Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 37, Issue 10, Pages 6737-6741Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.02.067
Keywords
Fuzzy support vector machine; Gray level co-occurrence matrix; Textural features; Ultrasonography classification
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Funding
- South China Normal University
- Guangdong financial education [(2008) 342]
- South China University of Technology
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In this study, we present an application of fuzzy support vector machine (FSVM) and image processing techniques for identifying liver tumor, including malignant and benign tumors. The gray level co-occurrence matrix (GLCM) matrices were utilized to evaluate the texture features of the regions of interest (ROI) of sonography in our experiment. Five textural features: energy, contrast, correlation, entropy, and homogeneity were extracted from the liver segmented images and analyzed using the texture average of four directions (0, 45, 90, 135) and distance, delta = 6. The proposed system adopts the FSVM to distinguish between malignant and benign tumor cases more efficiently than support vector machine (SVM). The Gaussian RBF kernel has been used be more suitable for the application of identifying liver tumor from B-Mode ultrasound images than polynomial learning machine kernel and linear network kernel. The values of the parameters gamma (g) and regularization parameter (C) have been selected as 0.29 and 4.31 x 10(3). respectively. Via testing over 200 test cases by using RBF kernel, an overall accuracy of 97.0% has been received by the proposed FSVM algorithm. FSVM (A(Z) = 0.984 +/- 0.014) obtain a better result than SVM (A(Z) = 0.963 +/- 0.017) in recognition. It is demonstrated that the proposed FSVM algorithm and GLCM texture features technique are feasible and excellent in ultrasonography classification of liver tumor. (C) 2010 Published by Elsevier Ltd.
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