, Profilage de la chaine de traitement sur un processeur embarqué, p.88

, État de l'art : transformée de Hough et ses implémentations 92

. .. Hough, , p.93

, Algorithme de la transformée d'Hough Unidimensionnelle (1DHT), p.93

.. .. Synthèse,

. .. , 98 7.2 Architecture pour la mise en oeuvre de l'algorithme 1DHT modifié . . 103 7.2.1 Calcul des coordonnées en x

. Mise and . .. Fpga,

, Mise en oeuvre de l'architecture du 1DHT IP dans un Système sur Puce112

. .. Candidats, 118 8.2 Architecture proposée pour la mise en oeuvre du calcul des coordonnées en x

F. .. Intégration-de-l'ip-1dht-optimisée-sur, , p.125

, Intégration de notre IP 1DHT-optimisée dans une système sur puce, p.125

. .. Ip-optimisée,

, Synthèse et discussion

, CapsoCam Plus R | CapsoVision

, Olympus America, vol.10

, GLCM texture features

, A High-Performance Reconfigurable Accelerator for Convolutional Neural Networks | Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing

, JINSHAN Science & TechnologyCapsuleEndoscopy

, La Sociedad Americana Contra El Cancer | Información sobre cáncer de seno, colon, pulmón, piel, y otros

T. M. Pillcam, |. Colon-2-system, and . Medtronic,

. Zedboard,

. Axi-reference-guide, , 1973.

. Axi-reference-guide, , 2011.

, Cancer Today, 2018.

, Zynq-7000 SoC Data Sheet : Overview (DS190), 2018.

N. K. Aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull et al., The BIBLIOGRAPHIE European Organization for Research and Treatment of Cancer QLQ-C30 : A Quality-of-Life Instrument for Use in International Clinical Trials in Oncology, J Natl Cancer Inst, vol.85, pp.365-376, 1993.

T. Acharya and A. K. Ray, Image Processing : Principles and Applications, 2005.

S. N. Adler, Y. C. Metzger, . Pillcam-colon-capsule, and . Endoscopy, Recent Advances and New Insights. Ther. Adv Gastroenterol, vol.4, pp.265-268, 2011.

A. R. Akoushideh, A. Shahbahrami, B. M. Maybodi, and .. , High Performance Implementation of Texture Features Extraction Algorithms Using FPGA Architecture, J Real-Time Image Proc, vol.9, pp.141-157, 2014.

, Design and Implementation of the Tree-Based Fuzzy Logic Controller, IEEE Trans Syst Man Cybern Part B Cybern, vol.27, issue.3, pp.475-487, 1997.

R. Andraka, A survey of CORDIC algorithms for FPGA based computers, Proceedings of the 1998 ACM/SIGDA Sixth International Symposium on Field Programmable Gate Arrays -FPGA '98, pp.191-200, 1998.

Q. Angermann, J. Bernal, C. Sanchez-montes, M. Hammami, G. Fernandez-esparrach et al., Towards Real-Time Polyp Detection in Colonoscopy Videos : Adapting Still Frame-Based Methodologies for Video Sequences Analysis, Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures, pp.29-41, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01590659

D. Antalovic, Review of the Hough Transform Method, With an Implementation of the Fast Hough Variant for Line Detection, DCS, 2008.

A. Axon, M. D. Diebold, M. Fujino, R. Fujita, R. M. Genta et al., Update on the Paris classification of superficial neoplastic lesions in the digestive tract, Endoscopy, vol.37, issue.6, pp.570-578, 2005.

J. Bernal, F. J. Sánchez, G. Fernández-esparrach, D. Gil, C. Rodríguez et al., Maps for Accurate Polyp Highlighting in Colonoscopy : Validation vs, Saliency Maps from Physicians. Comput. Med. Imaging Graph, vol.43, pp.99-111, 2015.

J. Bernal, J. Sánchez, and F. Vilarino, Automatic Polyp Detection with a Polyp Appearance Model, Pattern Recognit, vol.45, pp.3166-3182, 2012.

J. Bernal, N. Tajkbaksh, F. J. Sánchez, B. J. Matuszewski, H. Chen et al., Comparative Validation of Polyp Detection Methods in Video Colonoscopy : Results From the MICCAI 2015 Endoscopic Vision Challenge, IEEE Trans Med Imaging, vol.36, issue.6, pp.1231-1249, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01488652

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp.144-152, 1992.

N. Bourbakis, G. Giakos, and A. Karargyris, Design of New-Generation Robotic Capsules for Therapeutic and Diagnostic Endoscopy, 2010 IEEE International Conference on Imaging Systems and Techniques, pp.1-6, 2010.

F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre et al., Global Cancer Statistics 2018 : GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA Cancer J Clin, vol.68, pp.394-424, 2018.

