Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification

Abstract

Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.