Local fractal dimension based approaches for colonic polyp classification

Andrė, 2012, Learning semantic and visual similarity for endomicroscopy video retrieval, IEEE Trans. Med. Imag., 31, 1276, 10.1109/TMI.2012.2188301 Andrė, 2011, A smart atlas for endomicroscopy using automated video retrieval, Med. Image Anal., 15, 460, 10.1016/j.media.2011.02.003 Chaudhuri, 1995, Texture segmentation using fractal dimension, IEEE Trans. Pattern Anal. Mach. Intell., 17, 72, 10.1109/34.368149 Eystratios, 2012, Fuzzy binary patterns for uncertainty-aware texture representation, Electr. Lett. Comp. Vis. Image Anal., 10, 63 Fay, 2010, Wilcoxon-mann-whitney or t-test? on assumptions for hypothesis tests and multiple interpretations of decision rules, Stat. Surv., 4, 1, 10.1214/09-SS051 Geusebroek, 2003, Fast anisotropic gauss filtering, IEEE Trans. Image Process., 12, 938, 10.1109/TIP.2003.812429 Gono, 2003, Endoscopic observation of tissue by narrowband illumination, Opt. Rev., 10, 211, 10.1007/s10043-003-0211-8 Gross, 2012, Automated classification of colon polyps in endoscopic image data, Proc. SPIE, 8315, 10.1117/12.911177 Häfner, 2014, A novel shape feature descriptor for the classification of polyps in HD colonoscopy, 205 Häfner, 2009, Feature-extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images, Pattern Anal. Appl., 12, 407, 10.1007/s10044-008-0136-8 Häfner, 2012, Color treatment in endoscopic image classification using multi-scale local color vector patterns, Med. Image Anal., 16, 75, 10.1016/j.media.2011.05.006 Häfner, 2014, Bridging the resolution gap between endoscope types for a colonic polyp classification, 2734 Häfner, 2014, Shape and size adapted local fractal dimension for the classification of polyps in hd colonoscopy, 2299 Iakovidis, 2015, Software for enhanced video capsule endoscopy: Challenges for essential progress, Nat. Rev. Gastroenterol. Hepatol., 12, 172, 10.1038/nrgastro.2015.13 Iakovidis, 2006, An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy, Comp. Biol. Med., 36, 1084, 10.1016/j.compbiomed.2005.09.008 Iakovidis, 2005, A comparative study of texture features for the discrimination of gastric polyps in endoscopic video, 575 Jabbour, 2012, Confocal endomicroscopy: Instrumentation and medical applications, Annal. Biomed. Eng., 40, 378, 10.1007/s10439-011-0426-y Kanao, 2008, Clinical significance of type vi pit pattern subclassification in determining the depth of invasion of colorectal neoplasms, World J. Gastroenterol., 14, 211, 10.3748/wjg.14.211 Karkanis, 2003, Computer-aided tumor detection in endoscopic video using color wavelet features, IEEE Trans. Inform. Technol. Biomed., 7, 141, 10.1109/TITB.2003.813794 Kato, 2006, Magnifying colonoscopy as a non-biopsy technique for differential diagnosis of non-neoplastic and neoplastic lesions, World J. Gastroenterol., 12, 1416, 10.3748/wjg.v12.i9.1416 Kiesslich, 2009, Advanced imaging in endoscopy, Eur. Gastroenterol. Hepatol. Rev., 5, 22 Kodashima, 2010, Novel image-enhanced endoscopy with i-scan technology, World J. Gastroenterol., 16, 1043, 10.3748/wjg.v16.i9.1043 Kovesi, 1999, Image features from phase congruency, Videre: J. Comp. Vis. Res., 1, 2 Kovesi, P.D., 2000. MATLAB and Octave Functions for Computer Vision and Image Processing. Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia. Accessed from: <http://www.csse.uwa.edu.au/~pk/research/matlabfns/> . Kudo, 1994, Colorectal tumours and pit pattern, J. Clin. Pathol., 47, 880, 10.1136/jcp.47.10.880 Lazebnik, 2005, A sparse texture representation using local affine region, IEEE Trans. Pattern Anal. Mach. Intell., 27, 1265, 10.1109/TPAMI.2005.151 Liao, 2007, Learning multi-scale block local binary patterns for face recognition, Advances in Biometrics, Lecture Notes in Computer Science 4642, 2007, 828 Lopes, 2009, Fractal and multifractal analysis: A review, Med. Image Anal., 13, 634, 10.1016/j.media.2009.05.003 Maroulis, 2003, Cold: A versatile detection system for colorectal lesions in endoscopy video-frames, Comp. Method. Program. Biomed., 70, 151, 10.1016/S0169-2607(02)00007-X McNemar, 1947, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, 12, 153, 10.1007/BF02295996 Roerdink, 2000, The watershed transform: Definitions, algorithms and parallelization strategies, Fundam. Inform., 41, 187, 10.3233/FI-2000-411207 Romain, 2013, Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer, 1 Tamaki, 2013, Computer-aided colorectal tumor classification in NBI endoscopy using local features, Med. Image Anal., 17, 78, 10.1016/j.media.2012.08.003 Tan, 2010, Enhanced local texture feature sets for face recognition under difficult lighting conditions, IEEE Trans. Image Process., 19, 1635, 10.1109/TIP.2010.2042645 Uhl, 2011, Fractal analysis for the viewpoint invariant classification of celiac disease, 727 Varma, 2007, Locally invariant fractal features for statistical texture classification, 1 Varma, 2005, A statistical approach to texture classification from single images, Int. J. Comp. Vis., 62, 61, 10.1007/s11263-005-4635-4 Vedaldi, A., Fulkerson, B., 2008. VLFeat: An open and portable library of computer vision algorithms. Accessed from: http://www.vlfeat.org/. Vincent, 1991, Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Trans. Pattern Anal. Mach. Intell., 13, 583, 10.1109/34.87344 Xia, 2006, Morphology-based multifractal estimation for texture segmentation, IEEE Trans. Image Process., 15, 614, 10.1109/TIP.2005.863029 Xu, 2009, Viewpoint invariant texture description using fractal analysis, Int. J. Comp. Vis., 83, 85, 10.1007/s11263-009-0220-6 Yuce, 2012, Easy-to-swallow wireless telemetry, IEEE Microw. Mag., 13, 90, 10.1109/MMM.2012.2205833