Open AccessDetection of Alzheimer’s disease with segmentation approach using K-Means Clustering and Watershed Method of MRI image
Author(s) -
D Holilah,
Alhadi Bustamam,
Devvi Sarwinda
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1725/1/012009
Subject(s) - hippocampus , cluster analysis , alzheimer's disease , watershed , segmentation , magnetic resonance imaging , disease , image segmentation , neuroscience , pathology , computer science , artificial intelligence , medicine , pattern recognition (psychology) , psychology , computer vision , radiology
Alzheimer’s disease is a common form of neurodegenerative disorders characterized by defective brain cells, such as neurofibrillary tangles and amyloid plaque that is progressive. One of the physical characteristics of someone suffering from Alzheimer’s disease is shrinking of the hippocampus area of the brain. The hippocampus is the smallest part of the brain that serves to save memory. The detection of Alzheimer’s disease can be done using a Magnetic Resonance Image (MRI) which is a technique of noninovasive for an analysis of the structure of the brain in the Alzheimer’s patient. In this research, K-Means Clustering and Watershed method are used to segment the hippocampus area which is one part of the brain that was attacked by Alzheimer’s disease. The analysis used to detect Alzheimer’s is comparing the value of the threshold with the number of white pixels in the images. The data used in this research are Open Access Series of Image Studies (OASIS) database by using the image of coronal slices. Based on the our experiment result, both K-Means Clustering and Watershed method can segment the hippocampus area to detect Alzheimer’s disease.