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Proceedings Paper

Automatic quantification of crypt architecture in ex vivo gastrointestinal epithelium for high-resolution microendoscopic
Author(s): Hao Li; Luyao Wang; Hao Liu; Heng Xie; Yuqing Wu; Xingqun Zhao; Xin An; Guoxin Li
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Paper Abstract

High-resolution microendoscopy based on the fiber bundle has been showing immense potential to early detection of precancerous and cancerous lesions in gastrointestinal epithelium, especially for low-resource areas in China. However, obtaining clinical benefit from microendoscopic diagnosis usually remains in the hands of experts. Quantitative analysis focusing on computer-aided detection is therefore receiving attention as an attractive tool. In this paper, we present an automatic quantification method of crypts in gastrointestinal epithelium for high-resolution microendoscopic images, which is composed of four modules: filtering, contrast enhancement, crypt segmentation and morphologic quantification of crypts. The preliminary experiments on ex vivo image data indicate that the proposed method is effective for crypt segmentation from microendoscopic images with low-contrast, and quantitation of well-defined clinical features, which has a potential in future computer-aided diagnostic systems by revealing the morphologic characteristics of crypts at various clinical stages. The proposed method also enables instant processing. Thus, it may be a powerful tool for assisting endoscopists in real-time interpretation of high-resolution microendoscopic images, with high accuracy and consistent diagnosis. Furthermore, we are testing the method on larger gastrointestinal epithelium images and in vivo high-resolution microendoscopic images, and will integrate this work into a computer-aided diagnostic system.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080658 (9 August 2018); doi: 10.1117/12.2502831
Show Author Affiliations
Hao Li, Southeast Univ. (China)
Luyao Wang, Soochow Univ. (China)
Hao Liu, Southern Medical Univ. (China)
Heng Xie, Suzhou Infocus Vision Medical Technology Co., Ltd. (China)
Yuqing Wu, Suzhou Infocus Vision Medical Technology Co., Ltd. (China)
Xingqun Zhao, Southeast Univ. (China)
Xin An, Suzhou Infocus Vision Medical Technology Co., Ltd. (China)
Guoxin Li, Southern Medical Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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