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

• Published in: Journal of advanced research Vol. 16; pp. 15 - 23
• Main Authors: Moraru, Luminita, Moldovanu, Simona, Dimitrievici, Lucian Traian, Dey, Nilanjan, Ashour, Amira S., Shi, Fuqian, Fong, Simon James, Khan, Salam, Biswas, Anjan
• Format: Journal Article
• Language: English
• Published: Egypt Elsevier B.V 01.03.2019; Elsevier
• Subjects: Brain hemispheres; Cluster validity; Clustering; Gaussian mixture model; Original; Weight distribution; Weighted Euclidean distance; Weight distribution; Brain hemispheres; Cluster validity; Clustering; Weighted Euclidean distance; Gaussian mixture model
• ISSN: 2090-1232; 2090-1224
• DOI: 10.1016/j.jare.2019.01.001

[Display omitted] •A Gaussian mixture model to classify the pixel distribution of main brain tissues is introduced.•A hemisphere approach is proposed.•Mixing probabilities at the sub-class and class levels are estimated.•The k-means algorithm optimizes the parameters of the mixture distributions.•A difference in the mixing probabilities between hemispheres is determined. 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/mm2) 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.