Texture feature coding method for classification of liver sonography

Ming Huwi Horng, Yung-Nien Sun, Xi-Zhang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Liver sonography is a widely used noninvasive diagnostic tool. Analyzing histology changes in sonograms provides a means of diagnosing and monitoring chronic liver diseases. Nonetheless, conventional ultrasonography is still qualitative. To improve reliability of liver diagnosis, quantitative image analysis is highly desirable for the assessment of various liver states. In this paper, a novel approach, called Texture Feature Coding Method (TFCM) is presented for texture classification of liver sonography, more specifically, classification of normal liver, hepatitis and cirrhosis. TFCM is a texture analysis technique based on gray-level gradient variations in a 3x3 texture unit. It transforms an image into a texture feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The obtained texture feature numbers are then used to generate a TFN histogram and a TFN co-occurrence matrix which will produce texture feature descriptors. By coupling with a supervised maximum likelihood (ML) classifier, these descriptors form a classification system to discriminate the three above-mentioned liver classes. The TFCM-supervised ML system is trained by 30 liver samples proven by biopsy and tested on a set of 90 samples. The results show that the designed TFN-supervised ML system performs better than do existing techniques, and the correct classification rate can reach as high as 83.3%.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings
EditorsBernard Buxton, Roberto Cipolla
PublisherSpringer Verlag
Pages209-218
Number of pages10
ISBN (Print)3540611223, 9783540611226
Publication statusPublished - 1996 Jan 1
Event4th European Conference on Computer Vision, ECCV 1996 - Cambridge, United Kingdom
Duration: 1996 Apr 151996 Apr 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1064
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th European Conference on Computer Vision, ECCV 1996
CountryUnited Kingdom
CityCambridge
Period96-04-1596-04-18

Fingerprint

Ultrasonography
Texture Feature
Liver
Textures
Coding
Maximum Likelihood
Maximum likelihood
Descriptors
Co-occurrence Matrix
Texture Classification
Histology
Texture Analysis
Quantitative Analysis
Image Analysis
Histogram
Biopsy
Texture
Diagnostics
Pixel
Classifier

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Horng, M. H., Sun, Y-N., & Lin, X-Z. (1996). Texture feature coding method for classification of liver sonography. In B. Buxton, & R. Cipolla (Eds.), Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings (pp. 209-218). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1064). Springer Verlag.
Horng, Ming Huwi ; Sun, Yung-Nien ; Lin, Xi-Zhang. / Texture feature coding method for classification of liver sonography. Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings. editor / Bernard Buxton ; Roberto Cipolla. Springer Verlag, 1996. pp. 209-218 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Liver sonography is a widely used noninvasive diagnostic tool. Analyzing histology changes in sonograms provides a means of diagnosing and monitoring chronic liver diseases. Nonetheless, conventional ultrasonography is still qualitative. To improve reliability of liver diagnosis, quantitative image analysis is highly desirable for the assessment of various liver states. In this paper, a novel approach, called Texture Feature Coding Method (TFCM) is presented for texture classification of liver sonography, more specifically, classification of normal liver, hepatitis and cirrhosis. TFCM is a texture analysis technique based on gray-level gradient variations in a 3x3 texture unit. It transforms an image into a texture feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The obtained texture feature numbers are then used to generate a TFN histogram and a TFN co-occurrence matrix which will produce texture feature descriptors. By coupling with a supervised maximum likelihood (ML) classifier, these descriptors form a classification system to discriminate the three above-mentioned liver classes. The TFCM-supervised ML system is trained by 30 liver samples proven by biopsy and tested on a set of 90 samples. The results show that the designed TFN-supervised ML system performs better than do existing techniques, and the correct classification rate can reach as high as 83.3{\%}.",
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Horng, MH, Sun, Y-N & Lin, X-Z 1996, Texture feature coding method for classification of liver sonography. in B Buxton & R Cipolla (eds), Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1064, Springer Verlag, pp. 209-218, 4th European Conference on Computer Vision, ECCV 1996, Cambridge, United Kingdom, 96-04-15.

Texture feature coding method for classification of liver sonography. / Horng, Ming Huwi; Sun, Yung-Nien; Lin, Xi-Zhang.

Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings. ed. / Bernard Buxton; Roberto Cipolla. Springer Verlag, 1996. p. 209-218 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1064).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Liver sonography is a widely used noninvasive diagnostic tool. Analyzing histology changes in sonograms provides a means of diagnosing and monitoring chronic liver diseases. Nonetheless, conventional ultrasonography is still qualitative. To improve reliability of liver diagnosis, quantitative image analysis is highly desirable for the assessment of various liver states. In this paper, a novel approach, called Texture Feature Coding Method (TFCM) is presented for texture classification of liver sonography, more specifically, classification of normal liver, hepatitis and cirrhosis. TFCM is a texture analysis technique based on gray-level gradient variations in a 3x3 texture unit. It transforms an image into a texture feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The obtained texture feature numbers are then used to generate a TFN histogram and a TFN co-occurrence matrix which will produce texture feature descriptors. By coupling with a supervised maximum likelihood (ML) classifier, these descriptors form a classification system to discriminate the three above-mentioned liver classes. The TFCM-supervised ML system is trained by 30 liver samples proven by biopsy and tested on a set of 90 samples. The results show that the designed TFN-supervised ML system performs better than do existing techniques, and the correct classification rate can reach as high as 83.3%.

AB - Liver sonography is a widely used noninvasive diagnostic tool. Analyzing histology changes in sonograms provides a means of diagnosing and monitoring chronic liver diseases. Nonetheless, conventional ultrasonography is still qualitative. To improve reliability of liver diagnosis, quantitative image analysis is highly desirable for the assessment of various liver states. In this paper, a novel approach, called Texture Feature Coding Method (TFCM) is presented for texture classification of liver sonography, more specifically, classification of normal liver, hepatitis and cirrhosis. TFCM is a texture analysis technique based on gray-level gradient variations in a 3x3 texture unit. It transforms an image into a texture feature image in which each pixel is represented by a texture feature number (TFN) coded by TFCM. The obtained texture feature numbers are then used to generate a TFN histogram and a TFN co-occurrence matrix which will produce texture feature descriptors. By coupling with a supervised maximum likelihood (ML) classifier, these descriptors form a classification system to discriminate the three above-mentioned liver classes. The TFCM-supervised ML system is trained by 30 liver samples proven by biopsy and tested on a set of 90 samples. The results show that the designed TFN-supervised ML system performs better than do existing techniques, and the correct classification rate can reach as high as 83.3%.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

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Horng MH, Sun Y-N, Lin X-Z. Texture feature coding method for classification of liver sonography. In Buxton B, Cipolla R, editors, Computer Vision – ECCV 1996 - 4th European Conference on Computer Vision, Proceedings. Springer Verlag. 1996. p. 209-218. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).