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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Tamaki, T., Yoshimuta, J., Takeda, T., Raytchev, B., Kaneda, K., Yoshida, S., … Tanaka, S. (2011). A system for colorectal tumor classification in magnifying endoscopic NBI images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6493 LNCS, pp. 452–463). https://doi.org/10.1007/978-3-642-19309-5_35
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