DocumentCode
178505
Title
Segmenting Reddish Lesions in Capsule Endoscopy Images Using a Gastrointestinal Color Space
Author
Hai Vu ; Echigo, Tomio ; Imura, Yuma ; Yanagawa, Yukiko ; Yagi, Yasushi
Author_Institution
Int. Res. Inst. MICA, Hanoi Univ. of Sci. & Technol., Hanoi, Vietnam
fYear
2014
fDate
24-28 Aug. 2014
Firstpage
3263
Lastpage
3268
Abstract
Segmenting reddish lesions in capsule endoscopy (CE) images is an initial step for further computer-assisted applications such as image enhancement, abnormal measurement/tracking, and so on. In this paper, we propose an automatic segmentation method that is successful even with CE image including unclear reddish lesions. To obtain this, the proposed method seeks good features to discriminate the reddish lesions from normal tissues. For implementations, we first extract only meaningful regions in a CE image through a pre-segmentation step. The proposed features then are extracted for the meaningful regions in stead of the whole image. We approaches segmentation task through considering a statistical operator for the extracted features, that is local mean image. Candidates of the abnormal regions are located in the local mean image with assistants of a diffusion process. Evaluations in the experiments confirm effectiveness of the proposed method with both qualitative and quantitative measurement.
Keywords
biodiffusion; endoscopes; feature extraction; image colour analysis; image segmentation; medical image processing; statistical analysis; CE image; abnormal measurement; automatic segmentation method; capsule endoscopy images; computer-assisted applications; diffusion process; feature extraction; gastrointestinal color space; image enhancement; local mean image; reddish lesion segmentation; statistical operator; Educational institutions; Endoscopes; Feature extraction; Hemorrhaging; Image color analysis; Image segmentation; Lesions;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location
Stockholm
ISSN
1051-4651
Type
conf
DOI
10.1109/ICPR.2014.562
Filename
6977274
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