We define and investigate the Local Rotation Invariance (LRI) and Directional Sensitivity (DS) of radiomics features. Most of the classical features cannot combine the two properties, which are antagonist in simple designs. We propose texture operators based on spherical harmonic wavelets (SHW) invariants and show that they are both LRI and DS. An experimental comparison of SHW, popular radiomics operators and O-group equivariant Convolutional Neural Networks (CNNs) for classifying 3D textures reveals the importance of combining the two properties for optimal pattern characterization.
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