24 March 2014 Texture feature based liver lesion classification
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Abstract
Liver lesion classification is a difficult clinical task. Computerized analysis can support clinical workflow by enabling more objective and reproducible evaluation. In this paper, we evaluate the contribution of several types of texture features for a computer-aided diagnostic (CAD) system which automatically classifies liver lesions from CT images. Based on the assumption that liver lesions of various classes differ in their texture characteristics, a variety of texture features were examined as lesion descriptors. Although texture features are often used for this task, there is currently a lack of detailed research focusing on the comparison across different texture features, or their combinations, on a given dataset. In this work we investigated the performance of Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), Gabor, gray level intensity values and Gabor-based LBP (GLBP), where the features are obtained from a given lesion`s region of interest (ROI). For the classification module, SVM and KNN classifiers were examined. Using a single type of texture feature, best result of 91% accuracy, was obtained with Gabor filtering and SVM classification. Combination of Gabor, LBP and Intensity features improved the results to a final accuracy of 97%.
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Yeela Doron, Nitzan Mayer-Wolf, Idit Diamant, Hayit Greenspan, "Texture feature based liver lesion classification", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90353K (24 March 2014); doi: 10.1117/12.2043697; https://doi.org/10.1117/12.2043697
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