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Sensing and Processing for Medical Imaging: Methods and Applications
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Sensing and Processing for Medical Imaging: Methods and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1310

Special Issue Editor

Dr. Saed Moradi

E-Mail Website
Guest Editor
Systems Design Department, University of Waterloo, Waterloo, ON, Canada
Interests: computer vision; 3D computer vision; digital image processing; digital signal processing; low-power IC design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites original research articles, comprehensive reviews, and short communications that focus on the processing of medical images, from acquisition to analysis. With the goal of enhancing diagnostic accuracy, treatment planning, and clinical decision-making. We seek contributions that advance the state of the art in computational methods and algorithms used to process medical imaging data across various modalities.

We welcome submissions in, but not limited to, the following areas:

  • Medical Image Acquisition and Reconstruction Algorithms: innovative computational methods for improving image quality during or after acquisition, including motion correction, compressed sensing, and deep learning-based reconstruction.
  • Image Enhancement Techniques: algorithms for denoising, artifact removal, contrast enhancement, and super-resolution to improve the clarity and diagnostic value of medical images.
  • Segmentation and Registration: advanced techniques for delineating anatomical structures and pathologies, as well as aligning images across time points or modalities.
  • Quantitative Image Analysis and Interpretation: methods for extracting meaningful features, performing statistical analysis, and applying machine learning or deep learning for classification, detection, and prognosis.
  • Multi-Modal Image Fusion and Integration: computational approaches for combining data from different imaging modalities (e.g., MRI, CT, PET) to provide richer diagnostic insights.
  • Large Language Models (LLMs) and Vision-Language Models (VLMs): applications of LLMs and multi-modal VLMs in medical image processing, including report generation, image-text alignment, clinical decision support, and multi-modal reasoning.
  • Computer-Aided Diagnosis (CAD) and Decision Support: development of intelligent systems that assist clinicians in interpreting images and planning treatments.

Review articles should provide a balanced and up-to-date overview of specific image processing techniques or applications in medical imaging, highlighting key developments and future directions.

We look forward to your participation in this Special Issue.

For any enquiries, please contact the Special Issue editor, Silvia Li, at silvia.li@mdpi.com.

Dr. Saed Moradi
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors is an international peer-reviewed open access semimonthly 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

  • medical image processing
  • image reconstruction
  • image enhancement
  • image segmentation
  • image registration
  • multi-modal image fusion
  • deep learning in medical imaging
  • machine learning for diagnosis
  • vision–language models (VLMs)
  • large language models (LLMs)
  • multi-modal learning
  • computer-aided diagnosis (CAD)
  • medical image analysis
  • clinical decision support
  • AI in radiology
  • medical imaging applications
  • quantitative imaging
  • image interpretation
  • medical imaging informatics
  • medical vision transformers

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 2914 KB  
Article
Global-Token U-Net with Hybrid Loss for Trustworthy Medical Image Super-Resolution
by Jiaqi Shang, Zhiyuan Xu and Dongdong Wang
Sensors 2026, 26(5), 1454; https://doi.org/10.3390/s26051454 - 26 Feb 2026
Viewed by 302
Abstract
Super-resolution technology significantly enhances the visual quality of low-resolution medical images, resulting in ultra-high-resolution clear images. Super-resolution technology based on artificial intelligence has achieved great success in reconstruction quality. However, like the image restoration task, super-resolution is also an ill-posed problem, and current [...] Read more.
Super-resolution technology significantly enhances the visual quality of low-resolution medical images, resulting in ultra-high-resolution clear images. Super-resolution technology based on artificial intelligence has achieved great success in reconstruction quality. However, like the image restoration task, super-resolution is also an ill-posed problem, and current work lacks consideration of trustworthiness. Medical image super-resolution needs to ensure clarity and, more importantly, to ensure that the output image is reliable and does not produce false details and mislead the diagnosis. To address the trustworthy issue of medical image super-resolution, we design a novel hybrid loss that combines a hinge-based adversarial term with a PSNR-based regularization. In the designed loss function, the adversarial term makes the reconstructed result close to the distribution of the true high-resolution image, thus generating more refined high-frequency textures, while the PSNR-based regularization term explicitly reduces the deviation from the ground truth. We apply this loss in the global-token U-Net backbone network and add a lightweight VGG as the discriminator for adversarial terms. We empirically verify that integrating the proposed methods can enhance the trustworthiness of medical image super-resolution technology while maintaining high reconstruction quality. Full article
(This article belongs to the Special Issue Sensing and Processing for Medical Imaging: Methods and Applications)
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16 pages, 1725 KB  
Article
A Lightweight Modified Adaptive UNet for Nucleus Segmentation
by Md Rahat Kader Khan, Tamador Mohaidat and Kasem Khalil
Sensors 2026, 26(2), 665; https://doi.org/10.3390/s26020665 - 19 Jan 2026
Viewed by 661
Abstract
Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. [...] Read more.
Cell nucleus segmentation in microscopy images is an initial step in the quantitative analysis of imaging data, which is crucial for diverse biological and biomedical applications. While traditional machine learning methodologies have demonstrated limitations, recent advances in U-Net models have yielded promising improvements. However, it is noteworthy that these models perform well on balanced datasets, where the ratio of background to foreground pixels is equal. Within the realm of microscopy image segmentation, state-of-the-art models often encounter challenges in accurately predicting small foreground entities such as nuclei. Moreover, the majority of these models exhibit large parameter sizes, predisposing them to overfitting issues. To overcome these challenges, this study introduces a novel architecture, called mA-UNet, designed to excel in predicting small foreground elements. Additionally, a data preprocessing strategy inspired by road segmentation approaches is employed to address dataset imbalance issues. The experimental results show that the MIoU score attained by the mA-UNet model stands at 95.50%, surpassing the nearest competitor, UNet++, on the 2018 Data Science Bowl dataset. Ultimately, our proposed methodology surpasses all other state-of-the-art models in terms of both quantitative and qualitative evaluations. The mA-UNet model is also implemented in VHDL on the Zynq UltraScale+ FPGA, demonstrating its ability to perform complex computations with minimal hardware resources, as well as its efficiency and scalability on advanced FPGA platforms. Full article
(This article belongs to the Special Issue Sensing and Processing for Medical Imaging: Methods and Applications)
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