Copyright © 2006 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Pattern Recognition |
Texture Classification for Liver Tissues from Ultrasonic B-Scan Images Using Testified PNN
1 The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263–8522 Japan. E-mail: jmlu{at}faculty.chiba-u.jp
Visual criteria for diagnosing liver diseases, such as cirrhosis, from ultrasound images can be assisted by computerized texture classification. This paper proposes a system applying a PNN (Pyramid Neural Network) for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. In this study, we propose a multifractal-dimensions method to select the patterns for the training set and the validation sets. A modified box-counting algorithm is used to calculate the dimensions of the B-scan images. FDWT (Fast Discrete Wavelet Transform) is applied for feature extraction during the preprocessing. The structure of the proposed neural network is testified by training and validation sets by cross-validation method. The performance of the proposed system and a system based on the conventional multilayer network architecture is compared. The results show that, compared with the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by efficiently utilizing the lower layer of the neural network.
Key Words: fractal dimension, multifractal, DBC (Differential Boxing-Counting), FDWT, PNN (Pyramid Neural Network), cross-validation
Manuscript received September 8, 2005. Manuscript revised March 25, 2006.