(untitled) (International Journal of Engineering and Computer Science, 2014)
This paper describes a new method called adaptive hybrid Transformable geometric segmentation that uses knowledge of tissue intensity properties and intensity in-homogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segregation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, we have described an unsupervised fuzzy segmentation method, based on new objective function, which seems well adapted and efficient for functional MRI data segregation. The proposed segmentation method is more robust than the FCM algorithm and BCFCM. The proposed segmentation uses an automatic algorithm for robust WM, GM, and cerebrospinal ﬂuid (CSF) segmentation to facilitate accurate measurement of brain tissues. Both qualitative and quantitative results on synthetic and real brain MRI scans indicate superior and consistent performance. One popular family of brain tissue segregation methods is based on normalizing the brain scans by storing (or aligning) them to a pre deﬁned realistic view of brain tissues.