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Development of Diagnostic Classifier for Ultrasound Liver Lesion Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 52 - Number 18 |
| Year of Publication: 2012 |
| Authors: V. Ulagamuthalvi, D. Sridharan |
10.5120/8300-1681
|
V. Ulagamuthalvi, D. Sridharan . Development of Diagnostic Classifier for Ultrasound Liver Lesion Images. International Journal of Computer Applications. 52, 18 ( August 2012), 12-15. DOI=10.5120/8300-1681
Abstract
Liver cancer is the fifth most common cancer worldwide in men and eighth in women, and is one of the few cancers still on the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal, benign and malignant liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non invasive method.
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