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International Journal of Biomedical Imaging
Volume 2017, Article ID 7089213, 13 pages
https://doi.org/10.1155/2017/7089213
Research Article

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

1National Institute of Advanced Industrial Science and Technology (AIST), Artificial Intelligence Research Center (AIRC), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan
2Toho University Sakura Medical Center, 564-1 Shimoshizu, Sakura, Chiba 285-8741, Japan

Correspondence should be addressed to Hirokazu Nosato; pj.og.tsia@otason.h

Received 23 September 2016; Revised 11 December 2016; Accepted 25 December 2016; Published 1 February 2017

Academic Editor: Xian-Hua Han

Copyright © 2017 Hirokazu Nosato et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.