Improvements in medical imaging techniques have provided clinicians the ability to obtain detailed brain images of patients at lower costs. This increased availability of rich data opens up new avenues of research that promise better understanding of common brain ailments such as Alzheimer’s Disease and dementia. Improved data mining techniques, however, are required to leverage these new data sets to identify intermediate disease states (e.g., mild cognitive impairment) and perform early diagnosis.
We propose a 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. In addition, results show that our algorithm generalizes well across data sets and is less susceptible to outliers. Our method relies on minimal prior knowledge unlike atlas-based techniques, which assume images map to a normal template. Our results show that CRFs are a promising model for tissue segmentation, as well as other MRI data mining problems such as anatomical segmentation and disease diagnosis where atlas assumptions are unreliable in abnormal brain images.