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

Rosario Corso (), Alessandro Stefano, Giuseppe Salvaggio and Albert Comelli
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Rosario Corso: Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, 90123 Palermo, Italy
Alessandro Stefano: Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
Giuseppe Salvaggio: Department of Biomedicine, Neuroscience and Advanced Diagnostics—Section of Radiology, University Hospital “Paolo Giaccone”, 90127 Palermo, Italy
Albert Comelli: Ri.MED Foundation, 90133 Palermo, Italy

Mathematics, 2024, vol. 12, issue 9, 1-19

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.

Keywords: shearlet transform; wavelet transform; radiomics; prostate cancer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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