Proceedings of the KSMRM Conference (대한자기공명의과학회:학술대회논문집)

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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The preprocessing effect using K-means clustering and merging algorithms in cardiac left ventricle segmentation

  • Cho, Ik-Hwan (Department of Electronic Engineering, College of Engineering, Inha University) ;
  • Do, Ki-Bum (Department of Electronic Engineering, College of Engineering, Inha University) ;
  • Oh, Jung-Su (Interdiciplinary Program of Medial and Biological Major, Seoul National University) ;
  • Song, In-Chan (Department of Diagnostic Radiology, Seoul National University Hospital) ;
  • Chang, Kee-Hyun (Department of Diagnostic Radiology, Seoul National University Hospital) ;
  • Jeong, Dong-Seok (Department of Electronic Engineering, College of Engineering, Inha University)
  • Published : 2002.11.01
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Abstract

Purpose： For quantitative analysis of the cardiac diseases, it is necessary to segment the left-ventricle(LV) in MR cardiac images. Snake or active contour model has been used to segment LV boundary. In using these models, however, the contour of the LV may not converge to the desirable one because the contour may fall into local minimum value due to image artifact in inner region of the LV Therefore, in this paper, we propose the new preprocessing method using K-means clustering and merging algorithms that can improve the performance of the active contour model.

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