Tissue probability map constrained 4D clustering algorithm for increased accuracy and robustness in SerialMR brain image segmentation

The traditional fuzzy clustering algorithms might be biased due to the variability of tissue intensities and anatomical structures: clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we propose a tissue probability map constrained clustering algorithm and apply it to series of 3D MR brain images of the same subject at different time points for longitudinal study. The tissue probability maps consist of segmentation priors obtained from a population and reflect the probability of different tissue types. Using the proposed algorithm in the framework of the CLASSIC algorithm, which iteratively segments a series of images and estimates their longitudinal deformations, more accurate and robust longitudinal measures can be achieved. Experimental results using both simulated and real data confirmed the advantages of the proposed algorithm in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders.