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Segmentation of brain MRI using active contour model
Author(s) -
Ben Rabeh Amira,
Benzarti Faouzi,
Amiri Hamid
Publication year - 2017
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22205
Subject(s) - active contour model , computer science , artificial intelligence , segmentation , initialization , context (archaeology) , a priori and a posteriori , set (abstract data type) , level set (data structures) , pattern recognition (psychology) , image segmentation , computer vision , independence (probability theory) , mathematics , paleontology , philosophy , epistemology , biology , programming language , statistics
Alzheimer disease is a neurodegenerative disorder that impairs memory, cognitive function, and gradually leads to dementia, physical deterioration, loss of independence, and death of the affected individual. In this context, segmentation of medical images is a very important technique in the field of image analysis and Computer‐Assisted Diagnosis. In this article, we introduce a new automatic method of brain images’ segmentation based on the Active Contour (AC) model to extract the Hippocampus and the Corpus Callosum (CC). Our contribution is to combine the geometric method with the statistical method of the AC. We used the Caselle Level Set and added a learning phase to build an average shape and to make the initialization task automatic. For the step of contour evolution, we used the principle of Level set and we added to it the a priori knowledge. Experimental results are very promising. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 3–11, 2017