Abstract
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 and popular radiomics operators for classifying 3D textures reveals the importance of combining the two properties for optimal pattern characterization.
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Notes
- 1.
It is worth noting that steps (i) and (ii) are repeated multiple times in Convolutional Neural Networks (CNN).
- 2.
The matrix \(S_{n,\mathcal {R}}\) is called the steering matrix in the literature [7]. We recovered the well-known property that the steering matrix of spherical harmonics is orthogonal.
- 3.
Considered popular operators are those included in radiomics libraries including pyRadiomics, TexRAD, IBEX, CERR, MAZDA, QIFE, LifeX and QuantImage.
- 4.
https://radiomics.hevs.ch, as of June 2018.
- 5.
https://pyradiomics.readthedocs.io, as of June 2018.
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Acknowledgements
This work was supported by the Swiss National Science Foundation (grants PZ00P2_154891 and 205320_179069).
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Depeursinge, A., Fageot, J., Andrearczyk, V., Ward, J.P., Unser, M. (2018). Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_13
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