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J. Imaging
Special Issues
Learning and Optimization for Medical Imaging
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Learning and Optimization for Medical Imaging

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3620

Special Issue Editors

Dr. Simona Moldovanu

E-Mail
Guest Editor
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
Interests: computer vision; machine learning; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Elena Morotti

E-Mail Website
Guest Editor
Department of Political and Social Sciences, University of Bologna, 40125 Bologna, Italy
Interests: image processing; tomography; optimization; neural networks; data mining.

Special Issue Information

Dear Colleagues,

We all know that imaging plays an essential role in medicine today: radiography, different kinds of tomography, magnetic resonance and echography (just to mention the most widespread imaging technologies) play a central role in modern medicine by supporting the diagnosis and treatment of a disease.

At the heart of every medical imaging technology, there is a sophisticated algorithm to reconstruct an image from the measured data, according to a physics-driven model of the measurement process. Moreover, post-processing applications such as denoising, deblurring, image segmentation and object detection are crucial to improve the effectiveness of diagnosis and treatment. The latest technologies have introduced new challenges. Some of these problems are mathematically characterized as inverse problems. Optimization models and algorithms have been occupying a central stage in imaging applications; recently, deep learning approaches have been widely introduced to face the new challenges produced by the latest technologies.

We invite authors to submit original research papers related to different medical imaging applications. Papers focusing on numerical optimization and regularization algorithms, as well as contributions proposing new, robust deep-learning-based techniques, are welcome.

Dr. Simona Moldovanu 
Dr. Elena Morotti
Guest Editors

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. Journal of Imaging 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 1800 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 imaging
  • optimization methods
  • regularization methods
  • neural networks
  • machine learning
  • deep learning
  • image reconstruction
  • image deblur
  • image denoise
  • image segmentation
  • object detection
  • tomography
  • magnetic resonance imaging
  • echography
  • echocardiography
  • ultrasound imaging
  • spectral imaging
  • thermal images
  • X-ray imaging
  • computed tomography

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

Published Papers (3 papers)

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Research

11 pages, 1878 KiB  
Article
Typical Diagnostic Reference Levels of Radiation Exposure on Neonates Under 1 kg in Mobile Chest Imaging in Incubators
by Ioannis Antonakos, Matina Patsioti, Maria-Eleni Zachou, George Christopoulos and Efstathios P. Efstathopoulos
J. Imaging 2025, 11(3), 74; https://doi.org/10.3390/jimaging11030074 - 28 Feb 2025
Viewed by 556
Abstract
The purpose of this study is to determine the typical diagnostic reference levels (DRLs) of radiation exposure values for chest radiographs in neonates (<1 kg) in mobile imaging at a University Hospital in Greece and compare these values with the existing DRL values [...] Read more.
The purpose of this study is to determine the typical diagnostic reference levels (DRLs) of radiation exposure values for chest radiographs in neonates (<1 kg) in mobile imaging at a University Hospital in Greece and compare these values with the existing DRL values from the literature. Patient and dosimetry data, including age, sex, weight, tube voltage (kV), tube current (mA), exposure time (s), exposure index of a digital detector (S), and dose area product (DAP) were obtained from a total of 80 chest radiography examinations performed on neonates (<1 kg and <30 days old). All examinations were performed in a single X-ray system, and all data (demographic and dosimetry data) were collected from the PACS of the hospital. Typical radiation exposure values were determined as the median value of DAP and ESD distribution. Afterward, these typical values were compared with DRL values from other countries. Three radiologists reviewed the images to evaluate image quality for dose optimization in neonatal chest radiography. From all examinations, the mean value and standard deviation of DAP was 0.13 ± 0.11 dGy·cm2 (range: 0.01–0.46 dGy·cm2), and ESD was measured at 11.55 ± 4.96 μGy (range: 4.01–30.4 μGy). The typical values in terms of DAP and ESD were estimated to be 0.08 dGy·cm2 and 9.87 μGy, respectively. The results show that the DAP value decreases as the exposure index increases. This study’s typical values were lower than the DRLs reported in the literature because our population had lower weight and age. From the subjective evaluation of image quality, it was revealed that the vast majority of radiographs (over 80%) met the criteria for being diagnostic as they received an excellent rating in terms of noise levels, contrast, and sharpness. This study contributes to the recording of typical dose values in a sensitive and rare category of patients (neonates weighing <1 kg) as well as information on the image quality of chest X-rays that were performed in this group. Full article
(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)
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19 pages, 7485 KiB  
Article
Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization
by Sofia El Amoury, Youssef Smili and Youssef Fakhri
J. Imaging 2025, 11(2), 31; https://doi.org/10.3390/jimaging11020031 - 24 Jan 2025
Cited by 1 | Viewed by 941
Abstract
The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, diverse sizes, and subtle textural differences. Traditional convolutional neural [...] Read more.
The classification of brain tumors using MRI scans is critical for accurate diagnosis and effective treatment planning, though it poses significant challenges due to the complex and varied characteristics of tumors, including irregular shapes, diverse sizes, and subtle textural differences. Traditional convolutional neural network (CNN) models, whether handcrafted or pretrained, frequently fall short in capturing these intricate details comprehensively. To address this complexity, an automated approach employing Particle Swarm Optimization (PSO) has been applied to create a CNN architecture specifically adapted for MRI-based brain tumor classification. PSO systematically searches for an optimal configuration of architectural parameters—such as the types and numbers of layers, filter quantities and sizes, and neuron numbers in fully connected layers—with the objective of enhancing classification accuracy. This performance-driven method avoids the inefficiencies of manual design and iterative trial and error. Experimental results indicate that the PSO-optimized CNN achieves a classification accuracy of 99.19%, demonstrating significant potential for improving diagnostic precision in complex medical imaging applications and underscoring the value of automated architecture search in advancing critical healthcare technology. Full article
(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)
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14 pages, 2392 KiB  
Article
Convolutional Neural Network–Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification
by Simona Moldovanu, Gigi Tăbăcaru and Marian Barbu
J. Imaging 2024, 10(9), 235; https://doi.org/10.3390/jimaging10090235 - 20 Sep 2024
Cited by 5 | Viewed by 1463
Abstract
This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form [...] Read more.
This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form in any part. With MRI technology, cross-sectional images are generated, and radiologists can detect the abnormalities. When the size of the tumour is very small, it is undetectable to the human visual system, necessitating alternative analysis using AI tools. As is widely known, CNNs explore the structure of an image and provide features on the SoftMax fully connected (SFC) layer, and the classification of the items that belong to the input classes is established. Two comparison studies for the classification of meningioma tumours and healthy brains are presented in this paper: (i) classifying MRI images using an original CNN and two pre-trained CNNs, DenseNet169 and EfficientNetV2B0; (ii) determining which CNN and ML combination yields the most accurate classification when SoftMax is replaced with three ML models; in this context, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were proposed. In a binary classification of tumours and healthy brains, the EfficientNetB0-SVM combination shows an accuracy of 99.5% on the test dataset. A generalisation of the results was performed, and overfitting was prevented by using the bagging ensemble method. Full article
(This article belongs to the Special Issue Learning and Optimization for Medical Imaging)
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