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Proceedings Paper

Texture feature based liver lesion classification
Author(s): Yeela Doron; Nitzan Mayer-Wolf; Idit Diamant; Hayit Greenspan
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Paper 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%.

Paper Details

Date Published: 24 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90353K (24 March 2014); doi: 10.1117/12.2043697
Show Author Affiliations
Yeela Doron, Tel-Aviv Univ. (Israel)
Nitzan Mayer-Wolf, Tel-Aviv Univ. (Israel)
Idit Diamant, Tel-Aviv Univ. (Israel)
Hayit Greenspan, Tel-Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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