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Symmetry
Special Issues
Asymmetric and Symmetric Studies on Medical Imaging
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Asymmetric and Symmetric Studies on Medical Imaging

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

Deadline for manuscript submissions: closed (16 January 2023) | Viewed by 3506

Special Issue Editor

Prof. Dr. Yan Wang

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: computer vision;medical imaging;machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Symmetry is ubiquitous throughout the human body, with notable examples including tubular structures whose symmetry axis is of vital importance for tracking and segmentation, and two hemispheres in the brain whose anatomical structures serve as signals for the characterization of diseases. Asymmetric and symmetric learning can serve as important prior knowledge and assist in more comprehensive studies in medical images. This Special Issue aims to study 1) how to best utilize the asymmetric and symmetric prior knowledge to handle medical imaging data, 2) how to perform asymmetric or symmetric designed machine learning algorithms, and 3) how to improve the interpretability of medical imaging algorithms with regard to asymmetric and symmetric studies.

Prof. Dr. Yan Wang
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

  • symmetry axis
  • low/high-level asymmetric and symmetric features
  • asymmetric and symmetric design of machine learning algorithms
  • novel applications of asymmetric and symmetric study
  • asymmetric and symmetric architectures/loss functions in self/semi/weakly-supervised learning
  • framework
  • new datasets for the asymmetric and symmetric study

Published Papers (2 papers)

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Research

11 pages, 1709 KiB  
Article
Effect of Tube Voltage and Radiation Dose on Image Quality in Pediatric Abdominal CT Using Deep Learning Reconstruction: A Phantom Study
by Daehong Kim, Pil-Hyun Jeon, Chang-Lae Lee and Myung-Ae Chung
Symmetry 2023, 15(2), 501; https://doi.org/10.3390/sym15020501 - 14 Feb 2023
Viewed by 1675
Abstract
Background: Children have a potential risk from radiation exposure because they are more sensitive to radiation than adults. Objective: The purpose of this work is to estimate image quality according to tube voltage (kV) and radiation dose in pediatric computed tomography [...] Read more.
Background: Children have a potential risk from radiation exposure because they are more sensitive to radiation than adults. Objective: The purpose of this work is to estimate image quality according to tube voltage (kV) and radiation dose in pediatric computed tomography (CT) using deep learning reconstruction (DLR). Methods: Phantom images of children and adults were obtained for kV, radiation dose, and image reconstruction methods. The CT emits a fan beam to the opposite detector, and the geometry of the detector was symmetrical. Phantom images of children and adults were acquired at a volume CT dose index (CTDIvol) from 0.5 to 10.0 mGy for tube voltages at 80, 100, and 120 kV. A DLR was used to reconstruct the phantom image, and filtered back projection (FBP) and iterative reconstruction (IR) were also performed for comparison with the DLR. Image quality was evaluated by measuring the contrast-to-noise ratio (CNR) and noise. Results: Under the same imaging conditions, the DLR images of pediatric and adult phantoms generally provided improved CNR and noise compared with the FBP and IR images. At a similar CNR and noise, the FBP, IR, and DLR of the pediatric images showed a dose reduction compared with the FBP, IR, and DLR of the adult images, respectively. In terms of the effect of tube voltage, the CNR of the 100 kV DLR images was higher than that of the 120 kV DLR images. Conclusion: According to the results, since pediatric CT images maintain the same image quality at lower doses compared with adult CT images, DLR can improve image quality while reducing the radiation dose in children’s abdominal CT scans. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Studies on Medical Imaging)
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24 pages, 5298 KiB  
Article
Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging
by Jawad Rasheed
Symmetry 2022, 14(7), 1398; https://doi.org/10.3390/sym14071398 - 07 Jul 2022
Cited by 22 | Viewed by 2533
Abstract
The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, [...] Read more.
The massive adaptation of reverse transcriptase-polymerase chain reaction (RT-PCR) has facilitated efforts to battle against the COVID-19 pandemic that has inflicted millions of individuals around the world. Besides RT-PCR, radiography imaging examinations yields valuable insight for detecting and diagnosing this infectious disease. Thus, this paper proposed a computer vision and artificial-intelligence-based hybrid approach aid in efficient detection and control of COVID-19 disease. The study utilized chest X-ray images to segregate COVID-19 positive cases among healthy individuals by exploiting several combinational structures of image filtering, feature-extraction techniques, and machine learning algorithms. It analyzed the effects of three noise removal filters and two feature-extraction techniques on performance of several machine learning and deep-learning-based classifiers. The proposed schemes first remove unnecessary noise using a conservative smoothing filter, Crimmins speckle removal, and Gaussian filter. It then employs linear discriminant analysis (LDA) as linear method and principal component analysis (PCA) as non-linear feature-extraction technique to extract highly discriminant feature sets. Finally, it uses these feature sets to train various classification models, including convolutional neural network (CNN), support vector machine (SVM), and logistic regression (LG). Evidently, the proposed conservative smoothing filter with single peak to maintain symmetry in horizontal and vertical directions for enhancement of image, along with LDA and SVM, secured an overall classification accuracy of 99.93%. Experimental results show that, besides achieving high accuracies, the incorporation of feature-extraction techniques significantly reduces the computational time of the proposed model. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Studies on Medical Imaging)
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