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Fractal dimension for classifying 3D brain MRI using improved triangle box-counting method
Author(s)
Date Issued
February 23, 2017
Type
Conference Paper
Abstract
Although many papers have used fractal dimension (FD) to analyze magnetic resonance imaging (MRI) for detecting various brain diseases, especially Alzheimer's disease (AD), they have been unsuccessful to classify the AD patients in case of healthy and AD brain-MRIs. The significant problems are from (i) the lack of the efficient FD estimation method and (ii) the failure of applying statistical analysis to discriminate the subjects in MRIs. Therefore, this paper proposes an alternative way to overcome these problems by using an improved triangle box-counting method (ITBC) for effective FD estimation and using machine learning for brain-MRI discrimination. The proposed method is evaluated its performance with the Alzheimer's disease patient discrimination dataset of open access series of imaging studies (OASIS). The experimental results show that the pro-posed method can achieve the classification accuracy rate up to 86.20% whereas the statistical analysis approaches cannot discriminate healthy and AD.
Citation
Proceedings of 2016 8th International Conference on Information Technology and Electrical Engineering Empowering Technology for Better Future Icitee 2016, 2017