Image Segmentation Using Graph Representations and Local Appearance and Shape Models

Authors:
Johannes Keustermans;Dieter Seghers;Wouter Mollemans;Dirk Vandermeulen;Paul Suetens
Affiliations:
Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Research Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Leuven, Belgium B-3000;Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Research Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Leuven, Belgium B-3000;Medicim nv, Mechelen, Belgium 2800;Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Research Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Leuven, Belgium B-3000;Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Research Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Leuven, Belgium B-3000
Venue:
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Year:
2009

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Abstract

A generic model-based segmentation algorithm is presented. Based on a set of training data, consisting of images with corresponding object segmentations, a local appearance and local shape model is build. The object is described by a set of landmarks. For each landmark a local appearance model is build. This model describes the local intensity values in the image around each landmark. The local shape model is constructed by considering the landmarks to be vertices in an undirected graph. The edges represent the relations between neighboring landmarks. By implying the markovianity property on the graph, every landmark is only directly dependent upon its neighboring landmarks, leading to a local shape model. The objective function to be minimized is obtained from a maximum a-posteriori approach. To minimize this objective function, the problem is discretized by considering a finite set of possible candidates for each landmark. In this way the segmentation problem is turned into a labeling problem. Mean field annealing is used to optimize this labeling problem. The algorithm is validated for the segmentation of teeth from cone beam computed tomography images and for automated cephalometric analysis.