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Classification of Alzheimer's disease based on several features extracted from MRI T1-weighted brain images | ||
| Kerbala Journal for Engineering Sciences (KJES) | ||
| Article 10, Volume 1, Issue 2, 2021, Pages 252-279 PDF (1.07 M) | ||
| Document Type: Research Article | ||
| DOI: 10.63463/kjes1024 | ||
| Authors | ||
| Zahraa Shihab* ; Hawraa H. Abbas | ||
| Department of Electrical and Electronics, College of Engineering, University of Karbala, Karbala, Iraq | ||
| Abstract | ||
| Alzheimer’s disease (AD) diagnosis at an early stage plays a significant role in reducing its symptoms and decelerating cognitive deterioration. Hence the use of computer-aided systems for early and accurate AD diagnosis is critical. The proposed diagnostic tool depends on classifying features extracted from brain Magnetic Resonance Imaging (MRI). These Features must accurately capture main AD-related variations of the anatomical brain structures, such as hippocampi region atrophy, lateral ventricle enlargement, cortical thickness, brain volume, etc. In this work, T1-weighted structural MRIs were employed for extracting these AD-related features. The images resulting from MRI scans are interpreted to high-intensity visible features, making preprocessing and segmentation less complex. This work has proposed a software pipeline consisting of several preprocessing steps, a segmentation method for segmenting brain tissues, and Convolutional Neural Networks (CNN) for Regions of Interest (ROIs) Parcellation that is AD-related. Features extracted from these segmented tissues and ROIs are utilized for the final AD classification using a Support Vector Machine (SVM) classifier. The results show that the proposed approach has reached 89.1% accuracy in the binary classification of AD vs. CN (Cognitively Normal), Demonstrating promising classification performance. | ||
| Keywords | ||
| Alzheimer’s disease; Neuroimaging; Structural Brain MRI; deep learning; 3D CNN; U-Net; SVM | ||
| References | ||
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[1] J. Weller and A. Budson, “Current understanding of Alzheimer’s disease diagnosis and treatment,” F1000Research, vol. 7, p. F1000 Faculty Rev-1161, Jul. 2018, doi: 10.12688/f1000research.14506.1.
[2] “Dementia,” World Health Organization, Sep. 02, 2021. https://www.who.int/en/news-room/fact-sheets/detail/dementia
[3] D. Amaral and P. Lavenex, “Hippocampal neuroanatomy.” in The hippocampus book. New York, NY, US: Oxford University Press, 2007, pp. 37–114.
[4] P. Vemuri and C. R. Jack Jr, “Role of structural MRI in Alzheimer’s disease,” Alzheimer’s research & therapy, vol. 2, no. 4, p. 23, Aug. 2010, doi: 10.1186/alzrt47.
[5] G. B. Frisoni, N. C. Fox, C. R. Jack Jr, P. Scheltens, and P. M. Thompson, “The clinical use of structural MRI in Alzheimer disease,” Nature reviews. Neurology, vol. 6, no. 2, pp. 67–77, Feb. 2010, doi: 10.1038/nrneurol.2009.215.
[6] S. M. Nestor et al., “Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database,” Brain : a journal of neurology, vol. 131, no. Pt 9, pp. 2443–2454, Sep. 2008, doi: 10.1093/brain/awn146.
[7] P. Baglat, A. W. Salehi, A. Gupta, and G. Gupta, “Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset,” 2020, doi: 10.1007/978-3-030-64849-7_54.
[8] P. Vemuri et al., “Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies,” NeuroImage, vol. 39, no. 3, Feb. 2008, doi: 10.1016/j.neuroimage.2007.09.073.
[9] O. ben Ahmed, J. Benois-Pineau, M. Allard, C. ben Amar, and G. Catheline, “Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features,” Multimedia Tools and Applications, vol. 74, no. 4, Feb. 2015, doi: 10.1007/s11042-014-2123-y.
[10] K. Shen, J. Fripp, F. Mériaudeau, G. Chételat, O. Salvado, and P. Bourgeat, “Detecting global and local hippocampal shape changes in Alzheimer’s disease using statistical shape models,” NeuroImage, vol. 59, no. 3, Feb. 2012, doi: 10.1016/j.neuroimage.2011.10.014.
[11] S. Toshkhujaev et al., “Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets,” Journal of Healthcare Engineering, vol. 2020, p. 3743171, 2020, doi: 10.1155/2020/3743171.
[12] L. Khedher, J. Ramírez, J. M. Górriz, A. Brahim, and F. Segovia, “Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images,” Neurocomputing, vol. 151, Mar. 2015, doi: 10.1016/j.neucom.2014.09.072.
