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. 2021 Jun 11;3(4):e200157. doi: 10.1148/rycan.2021200157

Figure 3:

Low-dimensional radiomic feature patterns. The reduced radiomic feature space, Ƒr, following the unsupervised dimensionality reduction and feature selection operation, Ƒ→Ƒr, according to Equation (3). Column vectors of Ƒr denote low-dimensional radiomic signatures, and row vectors of Ƒr represent a subset of radiomic features from the complete feature space, Ƒ, that maximize the separation of patients into clusters and minimize the redundancy within those clusters. GLCOM = gray level co-occurrence matrix.

Low-dimensional radiomic feature patterns. The reduced radiomic feature space, Ƒr, following the unsupervised dimensionality reduction and feature selection operation, Ƒ→Ƒr, according to Equation (3). Column vectors of Ƒr denote low-dimensional radiomic signatures, and row vectors of Ƒr represent a subset of radiomic features from the complete feature space, Ƒ, that maximize the separation of patients into clusters and minimize the redundancy within those clusters. GLCOM = gray level co-occurrence matrix.