SVM-based ultrasonic medicine image diagnosis

Jun Zhang; Xiaomao Liu; Jianguo Liu; Fuyuan Peng; Jinwen Tian; Ying Wang; Wenjun Zhang; Mingxing Xie

doi:10.1117/12.440251

18 September 2001 SVM-based ultrasonic medicine image diagnosis

Jun Zhang, Xiaomao Liu, Jianguo Liu, Fuyuan Peng, Jinwen Tian, Ying Wang, Wenjun Zhang, Mingxing Xie

Proceedings Volume 4549, Medical Image Acquisition and Processing; (2001) https://doi.org/10.1117/12.440251
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

The technique of support vector machines (SVM's) has been used as a new method for solving classification, regression, time series prediction and function estimation problems with many successful applications. In this paper, we use SVM to solve the problem of classification for ultrasonic medicine image. We use statistical characteristics of the interesting part in an ultrasonic image to aid a doctor to give a correct diagnosis. The procedure is: first, to extract a number of small sampling regions in the interesting part; second, to calculate a series of moments about those sampling regions; third, to decide whether the interesting part of the organ is normal or abnormal according to the analyses of the series of moments based on SVM. SVM's structural risk minimization principle is the guarantee that the diagnosis has the minimum mistake probability. The diagnosis based on SVM is optimal from the viewpoint of structural risk minimization principle. It is hoped that the results presented here will be helpful to the diagnosis based on ultrasonic medicine image.

Citation Download Citation

Jun Zhang, Xiaomao Liu, Jianguo Liu, Fuyuan Peng, Jinwen Tian, Ying Wang, Wenjun Zhang, and Mingxing Xie "SVM-based ultrasonic medicine image diagnosis", Proc. SPIE 4549, Medical Image Acquisition and Processing, (18 September 2001); https://doi.org/10.1117/12.440251

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