Volume 18, Issue 2, October 2015, Pages 342–347
Reem Alattas1
1 Department of Computer Science & Engineering, University of Bridgeport, Bridgeport, CT, USA
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Alzheimer is defined as the loss of mental functions such as thinking, memory, and reasoning that is severe enough to interfere with a person's daily functioning. The appearance of Alzheimer's Disease symptoms are resulted based on which part of the brain has a variety of infection or damage. Therefore, MRI is the best biomedical instrumentation to detect Alzheimer's Disease. For that reason, this paper proposes a novel method for detecting Alzheimer's Disease in MRI images using thresholding and morphology. In this paper, we analyzed 20 MRI images collected from OASIS brains database to detect the threshold that will allow our program to automatically detect Alzheimer's Disease existence in MRI images. Automatically Image Classification is one of the challenging problems of our recent era. So, we have implemented and tested our proposed technique and the end results have 98% accuracy.
Author Keywords: Alzheimer's Disease, MRI, biomedical images, image processing, thresholding, masking, morphological operators.
Reem Alattas1
1 Department of Computer Science & Engineering, University of Bridgeport, Bridgeport, CT, USA
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Alzheimer is defined as the loss of mental functions such as thinking, memory, and reasoning that is severe enough to interfere with a person's daily functioning. The appearance of Alzheimer's Disease symptoms are resulted based on which part of the brain has a variety of infection or damage. Therefore, MRI is the best biomedical instrumentation to detect Alzheimer's Disease. For that reason, this paper proposes a novel method for detecting Alzheimer's Disease in MRI images using thresholding and morphology. In this paper, we analyzed 20 MRI images collected from OASIS brains database to detect the threshold that will allow our program to automatically detect Alzheimer's Disease existence in MRI images. Automatically Image Classification is one of the challenging problems of our recent era. So, we have implemented and tested our proposed technique and the end results have 98% accuracy.
Author Keywords: Alzheimer's Disease, MRI, biomedical images, image processing, thresholding, masking, morphological operators.
How to Cite this Article
Reem Alattas, “Hybrid Segmentation Algorithm for Detecting Alzheimer's Disease in MRI Images,” International Journal of Innovation and Scientific Research, vol. 18, no. 2, pp. 342–347, October 2015.
