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Gaussian mixture model for texture characterization with application to brain DTI images

A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variabi...

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Autores principales: Moraru, Luminita, Moldovanu, Simona, Dimitrievici, Lucian Traian, Dey, Nilanjan, Ashour, Amira S., Shi, Fuqian, Fong, Simon James, Khan, Salam, Biswas, Anjan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413310/
https://www.ncbi.nlm.nih.gov/pubmed/30899585
http://dx.doi.org/10.1016/j.jare.2019.01.001
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author Moraru, Luminita
Moldovanu, Simona
Dimitrievici, Lucian Traian
Dey, Nilanjan
Ashour, Amira S.
Shi, Fuqian
Fong, Simon James
Khan, Salam
Biswas, Anjan
author_facet Moraru, Luminita
Moldovanu, Simona
Dimitrievici, Lucian Traian
Dey, Nilanjan
Ashour, Amira S.
Shi, Fuqian
Fong, Simon James
Khan, Salam
Biswas, Anjan
author_sort Moraru, Luminita
collection PubMed
description A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm(2)) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres.
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spelling pubmed-64133102019-03-21 Gaussian mixture model for texture characterization with application to brain DTI images Moraru, Luminita Moldovanu, Simona Dimitrievici, Lucian Traian Dey, Nilanjan Ashour, Amira S. Shi, Fuqian Fong, Simon James Khan, Salam Biswas, Anjan J Adv Res Original Article A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm(2)) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres. Elsevier 2019-01-04 /pmc/articles/PMC6413310/ /pubmed/30899585 http://dx.doi.org/10.1016/j.jare.2019.01.001 Text en © 2019 The Authors. Published by Elsevier B.V. on behalf of Cairo University. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Moraru, Luminita
Moldovanu, Simona
Dimitrievici, Lucian Traian
Dey, Nilanjan
Ashour, Amira S.
Shi, Fuqian
Fong, Simon James
Khan, Salam
Biswas, Anjan
Gaussian mixture model for texture characterization with application to brain DTI images
title Gaussian mixture model for texture characterization with application to brain DTI images
title_full Gaussian mixture model for texture characterization with application to brain DTI images
title_fullStr Gaussian mixture model for texture characterization with application to brain DTI images
title_full_unstemmed Gaussian mixture model for texture characterization with application to brain DTI images
title_short Gaussian mixture model for texture characterization with application to brain DTI images
title_sort gaussian mixture model for texture characterization with application to brain dti images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413310/
https://www.ncbi.nlm.nih.gov/pubmed/30899585
http://dx.doi.org/10.1016/j.jare.2019.01.001
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