A system for colorectal tumor classification in magnifying endoscopic NBI images

Authors:
Toru Tamaki;Junki Yoshimuta;Takahishi Takeda;Bisser Raytchev;Kazufumi Kaneda;Shigeto Yoshida;Yoshito Takemura;Shinji Tanaka
Affiliations:
Hiroshima University;Hiroshima University;Hiroshima University;Hiroshima University;Hiroshima University;Hiroshima University;Hiroshima University;Hiroshima University
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Year:
2010

Citing 5
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Abstract

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.