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http://dx.doi.org/10.15701/kcgs.2016.22.4.11

Automatic Segmentation of Renal Parenchyma using Graph-cuts with Shape Constraint based on Multi-probabilistic Atlas in Abdominal CT Images  

Lee, Jaeseon (Department of Software Convergence, Seoul Women's University)
Hong, Helen (Department of Software Convergence, Seoul Women's University)
Rha, Koon Ho (Department of Urology, Yonsei University College of Medicine)
Publication Information
Journal of the Korea Computer Graphics Society / v.22, no.4, 2016 , pp. 11-19 More about this Journal
Abstract
In this paper, we propose an automatic segmentation method of renal parenchyma on abdominal CT image using graph-cuts with shape constraint based on multi-probabilistic atlas. The proposed method consists of following three steps. First, to use the various shape information of renal parenchyma, multi-probabilistic atlas is generated by cortex-based similarity registration. Second, initial seeds for graph-cuts are extracted by maximum a posteriori (MAP) estimation and renal parenchyma is segmented by graph-cuts with shape constraint. Third, to reduce alignment error of probabilistic atlas and increase segmentation accuracy, registration and segmentation are iteratively performed. To evaluate the performance of proposed method, qualitative and quantitative evaluation are performed. Experimental results show that the proposed method avoids a leakage into neighbor regions with similar intensity of renal parenchyma and shows improved segmentation accuracy.
Keywords
Renal parenchymal segmentation; Graph-cut; Multi-probabilistic atlas; Similarity registration;
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Times Cited By KSCI : 1  (Citation Analysis)
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