An effective method for cirrhosis recognition based on multi-feature fusion

Yameng Chen; Gengxin Sun; Yiming Lei; Jinpeng Zhang

doi:10.1117/12.2304733

10 April 2018 An effective method for cirrhosis recognition based on multi-feature fusion

Yameng Chen, Gengxin Sun, Yiming Lei, Jinpeng Zhang

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061518 (2018) https://doi.org/10.1117/12.2304733
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

Citation Download Citation

Yameng Chen, Gengxin Sun, Yiming Lei, and Jinpeng Zhang "An effective method for cirrhosis recognition based on multi-feature fusion", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061518 (10 April 2018); https://doi.org/10.1117/12.2304733

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