[13] M. R. Daliri, “Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images,” Journal of Medical Systems, vol. 36, no. 2, pp. 995–1000, 2012, doi: 10.1007/s10916-011-9738-6.
[14] M. Chupin et al., “Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI,” Hippocampus, vol. 19, no. 6, pp. 579–587, Jun. 2009, doi: 10.1002/hipo.20626.
[15] O. Colliot et al., “Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus,” Radiology, vol. 248, no. 1, Jul. 2008, doi: 10.1148/radiol.2481070876.
[16] Y. Liu et al., “Combination analysis of neuropsychological tests and structural MRI measures in differentiating AD, MCI and control groups—The AddNeuroMed study,” Neurobiology of Aging, vol. 32, no. 7, Jul. 2011, doi: 10.1016/j.neurobiolaging.2009.07.008.
[17] Kai-kai SHEN, “Automatic segmentation and shape analysis of human hippocampus in Alzheimer’s disease,” Sep. 2011.
[18] F. Citak-Er, D. Goularas, B. Ormeci, and T. Initiative, “A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment,” Journal of Neurological Sciences, vol. 34, pp. 52–69, Jan. 2017.
[19] A. Chaddad, C. Desrosiers, and T. Niazi, “Deep Radiomic Analysis of MRI Related to Alzheimer’s Disease,” IEEE Access, vol. 6, 2018, doi: 10.1109/ACCESS.2018.2871977.
[20] W. Lin et al., “Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment,” Frontiers in Neuroscience, vol. 12, Nov. 2018, doi: 10.3389/fnins.2018.00777.
[21] T. Shen, J. Jiang, Y. Li, P. Wu, C. Zuo, and Z. Yan, “Decision Supporting Model for One-year Conversion Probability from MCI to AD using CNN and SVM,” Jul. 2018. doi: 10.1109/EMBC.2018.8512398.
[22] E. Ambar Pambudi, P. Nurtantio Andono, and R. Anggi Pramunendar, “IMAGE SEGMENTATION ANALYSIS BASED ON K-MEANS PSO BY USING THREE DISTANCE MEASURES,” ICTACT Journal on Image and Video Processing, vol. 9, no. 1, Aug. 2018, doi: 10.21917/ijivp.2018.0256.
[23] A. Sheta, M. S. Braik, and S. Aljahdali, “Genetic Algorithms: A tool for image segmentation,” May 2012. doi: 10.1109/ICMCS.2012.6320144.
[24] U. Maulik and S. Bandyopadhyay, “Genetic algorithm-based clustering technique,” Pattern Recognition, vol. 33, no. 9, Sep. 2000, doi: 10.1016/S0031-3203(99)00137-5.
[25] Y. K. Dubey and M. M. Mushrif, “FCM Clustering Algorithms for Segmentation of Brain MR Images,” Advances in Fuzzy Systems, vol. 2016, 2016, doi: 10.1155/2016/3406406.
[26] P. Kalavathi, “Brain tissue segmentation in MR brain images using multiple Otsu’s thresholding technique,” in 2013 8th International Conference on Computer Science & Education, 2013, pp. 639–642. doi: 10.1109/ICCSE.2013.6553987.
[27] Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Transactions on Medical Imaging, vol. 20, no. 1, 2001, doi: 10.1109/42.906424.
[28] A. I. Scher et al., “Hippocampal shape analysis in Alzheimer’s disease: A population-based study,” NeuroImage, vol. 36, no. 1, pp. 8–18, 2007, doi: https://doi.org/10.1016/j.neuroimage.2006.12.036.
[29] B. Gutman, Y. Wang, J. Morra, A. W. Toga, and P. M. Thompson, Disease classification with hippocampal shape invariants, vol. 19. 2009. doi: 10.1002/hipo.20627.
[30] X. Liu, L. Song, S. Liu, and Y. Zhang, “A Review of Deep-Learning-Based Medical Image Segmentation Methods,” Sustainability, vol. 13, no. 3, Jan. 2021, doi: 10.3390/su13031224.
[31] M. Shao et al., “Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly,” NeuroImage: Clinical, vol. 23, 2019, doi: 10.1016/j.nicl.2019.101871.
[32] K. H. Zou et al., “Statistical validation of image segmentation quality based on a spatial overlap index,” Academic radiology, vol. 11, no. 2, pp. 178–189, Feb. 2004, doi: 10.1016/s1076-6332(03)00671-8.
[33] F. Falahati et al., “The Effect of Age Correction on Multivariate Classification in Alzheimer’s Disease, with a Focus on the Characteristics of Incorrectly and Correctly Classified Subjects,” Brain topography, vol. 29, no. 2, pp. 296–307, Mar. 2016, doi: 10.1007/s10548-015-0455-1.
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