H. Brenner and S. Tao, Superior Diagnostic Performance of Faecal Immunochemical Tests for Haemoglobin in a Head-to-Head Comparison with Guaiac Based Faecal Occult Blood Test among 2235 Participants of Screening Colonoscopy, Eur. J. Cancer, vol.49, pp.3049-3054, 2013.

A. Buades, T. Le, J. Morel, and L. Vese, Cartoon+Texture Image Decomposition. Image Process Line, vol.1, pp.200-207, 2011.

J. Canny, A Computational Approach to Edge Detection, IEEE Trans Pattern Anal Mach Intell PAMI, vol.8, issue.6, pp.679-698, 1986.

A. Chiolerio, A. Chiodoni, A. , and P. , Elemental distribution and morphological analysis of layered metallic systems : Application to co-sn evaporated multilayers, Thin Solid Films, vol.516, pp.8453-8461, 2008.

T. H. Dang, Discrimination measures and theirs applications in inductif learning, Theses, 2007.
URL : https://hal.archives-ouvertes.fr/tel-00184691

E. David, R. Boia, A. Malaescu, and M. Carnu, Automatic colon polyp detection in endoscopic capsule images, International Symposium on Signals, Circuits and Systems ISSCS2013, 2013.

T. R. De-wijkerslooth, E. M. Stoop, P. M. Bossuyt, G. A. Meijer, M. Van-ballegooijen et al., Immunochemical Fecal Occult Blood Testing Is Equally Sensitive for Proximal and Distal Advanced Neoplasia, Am J Gastroenterol, vol.107, pp.1570-1578, 2012.

V. K. Dik, L. M. Moons, and P. D. Siersema, Endoscopic Innovations to Increase the Adenoma Detection Rate during Colonoscopy, World J Gastroenterol, vol.20, pp.2200-2211, 2014.

L. Orazio, A. Bartoli, A. Baetz, S. Beorchia, G. Calvary et al., Multimodal and Multimedia Image Analysis and Collaborative Networking for Digestive Endoscopy, IRBM, vol.35, pp.88-93, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00953377

A. Elhossini and M. Moussa, Memory Efficient Fpga Implementation of Hough Transform for Line and Circle Detection, Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference On, pp.1-5, 2012.

R. Eliakim, K. Yassin, Y. Niv, Y. Metzger, J. Lachter et al., Prospective Multicenter Performance Evaluation of the Second-Generation Colon Capsule Compared with Colonoscopy, Endoscopy, vol.41, pp.1026-1031, 2009.

S. H. Elsafi, N. I. Alqahtani, N. Y. Zakary, A. Zahrani, and E. M. , The Sensitivity, Specificity, Predictive Values, and Likelihood Ratios of Fecal Occult Blood Test for the Detection of Colorectal Cancer in Hospital Settings, Clin Exp Gastroenterol, vol.8, pp.279-284, 2015.

F. Ferhat-taleb-alim, K. Messaoudi, S. Seddiki, and O. Kerdjidj, Modified Circular Hough Transform Using FPGA, 24th International Conference on Microelectronics (ICM), pp.1-4, 2012.

D. Filip, O. Yadid-pecht, G. Muench, M. P. Mintchev, and C. N. Andrews, Suture Marker Lesion Detection in the Colon by Self-Stabilizing and Unmodified Capsule Endoscopes : Pilot Study in Acute Canine Models, Gastrointest. Endosc, vol.77, pp.272-279, 2013.

L. R. Fisher and W. Hasler, New Vision in Video Capsule Endoscopy : Current Status and Future Directions, Nat Rev Gastroenterol Hepatol, vol.9, issue.7, pp.392-405, 2012.

A. Goneid, S. El-gindi, and A. Sewisy, A method for the Hough transform detection of circles and ellipses using a 1-dimensional array, Computational Cybernetics and Simulation 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol.4, pp.3154-3157, 1997.

V. P. Gopi and P. Palanisamy, Capsule Endoscopic Image Denoising Based on Double Density Dual Tree Complex Wavelet Transform, p.14

M. Häfner, M. Liedlgruber, and A. Uhl, POCS-Based Super-Resolution for HD Endoscopy Video Frames, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp.185-190, 2013.

A. K. Hara, C. D. Johnson, J. E. Reed, D. A. Ahlquist, H. Nelson et al., Detection of Colorectal Polyps with CT Colography : Initial Assessment of Sensitivity and Specificity, Radiology, vol.205, pp.59-65, 1997.

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Trans. Syst. Man Cybern. SMC, vol.3, pp.610-621, 1973.

