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An EM Algorithm for Shape Classification Based on Level Sets

Institution:
Department of Radiology at Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. atsai@mit.edu
Publisher:
Med Image Anal
Publication Date:
Oct-2005
Volume Number:
9
Issue Number:
5
Pages:
491-502
Citation:
Med Image Anal. 2005 Oct;9(5):491-502.
PubMed ID:
16046181
Keywords:
Shape Classification, Shape Estimation, Level set methods, EM Algorithm, Computer-aided diagnosis
Appears in Collections:
SPL, CRL, NAC
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
R01 LM007861/LM/NLM NIH HHS/United States
R21 MH067054/MH/NIMH NIH HHS/United States
Generated Citation:
Tsai A., Wells III W.M., Warfield S.K., Willsky A.S. An EM Algorithm for Shape Classification Based on Level Sets. Med Image Anal. 2005 Oct;9(5):491-502. PMID: 16046181.
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In this paper, we propose an expectation-maximization (EM) approach to separate a shape database into different shape classes, while simultaneously estimating the shape contours that best exemplify each of the different shape classes. We begin our formulation by employing the level set function as the shape descriptor. Next, for each shape class we assume that there exists an unknown underlying level set function whose zero level set describes the contour that best represents the shapes within that shape class. The level set function for each example shape in the database is modeled as a noisy measurement of the appropriate shape class's unknown underlying level set function. Based on this measurement model and the judicious introduction of the class labels as the hidden data, our EM formulation calculates the labels for shape classification and estimates the shape contours that best typify the different shape classes. This resulting iterative algorithm is computationally efficient, simple, and accurate. We demonstrate the utility and performance of this algorithm by applying it to two medical applications.

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Tsai-MIA2005-fig4.jpg (144.239kB)