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Symmetry
Special Issues
Medical Image Computing: Theory, System and Applications
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Medical Image Computing: Theory, System and Applications

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4672

Special Issue Editors

Prof. Dr. Qinghua Huang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: medical ultrasound imaging; medical robotics; pattern recognition; data mining; computer-aided diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Haigang Ma

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: multimodal imaging system; medical imaging and information processing; intelligent detection technology

Special Issue Information

Dear Colleagues,

Symmetry plays an important role in ‘’Medical Image Computing: Theory, System and Applications’’ as it may represent the crucial properties of medical images as well as properties of solutions of corresponding image resolution and contrast problems. For example, symmetry detection is an important image feature detection widely used in computer vision, image processing, and other fields and has broad application prospects .

The presented Special Issue is devoted to recent advances in ‘’Medical Image Computing: Theory, System and Applications’’ related to symmetries analysis. Among the topics of the Issue are the following: 1) image-based symmetry recognition; 2) medical image segmentation and visualization based on symmetry and graph cuts; 3) measurement and analyses of symmetry characteristic based on medical images; 4) symmetry-based medical signal analysis and processing; 5) diagnostic methods of medical imaging based on symmetric information; 6) advanced imaging in biomedicine ; 7) any topics using the concept of symmetry in ‘’Medical Image Computing: Theory, System and Applications’’.

Prof. Dr. Qinghua Huang
Dr. Haigang Ma
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. 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
  • medical image
  • medical imaging
  • image analyses
  • image segmentation
  • signal processing

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Published Papers (2 papers)

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Research

16 pages, 5906 KiB  
Article
Multifeature Detection of Microaneurysms Based on Improved SSA
by Liwei Deng, Xiaofei Wang and Jiazhong Xu
Symmetry 2021, 13(11), 2147; https://doi.org/10.3390/sym13112147 - 10 Nov 2021
Viewed by 1571
Abstract
The early diagnosis of retinopathy is crucial to the prevention and treatment of diabetic retinopathy. The low proportion of positive cases in the asymmetric microaneurysm detection problem causes preprocessing to treat microaneurysms as noise to be eliminated. To obtain a binary image containing [...] Read more.
The early diagnosis of retinopathy is crucial to the prevention and treatment of diabetic retinopathy. The low proportion of positive cases in the asymmetric microaneurysm detection problem causes preprocessing to treat microaneurysms as noise to be eliminated. To obtain a binary image containing microaneurysms, the object was segmented by a symmetry algorithm, which is a combination of the connected components and SSA methods. Next, a candidate microaneurysm set was extracted by multifeature clustering of binary images. Finally, the candidate microaneurysms were mapped to the Radon frequency domain to achieve microaneurysm detection. In order to verify the feasibility of the algorithm, a comparative experiment was conducted on the combination of the connected components and SSA methods. In addition, PSNR, FSIM, SSIM, fitness value, average CPU time and other indicators were used as evaluation standards. The results showed that the overall performance of the binary image obtained by the algorithm was the best. Last but not least, the accuracy of the detection method for microaneurysms in this paper reached up to 93.24%, which was better than that of several classic microaneurysm detection methods in the same period. Full article
(This article belongs to the Special Issue Medical Image Computing: Theory, System and Applications)
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12 pages, 1535 KiB  
Article
HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
by Xin Wei, Huan Wan, Fanghua Ye and Weidong Min
Symmetry 2021, 13(11), 2107; https://doi.org/10.3390/sym13112107 - 6 Nov 2021
Cited by 2 | Viewed by 2365
Abstract
In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty [...] Read more.
In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR. Full article
(This article belongs to the Special Issue Medical Image Computing: Theory, System and Applications)
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