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Volumetric Analysis of Choroid Plexus for the Early Detection of Alzheimer's Disease

Published: 30 May 2024 Publication History

Abstract

Alzheimer's Disease (AD) is a chronic cognitive neurodegenerative condition characterized by cognitive dysfunction, including memory loss and language impairment. This research introduces a Computer-Aided Detection (CAD) system designed for the early detection of AD through the calculation of volume of Choroid Plexus (CP). The study incorporates seventy-five T1-weighted image samples, each comprising twenty-five cases of AD, Neuropathological Change (NC), and Mild Cognitive Impairment (MCI). CP volume is determined by computing compactness and circularity. The methodology involves skull stripping, followed by Canny edge detection and morphological filtering to identify the Region of Interest (ROI). Compactness and circularity are then calculated from the ROI. Classification of MRI images into AD, MCI, and NC is based on predetermined values for compactness and circularity of CP. The study reveals average compactness values of 107.48, 82.34, and 66 for AD, NC, and MCI, respectively, with corresponding circularity values of 0.13, 0.16, and 0.22.

References

[1]
Qingze Zeng, Xiao Luo, Kaicheng Li, Shuyue Wang1, Ruiting Zhang, Hui Hong, Peiyu Huang, 2019. "Distinct spontaneous brain activity patterns in different biologically-defined Alzheimer's disease cognitive stage: a Preliminary study." Frontiers in Aging Neuroscience 11: 1-9.
[2]
Xianglian Meng, Yue Wu, Wenjie Liu, Ying Wang, and Zhe Xu and Zhuqing Jiao. 2022. "Research on Voxel-Based Features Detection and Analysis of Alzheimer's Disease Using Random Survey Support Vector Machine." Frontiers in Neuroinformatics 16: 1-12.
[3]
Zhe Wang, Yu Zheng, David C. Zhu, and Andrea C. Bozoki and Tongtong Li. 2018. "Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis." IEEE Journal of Translational Engineering in Health and Medicine 6: 1-9.
[4]
Mary Ellen Koran. 2019. "Neuroimaging and Alzheimer's Disease." Practical Neurology 61-70.
[5]
Yudong Zhang and Shuihua Wang. 2015. "Detection of Alzheimer's disease by displacement field and machine learning." PeerJ 3 (2015): 3: e1251.
[6]
Amir Abbas Tahami Monfared, Michael J. Byrnes, Leigh Ann White, and Quanwu Zhang. 2022. "Alzheimer's Disease: Epidemiology and Clinical Progression." Neurol Ther 11: 553-569.
[7]
Anirban Saha Kumar and PS Jagadeesh. 2014. " Improved Digital Image Processing based Detection for Alzheimer's disease using MATLAB ." International Journal on Recent Researches In Science, Engineering & Technology 12 (2): 1-7.
[8]
Shuang Liang and Yu Gu. 2021. "Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism." Sensors 21 (1): 1-15.
[9]
Shui-Hua Wang, Qinghua Zhou, and Ming Yang and Yu-Dong Zhang. 2021. "ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation." Frontiers in Aging Neuroscience 13 (article 687456): 1-15.
[10]
Yi RenvFung, Ziqiang Guan, Ritesh Kumar, and Joie Yeahuay Wu and Madalina Fiterau. 2019. "Alzheimer's Disease Brain MRI Classification: Challenges and Insights." arXiv:1906.04231v1 [eess.IV] IJCAI ARIAL workshop. Cornell University.
[11]
Suhad Al-Shoukry, Taha H Rassem and Nasrin M Makbol. 2020. "Alzheimer's diseases detection by using deep learning algorithms: a mini-review." IEEE Access 8: 77131-77141.
[12]
Rosa Chaves, Javier Ram´ırez, Juan M Gorriz, Ignacio Alvarez Illan, Manuel Gomez-Rio and Cristóbal Carnero-Pardo. 2012. " Effective diagnosis of Alzheimer's disease by means of large margin-based methodology." BMC Medical Informatics and Decision Making 79: 1-17.
[13]
Xiaowang Bi, Wei Liu, Huaiqin Liu and Qun Shang. 2021. "Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease." Journal of Healthcare Engineering 1-7.
[14]
Ethan Ocasio and Tim Q Duong, 2021. "Deep learning prediction of mild cognitive impairment conversion to Alzheimer's disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI." PeerJ Computer Science 7: e560.
[15]
"https://adni.loni.usc.edu/," [Online].
[16]
Tiago Azevedo, Richard A. I, Bethlehem David J. Whiteside, Nol Swaddiwudhipong, James B. Rowe, and Pietro Lió. 2023. "Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank." Communications Medicine 3:100: 1-15. https://doi.org/10.1038/s43856-023-00313-w | www.nature.com/commsmed.
[17]
Jones J, Mahmud A, and Baba Y (Accessed on 02 Dec 2023). 2023. "T1 weighted image." Radiopaedia. https://doi.org/10.53347/rID-5852.
[18]
Kinaan Javed, Vamsi Reddy, and Forshing Lui. 2023. Neuroanatomy, Choroid Plexus. StatPearls Publishing.
[19]
Jong Duck Choi, Yeonsil Moon, Hee-Jin Kim, Younghee Yim, Subin Lee and Won-Jin Moon. 2022. "Choroid Plexus Volume and Permeability at Brain MRI within the Alzheimer Disease Clinical Spectrum." Radiology 304 (3): 635-645.
[20]
Dayakshini Sathish, Sathish Kabekody, and Reshma K J. 2023. "Early Detection of Brain Tumor in MRI Images using Open by Reconstruction and Convolution Neural Networks." 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). Tiruchirappalli, India: IEEE. 1-7.
[21]
Pritpal Singh, Marcin Wątorek, Anna Ceglarek, Magdalena Fąfrowicz and Paweł Oświęcimka. 2022. “Analysis of fMRI Time Series: Neutrosophic- Entropy Based Clustering Algorithm”, Journal of Advances in Information Technology 1(3): 224-229.
[22]
Megha Madhukar, Arabinda K. Choudhary, Danielle K. Boal.  Mark S Dias, Mark R Iantosca. 2012. “Choroid plexus: normal size criteria on neuroimaging”. Surg Radiol Anat 34: 887–895. https://doi.org/10.1007/s00276-012-0980-5.

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ICSCA '24: Proceedings of the 2024 13th International Conference on Software and Computer Applications
February 2024
395 pages
ISBN:9798400708329
DOI:10.1145/3651781
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 30 May 2024

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