Segmenting Brain Tumors Using Pseudo---Conditional Random Fields

Authors:
Chi-Hoon Lee;Shaojun Wang;Albert Murtha;Matthew R. Brown;Russell Greiner
Affiliations:
Department of Computing Science, University of Alberta, Canada and Yahoo! Inc, USA;Department of Computing Science, Wright State University, USA;Cross Cancer Institute, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada
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

Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard classifiers, such as Logistic Regression (LR) and Support Vector Machines (SVM), typically have limited accuracy as they treat voxels as independentand identically distributed(iid). Approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. However, previous random field systems involved computationally intractable formulations, which are typically solved using some approximation. Here, we present pseudo-conditional random fields(PCRFs), which achieve accuracy similar to other random fields variants, but are significantly more efficient. We formulate a PCRF as a regularized discriminative classifier that relaxes the classification decision for each voxel by considering the labels and features of neighboring voxels.