Please use this identifier to cite or link to this item:
Title: Markovian level set for echocardiographic image segmentation
Authors: Cheng, Jierong
Foo, Say Wei
Issue Date: 2006
Source: Cheng, J.; & Foo, S. W. (2006). Markovian level set for echocardiographic image segmentation. IEEE International Symposium on Circuits and Systems. (pp. 5567-5570).
Abstract: Owing to the large amount of speckle noise and ill-defined edges present in echocardiographic images, computerbased boundary detection of the left ventricle (LV) has proved to be a challenging problem. In this paper, a Markovian level set method for boundary detection in long-axis echocardiographic images is proposed. It combines MRF model which makes use of local statistics with level set method which handles topological changes, to detect a continuous and smooth LV boundary. Experimental results show that high accuracy is achieved with the proposed method. The experimental results are also compared with two related MRF-based methods to demonstrate its superiority.
DOI: 10.1109/ISCAS.2006.1693896
Rights: © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
C94-2067ISCAS2006-ChengJ.pdf232.94 kBAdobe PDFThumbnail

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.