Segmenting MR Images Using Fully-Tuned Radial Basis Functions (RBF)

Segmenting medical images into different tissues is an important task in medical image analysis, e.g., classifying every voxel of input image into different tissue types: CSF, gray matter and white matter. This paper investigates the fully-tuned radial basis function (RBF) and compares it with the traditional fuzzy c-mean (FCM) clustering algorithm in MR image segmentation. It turns out that FCM is not only biased by the number of voxels in different groups, but also by the intensity differences between different tissue groups, while the fully-tuned RBF captures the multi-Gaussian distribution of the image intensities very well and thus it can be used to segment image intensities accurately. Moreover, in order to generate spatially smooth segmentation results, a Markov random field model is applied to the segmentation results of the fully-tuned RBF algorithm. Experimental results show that fully-tuned RBF method can capture the tissue intensity distribution more accurately than the FCM algorithm