JACIII Vol.11 No.4 pp. 396-402
doi: 10.20965/jaciii.2007.p0396


Classification of Liver Disease from CT Images Using a Support Vector Machine

Chien-Cheng Lee*, Sz-Han Chen*, and Yu-Chun Chiang**

*Department of Communications Engineering, Yuan Ze University, Chungli, Taoyuan 320, Taiwan

**Department of Mechanical Engineering, Yuan Ze University, Chungli, Taoyuan 320, Taiwan

April 30, 2006
August 5, 2006
April 20, 2007
SVM, co-occurrence matrix, liver cyst, hepatoma, hemangioma

We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art machine learning. The classifier is a part of computer-aided diagnosis (CADx), which assists radiologists in accurately diagnosing liver disease. We formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as a supervised learning problem, and apply SVM to classifying the diseases using gray level and co-occurrence matrix features and region-based shape descriptors, calculated from regions of interest (ROIs), as input. Significant features of ROI enable us to simplify SVM input space and to feed the SVM representative information. By simplifying and clarifying the diagnosis process, we separate the classification of liver disease into hierarchical multiclass classification. We use the receiver operating characteristic (ROC) curve to evaluate diagnosis performance, demonstrating the classifier’s good performance.

Cite this article as:
C. Lee, S. Chen, and Y. Chiang, “Classification of Liver Disease from CT Images Using a Support Vector Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 396-402, 2007.
Data files:
  1. [1] B. B. Gosnik, S. K. Lemon, W. Scheible, and G. R. Leupold “Accuracy of ultrasonography in diagnosis of hepatocellular disease,” AJR, Vol.133, pp. 19-23, 1979.
  2. [2] K. J. Foster, K. C. Dewbury, A. H. Griffith, and R. Wright, “The accuracy of ultrasound in the detection of fatty infiltration of the liver,” Br. J. Radiol., Vol.53, pp. 440-442, 1980.
  3. [3] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man. Cybern., Vol.SMC-3, No.6, pp. 610-621, Jun., 1973.
  4. [4] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Trans. Pattern Anal. Machine Intell., Vol.18, No.8, pp. 837-842, Aug., 1996.
  5. [5] D. A. Clausi and M. E. Jernigan, “Designing Gabor filters for optimal texture separability,” Pattern Recognit., Vol.33, pp. 1835-1849, 2000.
  6. [6] A. Kumar and G. K. H. Pang, “Defect detection in texture materials using Gabor filters,” IEEE Trans. Ind. Applicat., Vol.38, No.2, pp. 425-440, Mar./Apr., 2002.
  7. [7] P. Rivaz and N. Kingsbury, “Fast segmentation using level set curves of complex wavelet surfaces,” in Proc. IEEE Int. Conf. on Image Processing, Vancouver, BC, Canada, pp. 592-595, Sep., 2000.
  8. [8] Y. M. Kadah, A. A. Farag, J. M. Zurada, A. M. Badawi, and A.-B. M. Youssef, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Trans. Medical Imaging, Vol.15, No.4, pp. 466-478, Aug., 1996.
  9. [9] Y. N. Sun, M.-H. Horng, X.-Z. Lin, and J.-Y. Wang, “Ultrasound image analysis for liver diagnosis: A noninvasive alternative to determine liver disease,” IEEE Eng. Med. Biol. Mag., pp. 93-101, Nov./Dec., 1996.
  10. [10] E.-L. Chen, P.-C. Chung, C.-L. Chen, H.-M. Tsai, and C.-I. Chang, “An automatic diagnostic system for CT liver image classification,” IEEE Trans. Biomedical Engineering, Vol.45, No.6, pp. 783-794, Jun., 1998.
  11. [11] M. Gletsos et al., “A computer-aided diagnostic system to characterize CT focal liver eesions: design and optimization of a neural network classifier,” IEEE Transactions on Information Technology in Biomedicine, Vol.7, No.3, pp. 153-162, Sep., 2003.
  12. [12] B. Schölkopf, K.-K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing support vector machines with Gaussian kernels to radial basis function classifiers,” IEEE Trans. Signal Processing, Vol.45, No.11, pp. 2758-2765, Nov., 1997.
  13. [13] V. Vapnik, “The Nature of Statistical Learning Theory,” New York: Springer-Verlag, 1995.
  14. [14] K. I. Kim, K. Jung, S. H. Park, and H. J. Kim, “Support vector machines for texture classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.24, No.11, pp. 1542-1550, Nov., 2002.
  15. [15] M. Sonka, V. Hlavac, and R. Boyle, “Image Processing, Analysis and Machine Vision,” New York, Chapman and Hall, 1993.
  16. [16] W. K. Pratt, “Digital Image Processing,” New York, Wiley, 2001.
  17. [17] C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans Neural Networks, Vol.13, No.2, pp. 415-425, Nov., 2002.
  18. [18] D. Anguita, S. Ridella, and D. Sterpi, “A new method for multiclass support vector machines,” in Proc. of IEEE Int. Joint Conf. on neural networks, IJCNN 2004, Budapest, Hungary, pp. 25-29, July, 2004.
  19. [19] C. C. Chang and C. J. Lin, “Libsvm: a library for support vector machines,” 2001, available at˜cjlin/libsvm.

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