Detection of Alzheimer's disease with segmentation approach using K-Means Clustering and Watershed Method of MRI image

D. Holilah, A. Bustamam, D. Sarwinda

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Article number012009
JournalJournal of Physics: Conference Series
Volume1725
Issue number1
DOIs
Publication statusPublished - 12 Jan 2021
Event2nd Basic and Applied Sciences Interdisciplinary Conference 2018, BASIC 2018 - Depok, Indonesia
Duration: 3 Aug 20184 Aug 2018

Keywords

  • Alzheimer's disease
  • Clustering
  • Image processing
  • MRI images
  • Segmentation

Fingerprint Dive into the research topics of 'Detection of Alzheimer's disease with segmentation approach using K-Means Clustering and Watershed Method of MRI image'. Together they form a unique fingerprint.

Cite this