NDLI: Probabilistic shape-based segmentation using level sets

Content Provider	IEEE Xplore Digital Library
Author	Aslan, M.S. Abdelmunim, H. Farag, A.A.
Copyright Year	2011
Description	Author affiliation: Computer Vision and Image Processing Lab, University of Louisville, KY, 40299, USA (Aslan, M.S.; Farag, A.A.) \|\| Computer and Systems Engineering Department, Ain Shams University, Cairo, Egypt (Abdelmunim, H.)
Abstract	In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation of the training shapes. The resulting probability density function is used to embed the shape model into the image domain with a new energy minimization solution. Our method' s invariance to parameter initialization is evaluated through validation, and various synthetic and clinical shape registration examples are implemented. Experiments show that our proposed algorithm enhances the conventional global registration results, overcomes segmentation challenges, and is robust under various noise levels, severe occlusions, and missing parts.
Starting Page	1372
Ending Page	1377
File Size	1607057
Page Count	6
File Format	PDF
ISBN	9781467300629
e-ISBN	9781467300636
DOI	10.1109/ICCVW.2011.6130411
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image segmentation Accuracy Shape Computed tomography Level set Noise
Content Type	Text
Resource Type	Article

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