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A Framework for Joint Image-and-Shape Analysis

Institution:
1Department of Electrical and Computer Engineering and the Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA.
2Departments of Computer Science and Applied Mathematics/Statistics, Stony Brook University, Stony Brook, New York, NY, USA.
3Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Publication Date:
Mar-2014
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
9034
Pages:
90340V
Citation:
Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:90340V.
PubMed ID:
25302006
PMCID:
PMC4187242
Appears in Collections:
NAC, NA-MIC, PNL, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gao Y., Tannenbaum A., Bouix S. A Framework for Joint Image-and-Shape Analysis. Proc Soc Photo Opt Instrum Eng. 2014 Mar 21;9034:90340V. PMID: 25302006. PMCID: PMC4187242.
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Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.

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