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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/2366

Title: Semiautomatic endocardial contour estimation in contrast Echocardiography sequences using guided local search
Authors: BANSOD, P
DESAI, UB
BURKULE, N
Keywords: boundary detection
image segmentation
agent
Issue Date: 2007
Publisher: INT INST INFORMATICS & SYSTEMICS
Citation: INT CONF ON CYBERNETICS AND INFORMATION TECHNOLOGIES, SYSTEMS AND APPLICATIONS/INT CONF ON COMPUTING, COMMUNICATIONS AND CONTROL TECHNOLOGIES, VOL 1,70-75
Abstract: We present a Guided Local Search (GLS) in multi-frame environment for detection of endocardial contours in short axis (SAX) contrast echo cardiographic image sequences. The proposed method requires minimal user intervention at the first frame of the image sequence for specifying the candidate points of the contour. The initial contour is approximated by fitting an ellipse in the region defined by the points specified. First level of regularization on the contour is made by radial gradient search with magnitude and direction. The second level of regularization makes use of GLS algorithm. The GLS with features and constraints for solution space and penalty terms overcome the optimization being trapped in local minima. The proposed method also incorporates the temporal information from the adjacent frames during the regularization process. The subsequent frames in sequence are initialized by the final contour of the previous frame. The algorithm developed was applied to 10 data sets over full cardiac cycle and the results were validated by comparing computer generated boundaries to those manually outlined by one expert. The maximum error in the contours detected was +/- 2.9mm. The application of guided local search algorithm proposed in this paper offers a fast and efficient method for semiautomatic segmentation of heart chambers in 2D contrast echocardiography sequences.
URI: http://dspace.library.iitb.ac.in/xmlui/handle/10054/15919
http://hdl.handle.net/100/2366
ISBN: 978-1-934272-07-7
Appears in Collections:Proceedings papers

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