Conditional Random Fields For Brain Tissue Segmentation

Current atlas-based methods for MRI analysis assume brain images map to a “normal” template. This assumption, however, does not hold when analyzing abnormal brain shapes or disease states. We propose a discriminative-graphical model framework based on conditional random fields (CRFs) to mine MRI brain images. As a proof-of-concept, we apply CRFs to the problem of brain tissue segmentation. Experimental results show robust and accurate performance on tissue segmentation comparable to other state-of-the-art segmentation methods. Our algorithm generalizes well across data sets and is less susceptible to outliers, while relying on minimal prior knowledge relative to atlas-based techniques. These results provide a promising framework for future application on disease classification and atlas-free anatomical segmentation