Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines
A computer-aided diagnosis (CAD) of X-ray Computed Tomography (CT) liver images with contrast agent injection is presented. Regions of interests (ROIs) on CT liver images are defined by experienced radiologists. For each ROI, texture features based on first order statistics (FOS), spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and gray level difference matrix (GLDM) are extracted. Support vector machine (SVM) is originally for binary classification. In order to classify hepatic tissues from CT images into primary hepatic carcinoma, hemangioma and normal liver, we utilize two methods to construct multiclass SVMs: one-against-all (OAA), one-against-one (OAO) and compare their performance. The result shows that a total accuracy rate of 97.78% is obtained with the multiclass SVM using the OAO method. Our study has some practical significance for clinical diagnosis.
KeywordsCT liver images Texture feature Multiclass classification Support vector machine (SVM)
Unable to display preview. Download preview PDF.
- 4.Gletsos, M., Mougiakakou, S.G., Matsopoulos, G.K., Nikita, K.S., Nikita, A.S., Kelekis, D.: A Computer-aided Diagnostic System to Characterize CT Focal Liver Lesions: Design and Optimization of a Neural Network Classifier. IEEE Transactions on Information Technology in Biomedicine 7, 153–162 (2003)CrossRefGoogle Scholar
- 5.Lambrou, T., Linney, A.D., Todd-Pokropek, A.: Wavelet Transform Analysis and Classification of the Liver from Computed Tomography Datasets. In: IEEE 5th International Special Topic Conference on Information Technology in Biomedicine (2006)Google Scholar
- 6.Mala, K., Sadasivam, V., Alagappan, S.: Neural Network Based Texture Analysis of Liver Tumor from Computed Tomography Images. International Journal of Biomedical Sciences 2, 33–40 (2006)Google Scholar
- 8.Mir, A.H., Hanmandlu, M., Tandon, S.N.: Texture Analysis of CT Images. In: IEEE International Conference of the Engineering in Medicine and Biology Society, vol. 14, pp. 781–786 (1995)Google Scholar
- 9.Valavanis, I.K., Mougiakakou, S.G., Nikita, A., Nikita, K.S.: Evaluation of Texture Features in Hepatic Tissue Characterization from Non-enhanced CT Images. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3741–3744 (2007)Google Scholar
- 13.Liu, Y., Zheng, Y.F.: One-Against-All Multi-Class SVM Classification Using Reliability Measures. In: 2005 IEEE International Joint Conference on Neural Networks, pp. 849–854. IEEE Press, New York (2005)Google Scholar
- 15.Friedman, J.H.: Another Approach to Polychotomous Classification. Technical report, Stanford Department of Statistics, http://www-stat.stanford.edu/reports/friedman/poly.ps.Z