Improving MRI Brain Image Classification with Anatomical Regional Kernels

Abstract

Classification of brain images is frequently done using kernel based methods, such as the support vector machine. These lend themselves to improvement via multiple kernel learning, where a number of different kernels are linearly combined to integrate different sources of information and increase accuracy. Previous applications made use of a small number of kernels representing different image modalities or kernel functions. Here, the kernels instead represent 83 anatomically meaningful brain regions. To find the optimal combination of kernels and perform classification, we use a Gaussian Process framework to infer the maximum likelihood weights. The resulting formulation successfully combines voxel level features with prior anatomical knowledge. This gives an improvement in classification accuracy of MRI images of Alzheimer’s disease patients and healthy controls from the ADNI database to almost 88 %, compared to less than 86 % using a single kernel representing the whole brain. Moreover, interpretability of the classifier is also improved, as the optimal kernel weights are sparse and give an indication of the importance of each brain region in separating the two groups.