NDLI: Parametric Shape Modeling Using Deformable Superellipses for Segmentation of Medical Image

Content Provider	IEEE Xplore Digital Library
Author	Min Xiaoping Wang Boliang Huang Shaohui Dai Peishan
Copyright Year	2008
Description	Author affiliation: CS Dept., XMU, Xiamen (Min Xiaoping; Wang Boliang; Huang Shaohui)
Abstract	Medical image segmentation is a challenging task due to speckle noise, poor contrast, and especially absent boundary segments. One popular approach has been the use of deformable models. A parametric shape modeling using deformable superellipses for segmentation of medical image was proposed. Superellipses can represent the smooth and symmetrical contour perfectly, Most of anatomical structures having such characters can be approached by a superellipse. Prior shape information been got from a statistical modal analysis of a training set. This information was used to restrict the transformation of the surperellipse parameters. We used Hybrid Genetic Optimization Algorithm to find the optimal superellipse parameters, and snake algorithm embed with these shape information to segment the image. With shape guidance, this algorithm is less sensitive to initial contour placement and more robust even in the presence of large boundary gaps. The experiments show the efficiency of this method.
Starting Page	2673
Ending Page	2676
File Size	163990
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.1000
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deformable models Image segmentation Shape Modal analysis Anatomical structure Speckle Genetics Robustness Noise shaping Biomedical imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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