Active Volume Models with Probabilistic Object Boundary Prediction Module

Authors:
Tian Shen;Yaoyao Zhu;Xiaolei Huang;Junzhou Huang;Dimitris Metaxas;Leon Axel
Affiliations:
Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015;Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015;Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015;Computational Biomedicine Imaging and Modeling Center, Rutgers University NJ 08854;Computational Biomedicine Imaging and Modeling Center, Rutgers University NJ 08854;Department of Radiology, New York University School of Medicine, New York, NY 10016
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

We propose a novel Active Volume Model (AVM) which deforms in a free-form manner to minimize energy. Unlike Snakes and level-set active contours which only consider curves or surfaces, the AVM is a deforming object model that has both boundary and an interior area. When applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. The probabilistic object prediction module relies on the Bayesian Decision Rule to separate foreground (i.e. object represented by the model) and background. Optimization of the model is a natural extension of the Snakes model so that region information becomes part of the external forces. The AVM thus has the efficiency of Snakes while having adaptive region-based constraints. Segmentation results, validation, and comparison with GVF Snakes and level set methods are presented for experiments on noisy 2D/3D medical images.