Skull Stripping of Human Brain in MRI Images

Magnetic resonance imaging (MRI) is a very important thing in medical treatment. The exactness of various image processing applications lies on the efficiency of skull stripping. Removal of non-brain tissues can be a challenging exercise considering the various complications of human brain, irregular parameters of Magnetic resonance (MR) scanners, personalized distinctive trait and many more. Here we considered computationally efficient and robust T1-weighted magnetic resonance brain images. In this project, we have proposed a differential evolutionary (DE) methodology with Median filter which optimizes (time and quality of image) the skull stripping for extraction of brain tissue and compared the results with the existing differential evolutionary (DE) algorithm. The exactness of various image processing applications lies on the efficiency of skull stripping. The fully automated brain extraction methods are used to extract the brain portion from T1 MRI of head scans. Here, features are extracted in order to locate regions of interest (ROI). In this project, we have proposed a differential evolutionary (DE) methodology with Median filter which optimizes (time and quality of image) the skull stripping for extraction of brain tissue in low contrast and without neglected edges of affected tissue.