Histogram-based features track Alzheimer's progression in brain MRI
Abstract Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegener...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50631-1 |
| _version_ | 1797363367513423872 |
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| author | Nikaash Pasnoori Thania Flores-Garcia Buket D. Barkana |
| author_facet | Nikaash Pasnoori Thania Flores-Garcia Buket D. Barkana |
| author_sort | Nikaash Pasnoori |
| collection | DOAJ |
| description | Abstract Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegeneration during the disease. Computer-aided image processing tools have been used to aid medical professionals in ascertaining a diagnosis of Alzheimer's in its early stages. As characteristics of non and very-mild dementia stages overlap, tracking the progression is challenging. Our work developed an adaptive multi-thresholding algorithm based on the morphology of the smoothed histogram to define features identifying neurodegeneration and track its progression as non, very mild, mild, and moderate. Gray and white matter volume, statistical moments, multi-thresholds, shrinkage, gray-to-white matter ratio, and three distance and angle values are mathematically derived. Decision tree, discriminant analysis, Naïve Bayes, SVM, KNN, ensemble, and neural network classifiers are designed to evaluate the proposed methodology with the performance metrics accuracy, recall, specificity, precision, F1 score, Matthew’s correlation coefficient, and Kappa values. Experimental results showed that the proposed features successfully label the neurodegeneration stages. |
| first_indexed | 2024-03-08T16:19:22Z |
| format | Article |
| id | doaj.art-615c763d57ee47a8a08059b47df15335 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T16:19:22Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-615c763d57ee47a8a08059b47df153352024-01-07T12:25:25ZengNature PortfolioScientific Reports2045-23222024-01-0114111210.1038/s41598-023-50631-1Histogram-based features track Alzheimer's progression in brain MRINikaash Pasnoori0Thania Flores-Garcia1Buket D. Barkana2Biomedical Engineering Department, University of BridgeportComputer Engineering Department, University of BridgeportBiomedical Engineering Department, The University of AkronAbstract Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegeneration during the disease. Computer-aided image processing tools have been used to aid medical professionals in ascertaining a diagnosis of Alzheimer's in its early stages. As characteristics of non and very-mild dementia stages overlap, tracking the progression is challenging. Our work developed an adaptive multi-thresholding algorithm based on the morphology of the smoothed histogram to define features identifying neurodegeneration and track its progression as non, very mild, mild, and moderate. Gray and white matter volume, statistical moments, multi-thresholds, shrinkage, gray-to-white matter ratio, and three distance and angle values are mathematically derived. Decision tree, discriminant analysis, Naïve Bayes, SVM, KNN, ensemble, and neural network classifiers are designed to evaluate the proposed methodology with the performance metrics accuracy, recall, specificity, precision, F1 score, Matthew’s correlation coefficient, and Kappa values. Experimental results showed that the proposed features successfully label the neurodegeneration stages.https://doi.org/10.1038/s41598-023-50631-1 |
| spellingShingle | Nikaash Pasnoori Thania Flores-Garcia Buket D. Barkana Histogram-based features track Alzheimer's progression in brain MRI Scientific Reports |
| title | Histogram-based features track Alzheimer's progression in brain MRI |
| title_full | Histogram-based features track Alzheimer's progression in brain MRI |
| title_fullStr | Histogram-based features track Alzheimer's progression in brain MRI |
| title_full_unstemmed | Histogram-based features track Alzheimer's progression in brain MRI |
| title_short | Histogram-based features track Alzheimer's progression in brain MRI |
| title_sort | histogram based features track alzheimer s progression in brain mri |
| url | https://doi.org/10.1038/s41598-023-50631-1 |
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