Automated segmentation of caudate nucleus in MR brain images with voxel classification

This paper presents a supervised voxel classification method for segmentation of the caudate nucleus from brain MRI images. Supervised voxel classification is a general pattern recognition technique. In this application general spatial and local structure features extracted from image voxels were used together with a k-nearest neighbor classifier. The trained classifier has been applied to different groups of test data. On test data that originated from the same population as the training images, the method yielded segmentations that correlated very well with human segmentations (Pearson correlation coefficient (PCC) of 0.82 and volumetric overlap (VO) of 74.2%). On data from a different source that exhibits intensity ranges similar to the training data, the method performed slightly worse (PCC of 0.52, VO of 64%), and the method failed on data with different intensity ranges.