processing, vol.18, issue.7, pp.1512-1523, 2009.
DOI : 10.1109/TIP.2009.2019809
URL : https://hal.archives-ouvertes.fr/inria-00439284

R. Khan, J. Van-de-weijer, F. S. Khan, D. Muselet, C. Ducottet et al., Discriminative Color Descriptors, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2866-2873, 2013.
DOI : 10.1109/CVPR.2013.369
URL : https://hal.archives-ouvertes.fr/ujm-00854763

J. Van-de-weijer and C. Schmid, Coloring Local Feature Extraction, Procs. of the 9th European Conference on Computer Vision -Volume Part II (ECCV'06), pp.334-348, 2006.
DOI : 10.1002/col.10049
URL : https://hal.archives-ouvertes.fr/inria-00548576

S. Engelhardt, S. Ameling, S. Wirth, and D. Paulus, Features for classification of polyps in colonoscopy, Bildverarbeitung fr die Medizin, ser. CEUR Workshop Proceedings, pp.350-354, 2010.

P. Mesejo, R. Ugolotti, S. Cagnoni, F. D. Cunto, and M. Giacobini, Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp.1-4
DOI : 10.1109/CBMS.2012.6266318
URL : https://hal.archives-ouvertes.fr/hal-01221660

R. Ugolotti, P. Mesejo, S. Zongaro, B. Bardoni, G. Berto et al., Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC, PLoS ONE, vol.130, issue.9, p.74481, 2013.
DOI : 10.1371/journal.pone.0074481.s002
URL : https://hal.archives-ouvertes.fr/hal-01221314

T. Mäenpää and M. Pietikäinen, Classification with color and texture: jointly or separately?, Pattern Recognition, vol.37, issue.8, pp.1629-1640, 2004.
DOI : 10.1016/j.patcog.2003.11.011

M. Coimbra, P. Campos, and J. P. Cunha, Extracting clinical information from endoscopic capsule exams using MPEG-7 visual descriptors, 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (EWIMT 2005), pp.105-110, 2005.
DOI : 10.1049/ic.2005.0718

R. Kumar, Q. Zhao, S. Seshamani, G. Mullin, G. Hager et al., Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images, IEEE Transactions on Biomedical Engineering, vol.59, issue.2, pp.355-362, 2012.
DOI : 10.1109/TBME.2011.2172438

J. Heikkila and O. Silvén, A four-step camera calibration procedure with implicit image correction, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1106-1112, 1997.
DOI : 10.1109/CVPR.1997.609468

Z. Zhang, A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.11, pp.1330-1334, 2000.
DOI : 10.1109/34.888718

J. Bouguet, Camera calibration toolbox for matlab, 2004.

P. Cignoni, M. Corsini, and G. Ranzuglia, Meshlab: an open-source 3d mesh processing system, Ercim news, vol.73, pp.45-46, 2008.

M. Reuter, F. Wolter, and N. Peinecke, Laplace???Beltrami spectra as ???Shape-DNA??? of surfaces and solids, Computer-Aided Design, vol.38, issue.4, pp.342-366, 2006.
DOI : 10.1016/j.cad.2005.10.011

R. M. Rustamov, Laplace-beltrami eigenfunctions for deformation invariant shape representation, Proc. of Eurographics Symposium on Geometry Processing, pp.225-233, 2007.

C. M. Bishop, Pattern recognition and machine learning, 2006.

L. Bo, X. Ren, and D. Fox, Depth kernel descriptors for object recognition, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.821-826, 2011.
DOI : 10.1109/IROS.2011.6095119

L. Breiman, Random forests, Maching Learning, pp.5-32, 2001.

T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell, vol.20, issue.8, pp.832-844, 1998.

C. Lai, M. J. Reinders, and L. Wessels, Random subspace method for multivariate feature selection, Pattern Recognition Letters, vol.27, issue.10, pp.1067-1076, 2006.
DOI : 10.1016/j.patrec.2005.12.018

M. Hafner, M. Liedlgruber, S. Maimone, A. Uhl, A. Vecsei et al., Evaluation of cross-validation protocols for the classification of endoscopic images of colonic polyps, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp.1-6
DOI : 10.1109/CBMS.2012.6266355

A. Bertoni, R. Folgieri, and G. Valentini, Bio-molecular cancer prediction with random subspace ensembles of support vector machines, Neurocomputing, vol.63, issue.0, pp.535-539, 2005.
DOI : 10.1016/j.neucom.2004.07.007

J. Lee, J. H. Kim, S. H. Kim, H. S. Park, and B. I. Choi, A straightforward approach to computer-aided polyp detection using a polyp-specific volumetric feature in CT colonography, Computers in Biology and Medicine, vol.41, issue.9, pp.790-801, 2011.
DOI : 10.1016/j.compbiomed.2011.06.015

T. Tamaki, J. Yoshimuta, T. Takeda, B. Raytchev, K. Kaneda et al., A System for Colorectal Tumor Classification in Magnifying Endoscopic NBI Images, Computer Vision ACCV 2010, ser, pp.452-463, 2011.
DOI : 10.1007/s11263-006-9794-4

F. Riaz, F. Vilarino, M. D. Ribeiro, and M. Coimbra, Identifying Potentially Cancerous Tissues in Chromoendoscopy Images, Procs. of Iberian Conference on Pattern Recognition and Image Analysis, pp.709-716, 2011.
DOI : 10.1007/978-3-642-21257-4_88

J. M. Aman, R. M. Summers, and J. Yao, Characterizing Colonic Detections in CT Colonography Using Curvature-Based Feature Descriptor and Bag-of-Words Model, Virtual Colonoscopy and Abdominal Imaging -2nd International Workshop, Held in Conjunction with MICCAI, pp.15-23, 2010.
DOI : 10.1007/978-3-642-25719-3_3

W. Li, M. Coats, J. Zhang, and S. J. Mckenna, Discriminating dysplasia: Optical tomographic texture analysis of colorectal polyps, Medical Image Analysis, vol.26, issue.1, pp.57-69, 2015.
DOI : 10.1016/j.media.2015.08.002

T. Muto, H. Bussey, and B. Morson, The evolution of cancer of the colon and rectum, Cancer, vol.1, issue.6, pp.2251-2270, 1975.
DOI : 10.1002/cncr.2820360944

J. G. Einspahr, D. S. Alberts, S. M. Gapstur, R. M. Bostick, S. S. Emerson et al., Surrogate end-point biomarkers as measures of colon cancer risk and their use in cancer chemoprevention trials, Cancer Epidemiol. Biomarkers Prev, vol.6, issue.1, pp.37-48, 1997.

R. M. Summers, Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?, Radiology, vol.255, issue.3, pp.707-720, 2010.
DOI : 10.1148/radiol.10090877

S. Rm, K. T. , and R. Rv, Prevalence of nonpolypoid (flat and depressed) colorectal neoplasms in asymptomatic and symptomatic adults, JAMA, vol.299, issue.9, pp.1027-1035, 2008.

A. R. Hart, S. Kudo, E. H. Mackay, J. F. Mayberry, and W. S. Atkin, Flat adenomas exist in asymptomatic people: important implications for colorectal cancer screening programmes, Gut, vol.43, issue.2, pp.229-231, 1998.
DOI : 10.1136/gut.43.2.229

M. Arnold, A. Ghosh, S. Ameling, and G. Lacey, Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging, EURASIP Journal on Image and Video Processing, vol.58, issue.6, pp.1-9, 2010.
DOI : 10.1109/TITB.2003.813794

J. Bernal, J. Sánchez, and F. V. No, Impact of image preprocessing methods on polyp localization in colonoscopy frames, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.7350-7354, 2013.
DOI : 10.1109/EMBC.2013.6611256

F. Chadebecq, C. Tilmant, and A. Bartoli, Using the Infocus-Breakpoint to estimate the scale of neoplasia in colonoscopy, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.354-357, 2013.
DOI : 10.1109/ISBI.2013.6556485