Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 22, Issue 2, Pages 137-154Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2002.808355
Keywords
active 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
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Funding
- NCRR NIH HHS [1P41RR13218, P41 RR013218] Funding Source: Medline
- NIA NIH HHS [R01 AG019513, R01 AG 19513-01] Funding Source: Medline
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We 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.
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