ArticleJournal of advanced research2019
Gaussian mixture model for texture characterization with application to brain DTI images.
Article in Journal of advanced research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Surface-Enhanced Raman Scattering Imaging Assisted by Machine Learning Analysis: Unveiling Pesticide Molecule Permeation in Crop Tissues.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates.Journal of imaging · 2023Article
- Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.Earth and space science (Hoboken, N.J.) · 2021Article
- Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods.Frontiers in cardiovascular medicine · 2021Review
- A Medical Image Registration Method Based on Progressive Images.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
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
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Registered trials
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