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Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT
2014
IEEE Transactions on Medical Imaging
We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using high-order steerable Riesz wavelets and support vector machines (SVM). The organization of scales and directions that are specific to every VST are modeled as linear combinations of directional Riesz wavelets. The models obtained are steerable, which
doi:10.1109/tmi.2014.2321347
pmid:24808406
pmcid:PMC4129229
fatcat:y32sv3lkdndjpox6phsi6t6q24