MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach

Abstract

A fast spatially constrained kernel clustering algorithm is proposed for segmenting medical magnetic resonance imaging (MRI) brain images and correcting intensity inhomogeneities known as bias field in MRI data. The algorithm using kernel technique implicitly maps image data to a higher dimensional kernel space in order to improve the separability of data and provide more potential for effectively segmenting MRI data. Based on the technique, a speed-up scheme for kernel clustering and an approach for correcting spurious intensity variation of MRI images have been implemented. The fast kernel clustering and bias field correcting benefit each other in an iterative matter and have dramatically reduced the time complexity of kernel clustering. The experiments on simulated brain phantoms and real clinical MRI data have shown that the proposed algorithm generally outperforms the corresponding traditional algorithms when segmenting MRI data corrupted by high noise and gray bias field.