S. Hwang, Bag Of Visual Words Approach Based on SURF Features to Polyp Detection in Wireless Capsule Endoscopy Videos, vol.4

D. K. Iakovidis and A. Koulaouzidis, Software for Enhanced Video Capsule Endoscopy : Challenges for Essential Progress, Nat Rev Gastroenterol Hepatol, vol.12, pp.172-186, 2015.

A. Irwansyah, O. W. Ibraheem, J. Hagemeyer, M. Porrmann, and U. Rueckert, FPGA-Based Circular Hough Transform with Graph Clustering for Vision-Based Multi-Robot Tracking, 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), pp.1-8, 2015.

A. Karargyris and N. Bourbakis, Identification of polyps in Wireless Capsule Endoscopy videos using Log Gabor filters, 2009 IEEE/NIH Life Science Systems and Applications Workshop, pp.143-147, 2009.

A. Karargyris and N. Bourbakis, Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos, IEEE Trans Biomed Eng, vol.58, pp.2777-2786, 2011.

V. Kodogiannis and M. Boulougoura, An Adaptive Neurofuzzy Approach for the Diagnosis in Wireless Capsule Endoscopy Imaging, Int J Inf Technol, vol.13, pp.46-56, 2007.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012.

Z. Kulpa, On the properties of discrete circles, rings, and disks, Computer Graphics and Image Processing, vol.10, pp.348-365, 1979.

V. Kumar, A. Asati, and A. Gupta, Hardware Accelerators for Iris Localization, J Sign Process Syst, vol.90, pp.655-671, 2018.

H. D. Landahl, W. S. Mcculloch, and W. Pitts, A Statistical Consequence of the Logical Calculus of Nervous Nets, Bull. Math. Biophys, vol.5, pp.135-137, 1943.

I. Lansdorp-vogelaar, M. Van-ballegooijen, A. G. Zauber, J. D. Habbema, and E. J. Kuipers, Effect of Rising Chemotherapy Costs on the Cost Savings of Colorectal Cancer Screening, J Natl Cancer Inst, vol.101, pp.1412-1422, 2009.

Y. Lecun, Comparing Different Neural Network Architectures for Classifying Handwritten Digits, IJCNN Proc N IEEE Hebb, p.1949, 1989.

Y. Y. Lee, A. Erdogan, and S. S. Rao, How to Assess Regional and Whole Gut Transit Time With Wireless Motility Capsule, J Neurogastroenterol Motil, vol.20, pp.265-270, 2014.

T. R. Levin, D. A. Corley, C. D. Jensen, J. E. Schottinger, V. P. Quinn et al., Effects of Organized Colorectal Cancer Screening on Cancer Incidence and Mortality in a Large Community-Based Population, Gastroenterology, vol.155, pp.1383-1391, 2018.

B. T. Levy, C. Bay, Y. Xu, J. M. Daly, G. Bergus et al., Test Characteristics of Fecal Immunochemical Tests (FIT) Compared with Optical Colonoscopy Revised JMS-14-003.R2, J Med Screen, vol.21, pp.133-143, 2014.

B. Li, M. Q. Meng, and .. , Automatic Polyp Detection for Wireless Capsule Endoscopy Images, Expert Syst Appl, vol.39, pp.10952-10958, 2012.

A. V. Mamonov, I. N. Figueiredo, P. N. Figueiredo, and Y. R. Tsai, Automated polyp detection in colon capsule endoscopy, IEEE Trans. Med. Imaging, vol.33, pp.1488-1502, 2014.

C. Marsala, Apprentissage Inductif En Presence de Donnees Imprecises : Construction et Utilisation d'arbres de Decision Flous, Thesis, vol.6, 1998.

C. Marsala and B. Bouchon-meunier, Fuzzy partitioning using mathematical morphology in a learning scheme, Proceedings of IEEE 5th International Fuzzy Systems, vol.2, pp.1512-1517, 1996.

J. N. Morgan and J. A. Sonquist, Problems in the Analysis of Survey Data, and a Proposal, J. Am. Stat. Assoc, vol.58, pp.415-434, 1963.

P. Mukhopadhyay and B. B. Chaudhuri, A Survey of Hough Transform, Pattern Recognit, vol.48, pp.993-1010, 2015.

R. D. Nawarathna, J. Oh, X. Yuan, J. Lee, and S. J. Tang, Abnormal Image Detection Using Texton Method in Wireless Capsule Endoscopy Videos, Medical Biometrics, pp.153-162, 2010.

H. Okuhata, H. Nakamura, S. Hara, H. Tsutsui, and T. Onoye, Application of the Real-Time Retinex Image Enhancement for Endoscopic Images, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.3407-3410, 2013.

G. Pan, W. , and L. , Swallowable Wireless Capsule Endoscopy : Progress and Technical Challenges, Gastroenterol Res Pr, p.2012, 2012.

