Abstract—Ultrasound images are widely used for diagnosis of liver cirrhosis. In liver cirrhosis classification on M-mode ultrasound images, the use of higher order local auto-correlation (HLAC) features has been shown to be effective. In the previous study, we used the traditional 25 dimensional HLAC features. The 25 HLAC features are made by 25 mask patterns with up to 0th, 1st, and 2nd-order. On the other hand, there exists an extension of HLAC features. The extended HLAC features were shown to be more effective when higher-order HLAC features were used. Therefore, by the use of the extended HLAC features, we expected the liver cirrhosis classification performance to improve. However, the effectiveness of the extended HLAC features to classify the liver cirrhosis images is not clear. In this paper, more effectively to classify liver cirrhosis M-mode ultrasound images, we examine the performance of extended HLAC features.
Index Terms—Liver cirrhosis classification, M-mode ultrasound images, HLAC features, extended HLAC features.
Yoshihiro Mitani is with the Department of Intelligent System Engineering, National Institute of Technology, Ube College, Ube, Japan (e-mail: mitani@ube-k.ac.jp).
Yusuke Fujita, Yoshihiko Hamamoto, and Isao Sakaida are with Yamaguchi University, Ube, Japan.
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Cite:Yoshihiro Mitani, Yusuke Fujita, Yoshihiko Hamamoto, and Isao Sakaida, "Classification of Liver Cirrhosis on m-Mode Ultrasound Images by Extended Higher Order Local Autocorrelation Features," International Journal of Computer Theory and Engineering vol. 8, no. 2, pp. 167-170, 2016.
