Volume 31, Issue 1 (April 2020)
Studies in Medical Sciences 2020, 31(1): 15-23 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Golestani R, Gharbali A, Nazarbaghi S. DIAGNOSIS OF ALZHEIMER'S DISEASE IN EARLY STAGE USING LINEAR TEXTURE ANALYSIS IN MRI IMAGES. Studies in Medical Sciences 2020; 31 (1) :15-23
URL: http://umj.umsu.ac.ir/article-1-4681-en.html
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: (3446 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.
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.
Type of Study: Research |
Subject:
فیزیک پزشکی
References
1. Zhang J, Yu C, Jiang G, Liu W, Tong L. 3D texture analysis on MRI images of Alzheimer's disease. Brain Imaging Behav 2012;6(1): 61-9. [DOI:10.1007/s11682-011-9142-3] [PMID]
2. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state": a practical method for grading the cognitive state of patients for the clinician. Psychiatry Res 1975;12(3): 189-98. [DOI:10.1016/0022-3956(75)90026-6] [PMID]
3. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993; 43(11): 2412-4. [DOI:10.1212/WNL.43.11.2412-a] [PMID]
4. Dubois B, Feldman HH, Jacova C, DeKosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol2007;6(8): 734-46. [DOI:10.1016/S1474-4422(07)70178-3] [PMID]
5. Leandrou S, Petroudi S, Reyes-Aldasoro CC, Kyriacou PA, Pattichis CS. Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's disease: A Methodological Review. IEEE Rev Biomed Eng 2018; 11: 97-111. [DOI:10.1109/RBME.2018.2796598] [PMID]
6. Fox NC, Freeborough PA, Rossor MN. Visualisation and quantification of rates of atrophy in Alzheimer's disease. Lancet 1996;348(9020): 94-7. [DOI:10.1016/S0140-6736(96)05228-2] [PMID]
7. Scheltens P, van de Pol L. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992; 55(10): 967-72. [DOI:10.1136/jnnp.55.10.967] [PMID] [PMCID]
8. 8 Hwang EJ, Kim HG, Kim D, Rhee HY, Ryu CW, Liu T, et al. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer's disease from cognitive normal and mild cognitive impairment. Med Phys 2016;43(8): 4718-28. [DOI:10.1118/1.4958959] [PMID]
9. Evans MC, Barnes J, Nielsen C, Kim LG, Clegg SL, Blair M, et al. Volume changes in Alzheimer'sdisease and mild cognitive impairment: cognitive associations. Eur Radiol 2010;20(3): 674-82. [DOI:10.1007/s00330-009-1581-5] [PMID]
10. Karas G, Burton E, Rombouts S, Van Schijndel R, O'Brien J, Scheltens P, et al. A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry. Neuroimage 2003;18(4): 895-907. [DOI:10.1016/S1053-8119(03)00041-7] [PMID]
11. Karas G, Scheltens P, Rombouts S, Visser P, Van Schijndel R, Fox N, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. Neuroimage 2004;23(2): 708-16. [DOI:10.1016/j.neuroimage.2004.07.006] [PMID]
12. Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H, et al. Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 2008;248(1): 194-201. [DOI:10.1148/radiol.2481070876] [PMID]
13. Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnéhun S, et al. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease. Neuroimage 2007;34(3): 996-1019. [DOI:10.1016/j.neuroimage.2006.10.035] [PMID]
14. Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hänninen T, Laakso MP, et al. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging 2004;25(3): 303-10. [DOI:10.1016/S0197-4580(03)00084-8] [PMID]
15. Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27(4): 685-91. [DOI:10.1002/jmri.21049] [PMID] [PMCID]
16. Schuff N, Woerner N, Boreta L, Kornfield T, Shaw L, Trojanowski J, et al. MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain 2009;132(4): 1067-77. [DOI:10.1093/brain/awp007] [PMID] [PMCID]
17. Castellano G, Bonilha L, Li L, Cendes F. Texture analysis of medical images. Clin Radiol 2004;59(12): 1061-69. [DOI:10.1016/j.crad.2004.07.008] [PMID]
18. Sørensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, et al. Early detection of Alzheimer's disease using M RI hippocampal texture. Hum Brain Mapp 2016;37(3): 1148-61. [DOI:10.1002/hbm.23091] [PMID] [PMCID]
19. Freeborough PA, Fox NC. MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease. IEEE Trans Med Imaging1998;17(3): 475-8. [DOI:10.1109/42.712137] [PMID]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
