講演名 | 2009-05-28 Segmentation of Liver in Low-contrast Images Using K-Means Clustering and A Priori Knowledge , |
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抄録(英) | 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. |
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キーワード(英) | Liver segmentation / Low-contrast object segmentation / K-means clustering / CT image intensity analysis |
資料番号 | IE2009-27,PRMU2009-18,MI2009-18 |
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研究会 | PRMU |
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開催期間 | 2009/5/21(から1日開催) |
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申込み研究会 | Pattern Recognition and Media Understanding (PRMU) |
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本文の言語 | ENG |
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タイトル(英) | Segmentation of Liver in Low-contrast Images Using K-Means Clustering and A Priori Knowledge |
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キーワード(1)(和/英) | / Liver segmentation |
第 1 著者 氏名(和/英) | / Amir H. Foruzan |
第 1 著者 所属(和/英) | 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 |
発表年月日 | 2009-05-28 |
資料番号 | IE2009-27,PRMU2009-18,MI2009-18 |
巻番号(vol) | vol.109 |
号番号(no) | 64 |
ページ範囲 | pp.- |
ページ数 | 6 |
発行日 |