Presentation | 2009-05-28 Segmentation of Liver in Low-contrast Images Using K-Means Clustering and A Priori Knowledge Amir H. Foruzan, Yen-Wei Chen, Reza A. Zoroofi, Akira Furukawa, Yoshinobu Sato, Masatoshi Hori, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we address the problem of liver segmentation from low-contrast CT-scan datasets. We consider a 'Gaussian Mixture' model for intensity distribution of liver and non-liver tissues and use a priori knowledge to find statistical parameters of liver. Then, we apply thresholding in a narrow range round the mean of each component of liver's mixture model to find liver candidate pixels. K-means clustering is used to discriminate between liver and non-liver index pixels. Then, we establish a liver probability map by assigning a probability number to each pixel of the original image. We can find initial boundary for liver by thresholding the image map and use it as the input to a 'Geodesic Active Contour' algorithm to find final liver boundary. We tested the proposed algorithm on non-contrast liver datasets. Assessment of the results proves that the proposed method is both robust, resistant to leakage, requires minimum level of interaction. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Liver segmentation / Low-contrast object segmentation / K-means clustering / CT image intensity analysis |
Paper # | IE2009-27,PRMU2009-18,MI2009-18 |
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Committee | PRMU |
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Conference Date | 2009/5/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Segmentation of Liver in Low-contrast Images Using K-Means Clustering and A Priori Knowledge |
Sub Title (in English) | |
Keyword(1) | Liver segmentation |
Keyword(2) | Low-contrast object segmentation |
Keyword(3) | K-means clustering |
Keyword(4) | CT image intensity analysis |
1st Author's Name | Amir H. Foruzan |
1st Author's Affiliation | College of Information Science and Engineering, Ritsumeikan University:Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran() |
2nd Author's Name | Yen-Wei Chen |
2nd Author's Affiliation | College of Information Science and Engineering, Ritsumeikan University |
3rd Author's Name | Reza A. Zoroofi |
3rd Author's Affiliation | Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran |
4th Author's Name | Akira Furukawa |
4th Author's Affiliation | Department of Radiology, Shiga University of Medical Science |
5th Author's Name | Yoshinobu Sato |
5th Author's Affiliation | Division of Image Analysis, Graduate School of Medicine, Osaka University |
6th Author's Name | Masatoshi Hori |
6th Author's Affiliation | Department of Radiology, Graduate School of Medicine, Osaka University |
Date | 2009-05-28 |
Paper # | IE2009-27,PRMU2009-18,MI2009-18 |
Volume (vol) | vol.109 |
Number (no) | 64 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |