New method for texture classification based on local surface structure and its application to ultrasonic liver images

Ming Huwi Horng, Yung-Nien Sun, Xi-Zhang Lin, Jen Ya Wang

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the classification of ultrasonic liver images is presented. By combining texture features extracted by two methods, including co-occurrence matrix and local texture structure frequency, the system can classify three liver states, which are normal liver, hepatitis and cirrhosis. The co-occurrence method extracts texture features which represent gray level variation of pixel pair under a specific spatial relationship. The local texture structure frequency, we have proposed new texture descriptor, extract local surface structure of a small texture unit. Several texture features are derived from this texture frequency, which include coarseness, homogeneity, structure stability, etc., A forward sequential search process is adopted to look for the most useful texture features from co-occurrence matrix and our proposed method. These textures features extracted from the two methods are then fed into a probabilistic neural network to classify the liver states. The system has been implemented on a Sun sparc II workstation and tested with sixty ultrasonic images. The correct classification rate is around 88%. These results suggest that the selected texture features are effective for the classification of liver echotexture.

Original languageEnglish
Pages (from-to)491-498
Number of pages8
JournalBiomedical Engineering - Applications, Basis and Communications
Volume7
Issue number5
Publication statusPublished - 1995

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Bioengineering

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