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Sparse Texture Active Contour

Institution:
Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA. gaoyi@bwh.harvard.edu
Publisher:
IEEE Engineering in Medicine and Biology Society
Publication Date:
Oct-2013
Journal:
IEEE Trans Image Process
Volume Number:
22
Issue Number:
10
Pages:
3866-78
Citation:
IEEE Trans Image Process. 2013 Oct;22(10):3866-78.
PubMed ID:
23799695
PMCID:
PMC4006354
Keywords:
Active contour, Sparse representation, Texture representation
Appears in Collections:
NAC, NA-MIC, PNL
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Bouix S., Shenton M.E., Tannenbaum A. Sparse Texture Active Contour. IEEE Trans Image Process. 2013 Oct;22(10):3866-78. PMID: 23799695. PMCID: PMC4006354.
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In image segmentation, we are often interested in using certain quantities to characterize the object, and perform the classification based on criteria such as mean intensity, gradient magnitude, and responses to certain predefined filters. Unfortunately, in many cases such quantities are not adequate to model complex textured objects. Along a different line of research, the sparse characteristic of natural signals has been recognized and studied in recent years. Therefore, how such sparsity can be utilized, in a non-parametric way, to model the object texture and assist the textural image segmentation process is studied in this paper, and a segmentation scheme based on the sparse representation of the texture information is proposed. More explicitly, the texture is encoded by the dictionaries constructed from the user initialization. Then, an active contour is evolved to optimize the fidelity of the representation provided by the dictionary of the target. In doing so, not only a non-parametric texture modeling technique is provided, but also the sparsity of the representation guarantees the computation efficiency. The experiments are carried out on the publicly available image data sets which contain a large variety of texture images, to analyze the user interaction, performance statistics, and to highlight the algorithm's capability of robustly extracting textured regions from an image.

Additional Material
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Gao-IEEE-TIP2013-fig1.jpg (201.431kB)