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Life
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Image Analysis and Postprocessing in Medical Imaging
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Image Analysis and Postprocessing in Medical Imaging

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: closed (29 August 2025) | Viewed by 800

Special Issue Editor

Dr. Federico Bruno

E-Mail Website
Guest Editor
Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
Interests: neuroradiology; MRI; focused ultrasound; tremor; spine imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a revolution in the approach to radiological semeiotics in all fields of imaging.

Modern cutting-edge imaging technologies, from ultra-high-resolution US to spectral and photon counting CT and the countless advanced MRI sequences and modern hybrid modalities, generate vast amounts of high-resolution multiparametric data, often with complex anatomical and functional information.

Moreover, the actual advanced integration of Artificial intelligence and machine learning algorithms in medical imaging are transforming image analysis by enabling automation, improving image quality and accuracy in image segmentation, anomaly detection, and radiomic feature extraction.

In this scenario, the demand for robust, efficient, and reliable image analysis and reconstruction techniques has never been greater, requiring the translation of imaging data into clinically actionable insights, with a potential role in precision medicine in facilitating personalized treatment planning, disease monitoring, and risk stratification.

This Special Issue aims to highlight the growing importance of image postprocessing and analysis by providing a platform for interdisciplinary discourse among radiologists, biomedical engineers, computer scientists, and clinical practitioners. By bringing together these various perspectives, this issue will showcase state-of-the-art methodologies, explore their applications, and identify future challenges in the field.

Dr. Federico Bruno
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative imaging biomarkers
  • image segmentation
  • artificial intelligence in radiology
  • image postprocessing

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Published Papers (1 paper)

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Research

19 pages, 1873 KB  
Article
Optimization of the Non-Local Means Algorithm for Breast Diffusion-Weighted Magnetic Resonance Imaging Using a 3D-Printed Breast-Mimicking Phantom
by Soungmo Park, Seong-Hyeon Kang and Youngjin Lee
Life 2025, 15(9), 1373; https://doi.org/10.3390/life15091373 - 29 Aug 2025
Viewed by 452
Abstract
Diffusion-weighted magnetic resonance (DWMR) images were acquired using a custom-designed, 3D-printed breast-mimicking phantom. The smoothing factor of the non-local means (NLM) algorithm was then optimized for noise reduction. Phantoms were fabricated using polylactic acid, polyethylene terephthalate, and various concentrations of polyvinylpyrrolidone. DWMR images [...] Read more.
Diffusion-weighted magnetic resonance (DWMR) images were acquired using a custom-designed, 3D-printed breast-mimicking phantom. The smoothing factor of the non-local means (NLM) algorithm was then optimized for noise reduction. Phantoms were fabricated using polylactic acid, polyethylene terephthalate, and various concentrations of polyvinylpyrrolidone. DWMR images were obtained across b-values ranging from zero to 2000 s/mm2. Based on image contrast, the NLM algorithm was applied to the b = 1000 s/mm2 image, testing smoothing factors from 0.001 to 0.150. The NLM algorithm’s performance was quantitatively evaluated using a single DWMR image acquired from this custom phantom. At the optimized smoothing factor, the signal-to-noise ratio (SNR) improved from 96.87 ± 3.42 to 215.81 ± 4.18, and the contrast-to-noise ratio (CNR) from 43.63 ± 2.97 to 131.98 ± 3.56, representing 2.22-fold and 3.02-fold enhancements, respectively. No formal statistical tests were conducted as the analysis was based on a single acquisition. The optimized NLM algorithm also outperformed conventional denoising methods—median, Wiener, and total variation—in both noise suppression and contrast preservation. These findings suggest that the NLM algorithm with optimized parameters is likely to be more effective than existing approaches for enhancing breast DWMR image quality. However, further validation using in vivo patient datasets is essential to confirm its diagnostic utility and clinical generalizability because of the absence of tissue heterogeneity, motion, and physiological noise in the phantom environment. Full article
(This article belongs to the Special Issue Image Analysis and Postprocessing in Medical Imaging)
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