NDLI: SVM based colon polyps classifier in a wireless active stereo endoscope

Content Provider	IEEE Xplore Digital Library
Author	Ayoub, J. Granado, B. Mhanna, Y. Romain, O.
Copyright Year	2010
Description	Author affiliation: Université Pierre et Marie Curie (UPMC), groupe SYEL, 4 place Jussieu, Paris, France (Romain, O.) \|\| Department of Physics and Electronics, Faculty of Sciences I, Lebanese University, Beirut, Lebanon (Mhanna, Y.) \|\| ETIS, CNRS, ENSEA, Université de Cergy Pontoise, 1, Av du Ponceau, 95014 cedex, France (Ayoub, J.; Granado, B.)
Abstract	This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between hyperplastic and adenomatous polyps. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains detailed statistics about the detection rate and the computing complexity. Inspired by intensity histogram, the work shows a new approach that extracts a set of features based on depth histogram and combines stereo measurement with SVM classifiers to correctly classify benign and malignant polyps.
Starting Page	5585
Ending Page	5588
File Size	1125432
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Feature extraction Three dimensional displays Cancer Colonic polyps Kernel Solid modeling 3D endoscopy 3D reconstruction polypectomy object classification Active stereovision feature selection moment invariant
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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