Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

Abstract

For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts.

New Parametric 2D Curves for Modeling Prostate Shape in Magnetic Resonance Images

“…Popular methods for deep learning segmentation are based on convolutional neural networks, encoder-decoder and autoencoder models or generative adversarial networks [37]. More generally, artificial intelligence techniques are used in the medical field not only for segmentation but also for classification and prediction (radiomics) [38][39][40][41][42].…”