Symmetry in Medical Image Processing

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3145

Special Issue Editor

Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
Interests: artificial intelligence; machine learning; computer vision; medical image analysis and 3D construction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical Image Analysis has commonly enjoyed leveraging and incorporating techniques from the wider field of Computer Vision. On the one hand, compared to natural images (photography), medical images often present relatively lower variability of anatomy, orientation, and field-of-view; on the other hand, clinical applications necessitate much stricter requirements on accuracy. In many fields, recent neural network-based end-to-end machine-learning approaches have shown great success and had a remarkable impact, especially thanks to the availability of large annotated datasets. Their effects in Medical Image Analysis are also prominent, although the lack of large, curated, annotated datasets, and sometimes prohibitive 3D data sizes, may pose limitations.

In this Special Issue, we aim to cover recent advances and applications in Medical Image Analysis. We are particularly interested in exploring novel applications of machine- and deep-learning approaches, although submissions are open to a wider range of medical image processing topics. Some potential areas of interest include methods for dealing with a low number (lack) of annotations; optimal/efficient approaches to procure annotations; scalable methods for multi-organ, multi-tissue analysis applications; approaches to deal with non-normalized sequences/imaging data; and techniques to gather population information.

We welcome submissions on topics including, but not limited to, the following:

  • Novel applications of deep or machine learning;
  • Applications in medical imaging, acquisition, reconstruction, denoising, super-resolution, segmentation, registration, tracking, and others;
  • Applications in different medical image modalities, including MR, X-ray, PET, and US imaging (but excluding biological or microscopy imaging). 

Prof. Dr. Jie Yang
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. Symmetry 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 2400 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 analysis
  • theoretical and experimental advances
  • imaging modalities

Published Papers (1 paper)

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Review

26 pages, 1961 KiB  
Review
Radiomics and Its Feature Selection: A Review
by Wenchao Zhang, Yu Guo and Qiyu Jin
Symmetry 2023, 15(10), 1834; https://doi.org/10.3390/sym15101834 - 27 Sep 2023
Cited by 3 | Viewed by 2666
Abstract
Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics [...] Read more.
Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics with artificial intelligence presents innovative avenues for cancer diagnosis, prognosis evaluation, and therapeutic choices. In the context of oncology, radiomics offers significant potential. Feature selection emerges as a pivotal step, enhancing the clinical utility and precision of radiomics. It achieves this by purging superfluous and unrelated features, thereby augmenting model performance and generalizability. The goal of this review is to assess the fundamental radiomics process and the progress of feature selection methods, explore their applications and challenges in cancer research, and provide theoretical and methodological support for future investigations. Through an extensive literature survey, articles pertinent to radiomics and feature selection were garnered, synthesized, and appraised. The paper provides detailed descriptions of how radiomics is applied and challenged in different cancer types and their various stages. The review also offers comparative insights into various feature selection strategies, including filtering, packing, and embedding methodologies. Conclusively, the paper broaches the limitations and prospective trajectories of radiomics. Full article
(This article belongs to the Special Issue Symmetry in Medical Image Processing)
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