A shape-based approach to the segmentation of medical imagery using level sets.
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, 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.
Tsai
Andy
A
Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Department of Electrical Engineering, Room 35-427, 127 Massachusets Ave., Cambridge 02139, USA. atsai@mit.edu
Yezzi
Anthony
A
Wells
William
W
Tempany
Clare
C
Tucker
Dewey
D
Fan
Ayres
A
Grimson
W Eric
WE
Willsky
Alan
A
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D003198:Computer Simulation; D006352:Heart Ventricles / Q000033:anatomy & histology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D011467:Prostate / Q000033:anatomy & histology; D013382:Subtraction Technique