Abstract:
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Radiomics, as an emerging field in quantitative imaging, encompasses a broad class of analytical techniques. GLCM, as one type of texture features, is a matrix defined over an image to be the distribution of co-occurring gray-level pixels at a given offset and angle. GLCM-based texture features have been used to quantitatively describe tumor phenotypes. Recent literature have interrogated associations with clinical/pathology information with Machine Learning algorithms using GLCM-based texture features. Reducing the multivariate functional structure to a set of summary statistics is potentially reductive, however, masking patterns that describe disease pathogenesis. In this article, we present a Bayesian probabilistic method for count data observed over a lattice with symmetric structure. The approach was utilized to model the entire GLCM as a multivariate response surface, and applied in a cancer detection context to discriminant malignant from benign adrenal lesions using GLCMs arising from pixel-level image data. In case study and simulation study, our proposed method improved classification accuracy compared to current approaches utilizing GLCM-based texture features.
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