J. R. Quinlan, Induction of decision trees, Mach Learn, vol.1, pp.81-106, 1986.

D. Ramsoekh, J. Haringsma, J. W. Poley, P. Van-putten, H. Van-dekken et al., A Back-to-Back Comparison of White Light Video Endoscopy with Autofluorescence Endoscopy for Adenoma Detection in High-Risk Subjects, Gut, vol.59, issue.6, pp.785-793, 2010.

D. Rex, C. Cutler, G. Lemmel, E. Rahmani, D. Clark et al., Colonoscopic Miss Rates of Adenomas Determined by Back-to-Back Colonoscopies, Gastroenterology, vol.112, pp.24-28, 1997.

O. Romain, A. Histace, J. Silva, J. Ayoub, B. Granado et al., Towards a Multimodal Wireless Video Capsule for Detection of Colonic Polyps as Prevention of Colorectal Cancer, 13th IEEE International Conference on BioInformatics and BioEngineering, pp.1-6, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00861529

E. Rondonotti, A. Koulaouzidis, A. Karargyris, A. Giannakou, L. Fini et al., Utility of 3-Dimensional Image Reconstruction in the Diagnosis of Small-Bowel Masses in Capsule Endoscopy (with Video), Gastrointest. Endosc, vol.80, pp.642-651, 2014.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, Int J Comput Vis, vol.115, pp.211-252, 2015.

A. Samuel, some studies in machine learning using the game of checkers, IBM Journal of Researchand Development, vol.3, pp.211-229, 1959.

T. J. Sejnowski, P. K. Kienker, and G. E. Hinton, Learning Symmetry Groups with Hidden Units : Beyond the Perceptron, Phys. Nonlinear Phenom, vol.22, pp.260-275, 1986.

S. Seo, K. , and M. , Efficient architecture for circle detection using Hough transform, 2015 International Conference on Information and Communication Technology Convergence (ICTC), pp.570-572, 2015.

C. E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal, vol.27, issue.3, pp.379-423, 1948.

K. Shim, S. R. Jeon, H. J. Jang, J. Kim, Y. J. Lim et al., Quality Indicators for Small Bowel Capsule Endoscopy, Clin Endosc, vol.50, pp.148-160, 2017.

E. Spyrou and D. K. Iakovidis, Video-Based Measurements for Wireless Capsule Endoscope Tracking, Meas Sci Technol, vol.25, p.15002, 2013.

P. Swain, Wireless Capsule Endoscopy. Gut, vol.52, issue.4, pp.48-50, 2003.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going Deeper With Convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.

N. Tajbakhsh, S. R. Gurudu, and J. Liang, Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information, IEEE Trans Med Imaging, vol.35, pp.630-644, 2016.

T. D. Than, G. Alici, H. Zhou, and W. Li, A Review of Localization Systems for Robotic Endoscopic Capsules, IEEE Trans Biomed Eng, vol.59, pp.2387-2399, 2012.

A. Van-gossum, M. Munoz-navas, I. Fernandez-urien, C. Carretero, G. Gay et al., Capsule Endoscopy versus Colonoscopy for the Detection of Polyps and Cancer, N Engl J Med, vol.361, pp.264-270, 2009.

J. S. Walther, Unified Algorithm for Elementary Functions, Spring Joint Computer Conference, pp.379-385, 1971.

X. Wu and V. Kumar, The Top Ten Algorithms in Data Mining, 2009.

L. Yu, H. Chen, Q. Dou, J. Qin, and P. A. Heng, Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos, IEEE J Biomed Health Inf, vol.21, pp.65-75, 2017.

H. Yuen, J. Princen, J. Illingworth, and J. Kittler, Comparative study of Hough Transform methods for circle finding, Image and Vision Computing, vol.8, pp.71-77, 1990.

L. Zadeh, Probability measures of Fuzzy events, Journal of Mathematical Analysis and Applications, vol.23, pp.421-427, 1968.

L. A. Zadeh, G. J. Klir, and B. Yuan, Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems : Selected Papers, 1996.

R. Zhang, Y. Zheng, C. C. Poon, D. Shen, and J. Y. Lau, Polyp Detection during Colonoscopy Using a Regression-Based Convolutional Neural Network with a Tracker, Pattern Recognit, vol.83, pp.209-219, 2018.

Q. Zhao, T. Dassopoulos, G. E. Mullin, M. Q. Meng, and R. Kumar, A Decision Fusion Strategy for Polyp Detection in Capsule Endoscopy. Stud Health Technol Inf, vol.173, pp.559-565, 2012.

X. Zhou, Y. Ito, and K. Nakano, An Efficient Implementation of the One-Dimensional Hough Transform Algorithm for Circle Detection on the FPGA, Computing and Networking (CANDAR), pp.447-452, 2014.