Edge Enhanced Fuzzy C Means Algorithm for Hippocampus Segmentation and Abnormality Identification
G. L. N. Murthy1
and B. Anuradha21Department of ECE, LBRCE, Mylavaram, Andhra Pradesh, India.
2Department of ECE, S.V. University College of Engineering, Tirupati, A.P, India.
Abstract: Unclear boundaries as well as misclassification are the significant problems that need to be addressed in many of the medical imaging related problems. In particular,pathological studies need accurate delineation of objects of interest. Further presence of noise and non-clear boundaries deteriorate the performance of segmentation of brain Magnetic resonance images.Extracting any tissue from brain images fundamentally involveseither registration with atlas or complex deformation models. In the current work, all these problems are addressed by merging the clustering approaches with region growing methods to extract most prominent brain tissue, Hippocampus. Structural analysis of Hippocampus plays a vital role in diagnosis of many cognitive related disorders. An edge enhanced Fuzzy C means (EEFCM) algorithm is proposed aimed at extracting the Hippocampus. The results have shown better results when compared to existing approaches in terms of Dice and Jaccard coefficient.
Keywords: Hippocampus; Skull Removal; Misclassification; Skull Stripping; Edge Enhancement Back to TOC