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Proceedings Paper

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features
Author(s): Kenya Sasaki; Yoshihiro Mitani; Yusuke Fujita; Yoshihiko Hamamoto; Isao Sakaida
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Paper Abstract

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250U (8 February 2017); doi: 10.1117/12.2266914
Show Author Affiliations
Kenya Sasaki, Ube College (Japan)
Yoshihiro Mitani, Ube College (Japan)
Yusuke Fujita, Yamaguchi Univ. (Japan)
Yoshihiko Hamamoto, Yamaguchi Univ. (Japan)
Isao Sakaida, Yamaguchi Univ. (Japan)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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