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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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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. |
format | Online Article Text |
id | pubmed-6413310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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