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Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization
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Author(s): Lilia Lazli (CoFaMic Research Centre, Department of Computer Science, UQÀM, University of Quebec, Canada & Department of Electrical Engineering, ÉTS, University of Quebec, Canada)and Mounir Boukadoum (CoFaMic Research Centre, Department of Computer Science, UQÀM, University of Quebec, Canada)
Copyright: 2019
Volume: 7
Issue: 1
Pages: 25
Source title:
International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2019010107
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Abstract
Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.
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