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Published on: June 9, 2018
Histogram-based features track Alzheimer's progression in brain MRI
Nikaash Pasnoori1, Thania Flores-Garcia2, Buket D Barkana3
1Biomedical Engineering Department, University of Bridgeport, Bridgeport, CT, 06604, USA.
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.
Insights
This study introduces an adaptive algorithm to detect and track neurodegeneration stages in Alzheimer's disease using MRI. The method successfully identifies disease progression, aiding early diagnosis and patient management.
Area of Science:
- Neuroscience
- Medical Imaging
- Computer Science
Background:
- Alzheimer's disease (AD) is a progressive dementia characterized by neurodegeneration, amyloid plaques, and neurofibrillary tangles.
- Early detection of AD is crucial for patient outcomes, with Magnetic Resonance Imaging (MRI) playing a key role in assessing neurodegeneration.
- Current computer-aided diagnosis tools face challenges in differentiating early dementia stages due to overlapping characteristics.
Purpose of the Study:
- To develop an adaptive multi-thresholding algorithm for identifying and tracking neurodegeneration progression in Alzheimer's disease.
- To define novel features for quantifying neurodegeneration stages from MRI data.
- To evaluate the efficacy of the proposed algorithm and features in classifying different stages of dementia.
Main Methods:
- An adaptive multi-thresholding algorithm based on smoothed histogram morphology was developed.
- Features such as gray/white matter volume, statistical moments, shrinkage, and geometric measures were mathematically derived.
- Multiple machine learning classifiers (Decision Tree, SVM, Naïve Bayes, KNN, Ensemble, Neural Network) were employed for evaluation.
Main Results:
- The proposed algorithm successfully defined features capable of identifying neurodegeneration.
- The derived features enabled tracking of disease progression through distinct stages: non, very mild, mild, and moderate.
- Experimental results demonstrated the successful labeling of neurodegeneration stages using the developed methodology.
Conclusions:
- The developed adaptive multi-thresholding algorithm and derived features show promise for accurate staging of neurodegeneration in Alzheimer's disease.
- This approach can aid clinicians in early diagnosis and monitoring disease progression.
- The methodology offers a quantitative tool for assessing the impact of Alzheimer's disease on the brain.
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01:24Magnetic Resonance Imaging
01:26Alzheimer's Disease: Overview
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