Automated Classification of Liver Disorders using Ultrasound Images

被引：0

作者：

Fayyaz ul Amir Afsar Minhas

Durre Sabih

Mutawarra Hussain

机构：

[1] Colorado State University,Department of Computer Science

[2] Multan Institute of Nuclear Medicine and Radiotherapy (MINAR),Department of Computer Science

[3] Pakistan Institute of Engineering and Applied Sciences,undefined

来源：

Journal of Medical Systems | 2012年 / 36卷

关键词：

Ultrasound; Fatty liver disease; Heterogeneous liver; Wavelet packet transform; Support vector machines;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents a novel approach for detection of Fatty liver disease (FLD) and Heterogeneous liver using textural analysis of liver ultrasound images. The proposed system is able to automatically assign a representative region of interest (ROI) in a liver ultrasound which is subsequently used for diagnosis. This ROI is analyzed using Wavelet Packet Transform (WPT) and a number of statistical features are obtained. A multi-class linear support vector machine (SVM) is then used for classification. The proposed system gives an overall accuracy of ~95% which clearly illustrates the efficacy of the system.

引用

页码：3163 / 3172

页数：9

共 19 条

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