1. Chien-Cheng Lee, Sz-Han Chen, Hong-Ming Tsai, Pau- Choo Chung, and Yu-Chun Chiang Department of Communications Engineering, Yuan Ze University Chungli, Taoyuan 320, Taiwan

2. Introduction The accurate decision rate estimated by using only simple visual interpretation of liver diseases is around 72%.

3. In this papper The diagnosis scheme includes two steps: features extraction classification Three kinds of liver diseases are identified: cyst, cavernous hepatoma Hemangioma

4. Features extraction Gabor filters have the ability to model the frequency and orientation sensitivity characteristic of the human visual system. The features are optimal in the sense of minimizing the joint two-dimensional uncertainty in space and frequency.

5. 2D Gabor filter Frequency: ψ Orientation: θ Bandwidth: σ

6. 2D Gabor filter 2-D convolution with image: The convolution is implemented using the mask of M×M sizes, which M is preferred to be an odd number.

7. 2D Gabor filter Energy Minimum Energy

8. Supervised diseases classification Support Vector Machines Train linear machines with margins Preprocessing the data to represent patterns in a high dimension with an appropriate nonlinear mapping Data from two categories can be linearly separable Find the separating plane with largest margin The larger the margin, the better generalization of the classifier

9. SVM

10. For linearly separable data set, the optimal separating hyper-planes can be defined as follows: where is a subset of the training patterns called Support Vectors (SVs).

11. SVM The coefficients and b are obtained by solving the optimization problem: The parameter C is a regularization parameter selected by the user. C corresponds to assigning a payment to the training errors.

12. Experiment The images is 512 × 512 with contrast media injection and the graylevel is stored at 12 bits per pixel, include 76 liver cysts 30 hepatomas 40 cavernous hemangiomas

14. Result