Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 30, Issue 3, Pages 412-423Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2007.70713
Keywords
directional segmentation; Finsler metric; dynamic programming; active contours; diffusion weighted imagery
Funding
- NCRR NIH HHS [P41 RR013218, P41 RR 13218] Funding Source: Medline
- NIBIB NIH HHS [U54 EB005149, U54 EB005149-010003, U54 EB 005149] Funding Source: Medline
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In this paper, we propose an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the isotropic case, the euclidean metric is locally multiplied by a scalar conformal factor based on image information such that the weighted length of curves lying on points of interest (typically edges) is small. The conformal factor that is chosen depends only upon position and is in this sense isotropic. Although directional information has been studied previously for other segmentation frameworks, here, we show that if one desires to add directionality in the conformal active contour framework, then one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming-based schemes. Finally, we demonstrate the technique by extracting roads from aerial imagery, blood vessels from medical angiograms, and neural tracts from diffusion-weighted magnetic resonance imagery.
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