Volume 35, Issue 2 (May 2024)
Studies in Medical Sciences 2024, 35(2): 136-144 |
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Gharbali A, Golestani R, Nazarbaghi S. DIFFERENTIAL DISCRIMINATION OF THE ALZHEIMER PATIENTS FROM NORMAL AGING BY COMPUTERIZE ANALYSIS OF THE BRAIN MRI. Studies in Medical Sciences 2024; 35 (2) :136-144
URL: http://umj.umsu.ac.ir/article-1-4707-en.html
URL: http://umj.umsu.ac.ir/article-1-4707-en.html
Assistant Professor of Medical Physics, Urmia University of Medical Sciences, Urmia, Iran (Corresponding Author) , gharbali@yahoo.com
Abstract: (140 Views)
Background & Aim: Early detection and reliable differentiation of the Alzheimer’s diseases from normal aging dementia provide optimal rehabilitation. MRI is a convenient imaging method for interpreting dementia caused by brain atrophy. Visual interpretation of brain MRI for atrophy is a qualitative procedure which un able to discriminate Alzheimer atrophy from aging brain atrophy. In recent years, Quantitative texture analysis of the medical imaging represent important biological information from pixels of the digital imaging for differential diseases discrimination. Quantitative texture analysis of the brain atrophy is not yet available for routine clinical use. The aim of this study is to evaluate performance of the applied automated texture analysis methods in discrimination Alzheimer versus normal aging by brain MRI.
Materials & Methods: In this approach, a total of 26 brain MRI (13 Alzheimer and 13 normal aging) images were analyzed By MaZda software. About 26 suitable regions of interest (ROI) were selected from hippocampal on MR images. Up to 270 texture features parameters were computed per ROI. The sets of 10 features parameters as a best differential descriptor are selected by applying Fisher and or POE+ACC algorithms. Under two standard / nonstandard states, both sets of features were discriminated by PCA, LDA and NDA. The confuse matrix applied for determination sensitivity, specificity and accuracy of applied texture analysis methods. The ROC cure analysis was used for examining the discrimination performance of the applied texture analysis methods.
Results: In comparison with PCA and LDA, in general, NDA has the best result for discriminating Alzheimer from normal aging dementia with sensitivity 100%, specificity of 100% and accuracy 100%.
Conclusions: our results indicate that the computerize brain atrophy discrimination in MR image can be an auxiliary tool in diagnosing Alzheimer's in early stages.
Materials & Methods: In this approach, a total of 26 brain MRI (13 Alzheimer and 13 normal aging) images were analyzed By MaZda software. About 26 suitable regions of interest (ROI) were selected from hippocampal on MR images. Up to 270 texture features parameters were computed per ROI. The sets of 10 features parameters as a best differential descriptor are selected by applying Fisher and or POE+ACC algorithms. Under two standard / nonstandard states, both sets of features were discriminated by PCA, LDA and NDA. The confuse matrix applied for determination sensitivity, specificity and accuracy of applied texture analysis methods. The ROC cure analysis was used for examining the discrimination performance of the applied texture analysis methods.
Results: In comparison with PCA and LDA, in general, NDA has the best result for discriminating Alzheimer from normal aging dementia with sensitivity 100%, specificity of 100% and accuracy 100%.
Conclusions: our results indicate that the computerize brain atrophy discrimination in MR image can be an auxiliary tool in diagnosing Alzheimer's in early stages.
Type of Study: Research |
Subject:
فیزیک پزشکی
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