Volume 31, Issue 1 (April 2020)                   Stud Med Sci 2020, 31(1): 15-23 | Back to browse issues page

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Golestani R, Gharbali A, Nazarbaghi S. DIAGNOSIS OF ALZHEIMER'S DISEASE IN EARLY STAGE USING LINEAR TEXTURE ANALYSIS IN MRI IMAGES. Stud Med Sci. 2020; 31 (1) :15-23
URL: http://umj.umsu.ac.ir/article-1-4681-en.html
Assistant Professor of Medical Physics, Urmia University of Medical Sciences, Urmia, Iran (Corresponding Author) , gharbali@yahoo.com
Abstract:   (2037 Views)
Background & Aims: The purpose of this study was to evaluate the potential of linear discriminant analysis (LDA) and principal component analysis (PCA) in discriminating atrophy of Alzheimer's disease in early stage and atrophy of aging using MRI images.
Materials & Methods: In general, 26 MRI images (13 Alzheimer and 13 elderly) were analyzed under applied options and two texture features analysis methods: principal component analysis (PCA), linear discriminant analysis (LDA) using MaZda software. The K-NN (K=1) classifier was used for features resulting from PCA and LDA. The confusion matrix and Receiver operating characteristic (ROC) curve were also calculated.
Results:  Computer aim diagnosis is able to discriminate atrophy of Alzheimer's disease from atrophy of normal aging.
Discussion: Our results indicated that texture analysis can be an auxiliary tool in diagnosing Alzheimer's disease in early stages.
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Type of Study: Research | Subject: فیزیک پزشکی

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