Special Issue "Intelligent Strategies for Medical Image Analysis"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Giuseppe Placidi
E-Mail Website
Guest Editor
A2VI-Lab, c/o Dept of Life, Health & Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
Interests: medical imaging; image sampling, reconstruction, processing, and compression; artificial intelligence; inverse filtering
Prof. Dr. Mrinal Mandal
E-Mail Website
Guest Editor
Digital Image Analysis Laboratory, Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 1H9, AB, Canada
Interests: medical image analysis; computer-aided diagnosis; computer vision; machine learning; deep learning
Dr. Mustapha Bouhrara
E-Mail Website
Guest Editor
MRPAD Unit, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, 251 Bayview Blvd., Baltimore 21224 MD, USA.
Interests: MR physics; neuroimaging; signal processing; image filtering; inverse problem; aging

Special Issue Information

Dear Colleagues,

Medical imaging (MI) is now an explosive field since the technologies for visualizing the body structure and functions have become increasingly various and powerful. Imaging is at the core of medical practice, as most patients are likely to undergo imaging scans during care both for diagnostic and disease monitoring purposes. Automated intelligent postprocessing analyses are fundamental for MI where images are the result of numerical processing of raw data to extract meaningful information and have wide application in all aspects, including sampling and reconstruction, filtering, compression, processing, registration, fusion, and interpretation. In recent years, in addition to classical tools, artificial intelligence is increasingly playing a decisive role in optimizing and lowering time acquisition to reduce exposure to potentially harmful radiations; reduce motion artifacts and noise; improve image quality; increase information by registering images from different modalities; identify image structures; objectively monitor disease progression; and facilitate analysis and early detection of diseases. This Special Issue aims to explore innovative advanced techniques for MI in different fields, including but not limited to MRI and fMRI, CT, EEG, US, IR, PET, SPECT, and combined modalities.

Prof. Dr. Giuseppe Placidi
Prof. Dr. Mrinal Mandal
Dr. Mustapha Bouhrara
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 papers will be 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 1600 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
  • intelligent strategies
  • sampling
  • reconstruction
  • processing
  • filtering
  • compression
  • registration
  • fusion
  • interpretation
  • machine learning
  • deep learning
  • artificial intelligence

Published Papers (1 paper)

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Research

Article
Super Resolution of Magnetic Resonance Images
J. Imaging 2021, 7(6), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7060101 - 21 Jun 2021
Viewed by 155
Abstract
In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed [...] Read more.
In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed method categorizes the patches as either smooth or textured and then denoises them by deploying different denoising strategies for efficient denoising. The denoising algorithm is integrated into the SR approach, which uses a gradient profile-based constraint in a sparse representation-based framework to improve the resolution of MR images with reduced smearing of image details. This constraint regularizes the estimation of HR images such that the estimated HR image has gradient profiles similar to the gradient profiles of the original HR image. For this, the gradient profile sharpness (GPS) values of an unknown HR image are estimated using an approximated piece-wise linear relation among GPS values of LR and upsampled LR images. The experiments are performed on three different publicly available datasets. The proposed SR approach outperforms the existing unsupervised SR approach addressed for real MR images that exploits low rank and total variation (LRTV) regularization, by an average peak signal to noise ratio (PSNR) of 0.73 dB and 0.38 dB for upsampling factors 2 and 3, respectively. For the super resolution of noisy real MR images (degraded with 2% noise), the proposed approach outperforms the LRTV approach by an average PSNR of 0.54 dB and 0.46 dB for upsampling factors 2 and 3, respectively. The qualitative analysis is shown for real MR images from healthy subjects and subjects with Alzheimer’s disease and structural deformity, i.e., cavernoma. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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