Fusion of Magnetic Resonance and Ultrasound Images for Endometriosis Detection

Oumaima El Mansouri, El Mansouri O, Fabien Vidal, Vidal F, Adrian Basarab, Pierre Payoux, Payoux P, Denis Kouamé, Denis Kouame, Kouame D, Jean‐Yves Tourneret, Tourneret JY, 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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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 · read from full text ⓘ

I can’t access the paper content: the provided text is an anti-scraping/Proof-of-Work interstitial page from the website rather than study methods, results, or conclusions. Because the actual manuscript details are missing, there’s no information to summarize about the population, imaging methods (e.g., MRI/ultrasound fusion), performance findings, or limitations stated by the authors. The paper cannot be accurately summarized without the full text. This paper is centrally about endometriosis — it is titled around fusing magnetic resonance and ultrasound images for endometriosis detection, but the underlying study content was not provided.

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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)

Cited by (3)

References (39)

Cited by (3)

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europepmc
last seen: 2026-07-30T06:25:42.655704+00:00
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License: CC0 · commercial use OK