Horng Ming-Huwi, Sun Yung-Nien, Lin Xi-Zhang
Department of Information Management, Nan Hua University, No. 32, Chung Keng Li, Dalin Chiayi, Taiwan, ROC.
Comput Med Imaging Graph. 2002 Jan-Feb;26(1):33-42. doi: 10.1016/s0895-6111(01)00029-5.
This paper introduces a new texture analysis method called texture feature coding method (TFCM) for classification of ultrasonic liver images. The TFCM transforms a gray-level image into a feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The TFNs obtained are used to generate a TFN histogram and a TFN co-occurrence matrix (CM), which produces texture feature descriptors for classification. Four conventional texture analysis methods that are gray-level CM, texture spectrum, statistical feature matrix and fractal dimension, are used also to classify liver sonography for comparison. The supervised maximum likelihood (ML) classifiers implemented by different type texture features are applied to discriminate ultrasonic liver images into three disease states that are normal liver, liver hepatitis and cirrhosis. The 30 liver sample images proven by needle biopsy are used to train the ML system that classify on a set of 90 test sample images. Experimental results show that the ML classifier together with TFCM texture features outperforms one with the four conventional methods with respect to classification accuracy.
本文介绍了一种名为纹理特征编码方法（TFCM）的新纹理分析方法，用于对肝脏超声图像进行分类。TFCM将灰度图像转换为特征图像，其中每个像素由TFCM编码的纹理特征数（TFN）表示。所获得的TFN用于生成TFN直方图和TFN共生矩阵（CM），从而产生用于分类的纹理特征描述符。还使用四种传统的纹理分析方法，即灰度CM、纹理谱、统计特征矩阵和分形维数，对肝脏超声检查进行分类以作比较。由不同类型纹理特征实现的监督最大似然（ML）分类器用于将肝脏超声图像区分为三种疾病状态，即正常肝脏、肝炎和肝硬化。经针吸活检证实的30幅肝脏样本图像用于训练对一组90幅测试样本图像进行分类的ML系统。实验结果表明，就分类准确率而言，结合TFCM纹理特征的ML分类器优于采用四种传统方法的分类器。
