Image analysis in the medical field aims to offer tools for the diagnosis and detection of life-threatening illness. This study means to propose a novel content-based image retrieval system oriented to medical diagnosis. In particular, we exploit several classic and deep image descriptors together with different similarity measures on three different data set, containing computed tomography and magnetic resonance images. Experiments show that feature selection can bring benefit if applied to deep and texture features, contrary to what observed for invariant moments. Moreover, the cityblock distance emerged to be quite suitable overall in this domain, although some other distances also exhibit satisfying robustness.

Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval

Putzu L.
;
Loddo A.
;
2021-01-01

Abstract

Image analysis in the medical field aims to offer tools for the diagnosis and detection of life-threatening illness. This study means to propose a novel content-based image retrieval system oriented to medical diagnosis. In particular, we exploit several classic and deep image descriptors together with different similarity measures on three different data set, containing computed tomography and magnetic resonance images. Experiments show that feature selection can bring benefit if applied to deep and texture features, contrary to what observed for invariant moments. Moreover, the cityblock distance emerged to be quite suitable overall in this domain, although some other distances also exhibit satisfying robustness.
978-3-030-89127-5
978-3-030-89128-2
Biomedical image retrieval
CBIR
Deep features
Hand-crafted features
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/322889
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