Texture feature coding method for classification of liver sonography

研究成果: Article同行評審

83 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)33-42
頁數10
期刊Computerized Medical Imaging and Graphics
26
發行號1
DOIs
出版狀態Published - 2002 1月 2

All Science Journal Classification (ASJC) codes

  • 放射與超音波技術
  • 放射學、核子醫學和影像學
  • 電腦視覺和模式識別
  • 健康資訊學
  • 電腦繪圖與電腦輔助設計

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