Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Pages
3421-3424
Date Issued
2017
Author(s)
Abstract
Skull stripping, which refers to the segmentation of brain tissue from non-brain tissue, has been challenging due to the ramification of the human brain structures and volatile parameters in the magnetic resonance imaging (MRI) procedures. It has been one of the most critical preprocessing steps in medical image analysis. We propose a hybrid skull stripping algorithm that is based on texture feature analysis, fuzzy possibilistic c-means (FPCM), and morphological operations. The input MR image is first processed to obtain two texture feature maps, to which the FPCM is applied for acquiring brain and non-brain masks. A number of morphological operations are subsequently performed to extract the brain. Our algorithm has been compared with two famous methods and evaluated on the internet brain segmentation repository (IBSR) datasets. Preliminary experimental results suggested that this new framework achieved high accuracy and outperformed the compared methods. We believe that the proposed scheme is of effectively potential in a wide variety of brain MR image segmentation applications.
Type
conference paper