Edge Enhanced Fuzzy C Means Algorithm for Hippocampus Segmentation and Abnormality Identification

• Published in: Biomedical & pharmacology journal Vol. 10; no. 4; pp. 1747 - 1755
• Main Authors: Murthy, G. L. N., Anuradha, B.
• Format: Journal Article
• Language: English
• Published: Bhopal Biomedical and Pharmacology Journal 2017
• Subjects: Algorithms; Automation; Boundaries; Brain research; Cognitive ability; Fuzzy sets; Hippocampus; Image processing; Magnetic resonance imaging; Medical diagnosis; Memory; Methods; Neuroimaging; Noise; Registration; Segmentation
• ISSN: 0974-6242; 2456-2610; 2456-2610
• DOI: 10.13005/bpj/1288

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.