A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas.
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ABSTRACT:
SUBMITTER: Chekouo T
PROVIDER: S-EPMC7554657 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
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A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas.
Chekouo Thierry T Mohammed Shariq S Rao Arvind A
NeuroImage. Clinical 20200918
In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional ...[more]
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