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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A System for Colorectal Tumor Classification in Magnifying Endoscopic NBI Images
verfasst von : Toru Tamaki, Junki Yoshimuta, Takahishi Takeda, Bisser Raytchev, Kazufumi Kaneda, Shigeto Yoshida, Yoshito Takemura, Shinji Tanaka
Erschienen in: Computer Vision – ACCV 2010
Verlag: Springer Berlin Heidelberg
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In this paper we propose a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) of structures of microvessels on the colorectal surface. These types have a strong correlation with histologic diagnosis:
hyperplasias
(HP),
tubular adenomas
(TA), and
carcinomas with massive submucosal invasion
(SM-m). Images are represented by Bag-of-features of the SIFT descriptors densely sampled on a grid, and then classified by an SVM with an RBF kernel. A dataset of 907 NBI images were used for experiments with 10-fold cross-validation, and recognition rate of 94.1% were obtained.