Shape Prior Models prior information about the shape of the object being segmented by,

1. Image Segmentation Using Shape Prior and MRF Theories Mumford shah energy Appearance Neva Waynesboro Problem Statement Appearance Prior Mumford shah energy Divergence theorem We then set and get Now Input Image Result Labels of the MRF = local displacements of the contour points Database of Heart Annotations Image Segmentation We cast the segmentation problem as a MAP-MRF problem [1]. We compute the MAP-MRF solution by minimizing the following Gibbs energy function: We represent the segmentation energy as a combination of two energy functions, each modeling a specific type of prior information, as shown below: Learn shape by learning the distance between contour points Sites of the MRF = points on the contour (explicit contour representation) Shape Prior Models prior information about the shape of the object being segmented by, Results • Segmentation Algorithm • Repeat • Step1: Compute Appearance Prior • Step 2: Compute Shape Prior • Step 3: Minimize MRF energy using belief propagation • until convergence is reached Input Image Initial Contour with Normals Shape Result The problem was that the appearance was not growing correctly, so when applied both the result just moved rather than actually grow Literature Cited Ahmed Besbes, N. K., Nikos Paragios (2009). "Graph-Bases Knowledge-Driven Discrete Segementation Of The Left Ventricle." IEEE: 49 - 52. Ahmed Besbes, N. K., Georg Langs, Nikos Paragios (2009). "Shape Priors and Discrete MRFs for Knowledge-based Segmentation." IEEE: 1295 1302. D.R. Chittajallu , E. S., O.C. Avila-Monets, R.P. Yalamanchili MRF-Based Solutions to Image Analysis Problems: An Investigation. Houston University of Houston.