Segmenting brain tumors using alignment-based features

Detecting and segmenting brain tumors in magnetic resonance images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal areas and tumor areas. Several recent projects have explored ways to use an aligned spatial 'template' image to incorporate spatial anatomic information about the brain, but it is not obvious what types of aligned information should be used. This work quantitatively evaluates the performance of 4 different types of alignment-based (AB) features encoding spatial anatomic information for use in supervised pixel classification. This is the first work to (1) compare several types of AB features, (2) explore ways to combine different types of AB features, and (3) explore combining AB features with textural features in a learning framework. We considered situations where existing methods perform poorly, and found that combining textural and AB features allows a substantial performance increase, achieving segmentations that very closely resemble expert annotations.