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MICCAI
2008
Springer

Segmenting Brain Tumors Using Pseudo-Conditional Random Fields

9 years 11 months ago
Segmenting Brain Tumors Using Pseudo-Conditional Random Fields
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 nontumor, 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 independent and 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 discriminat...
Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matt
Added 06 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where MICCAI
Authors Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R. G. Brown, Russell Greiner
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