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Preprocessing Effect by Using k-means Clustering and Merging .Algorithms in MR Cardiac Left Ventricle Segmentation  

Ik-Hwan Cho (Department of Electronic Engineering, College of Engineering, Inha University)
Jung-Su Oh (Interdisciplinary Program of Biomedical Engineering, Seoul National University)
Kyong-Sik Om (Institute of Medical Biological Engineering, Medical Research Center Seoul National University Hospital)
In-Chan Song (Department of Radiology, Seoul National University Hospital)
Kee-Hyun Chang (Department of Radiology, Seoul National University Hospital)
Dong-Seok Jeong (Department of Electronic Engineering, College of Engineering, Inha University)
Publication Information
Journal of Biomedical Engineering Research / v.24, no.2, 2003 , pp. 55-60 More about this Journal
Abstract
For quantitative analysis of the cardiac diseases. it is necessary to segment the left-ventricle (LY) in MR (Magnetic Resonance) cardiac images. Snake or active contour model has been used to segment LV boundary. However, the contour of the LV front these models may not converge to the desirable one because the contour may fall into local minimum value due to image artifact inside of the LY Therefore, in this paper, we Propose the Preprocessing method using k-means clustering and merging algorithms that can improve the performance of the active contour model. We verified that our proposed algorithm overcomes local minimum convergence problem by experiment results.
Keywords
Magnetic Resonance Imaging; Segmentation; K-means algorithm; Merging algorithm; Cardiac; Snake model;
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