Fusion of Magnetic Resonance and Ultrasound Images for Endometriosis Detection

Oumaima El Mansouri, Fabien Vidal, Adrian Basarab, Pierre Payoux, Denis Kouamé, Denis Kouame, Jean-Yves Tourneret, Jean‐Yves Tourneret
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2020 · vol. 29 , pp. 5324–5335 · doi:10.1109/tip.2020.2975977 · PMID:32142435 · W3008590643
article OA: green CC0 ⤵ 3 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This paper presents a novel MR and US image fusion algorithm, solving inverse problems for super-resolution and denoising, to improve endometriosis detection accuracy.

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Abstract

This paper introduces a new fusion method for magnetic resonance (MR) and ultrasound (US) images, which aims at combining the advantages of each modality, i.e., good contrast and signal to noise ratio for the MR image and good spatial resolution for the US image. The proposed algorithm is based on two inverse problems, performing a super-resolution of the MR image and a denoising of the US image. A polynomial function is introduced to model the relationships between the gray levels of the two modalities. The resulting inverse problem is solved using a proximal alternating linearized minimization framework. The accuracy and the interest of the fusion algorithm are shown quantitatively and qualitatively via evaluations on synthetic and experimental phantom data.
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Condition tags

endometriosis

Citation neighborhood (sparse)

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Cites (3)

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References (39)

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europepmc
last seen: 2026-06-04T01:30:01.192114+00:00
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License: CC0 · commercial use OK