Analysis of fluctuation for pixel-pair distance in co-occurrence matrix applied to ultrasonic images for diagnosis of liver fibrosis
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- ISONO Hiroshi
- Graduate School of Science and Technology, Tokyo Institute of Technology
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- HIRATA Shinnosuke
- Graduate School of Science and Technology, Tokyo Institute of Technology
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- YAMAGUCHI Tadashi
- Research Center for Frontier Medical Engineering, Chiba University
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- HACHIYA Hiroyuki
- Graduate School of Science and Technology, Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- 肝線維化診断に向けた超音波画像の同時生起行列における画素間距離に対する揺らぎ応答解析
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Description
<p>Purpose: Chronic liver disease requires careful follow-up during long-term treatment, and development of a quantitative diagnosis method for liver fibrosis based on an ultrasonic imaging system is highly desired. Methods: Texture analysis using a co-occurrence matrix was applied to both clinical and simulated ultrasonic images of fibrotic livers. A sequence of matrices was generated for pixel-pair distance, r, and texture feature contrast was chosen to examine the response to r in combination with statistical analysis of echo amplitude distribution using a multi-Rayleigh model. Results: The contrast converged with a larger value and fluctuated more significantly in response to r as fibrosis progressed in both the clinical and simulated ultrasonic images. The convergent value rapidly increased at the early stage of fibrosis, and the fluctuation became larger at the advanced stage of fibrosis. Analysis using simulated ultrasonic images with a known fibrous tissue structure theoretically clarified the relationship between contrast behavior and fibrosis progression. Conclusion: It was revealed that contrast convergent value and contrast fluctuation provided information on the fibrous tissue structure, and they are expected to be used for quantitative diagnosis of the degree of liver fibrosis.</p>
Journal
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- Choonpa Igaku
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Choonpa Igaku 48 (1), 3-15, 2021
The Japan Society of Ultrasonics in Medicine
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Keywords
Details 詳細情報について
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- CRID
- 1391131406304995712
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- NII Article ID
- 130007967684
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- ISSN
- 18819311
- 13461176
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed