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titleA Shape-Based Approach to the Segmentation of Medical Imagery Using Level Sets
authorsAndy Tsai, Anthony Yezzi, Jr., William Wells, Clare Tempany, Dewey Tucker, Ayres Fan, W. Eric Grimson, and Alan Willsky
keywordsActive contours, binary image alignment, cardiac MRI segmentation, curve evolution, deformable model, distance transforms, eigenshapes, implicit shape representation, medical image segmentation, parametric shape model, principal component analysis, prostate segmentation, shape prior, statistical shape model
abstractWe propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras [15], we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this
representation are then manipulated to minimize an objective
function for segmentation. The resulting algorithm is able to
handle multidimensional data, can deal with topological changes
of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.
typeJournal Paper
journalIEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 22, NO. 2, FEBRUARY 2003
published year2003
serial1696
is_viewableyes
(Total records:1429)
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