Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Symmetry
Special Issues
Advances in Medical Image Segmentation 2019
share announcement

Advances in Medical Image Segmentation 2019

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

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 4917

Special Issue Editor

Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is currently an increasing interest in computer-aided medical image processing to develop more accurate medical diagnosis systems. Medical image segmentation constitutes a fundamental processing task that needs to be applied initially in order to extract specific regions of interest in any modality of medical image. Due to the rapid development of powerful medical imaging devices that are able to provide high resolution images of multiple volumes, the need for efficient (accuracy, speed) segmentation methodologies has emerged. A good segmentation result can be very useful for any medical diagnosis system and the doctors as well, since it can help with the diagnosis of a disease at an early stage and thus the application of more effective treatments. In the light of these needs, this Special Issue will provide a snapshot of the current advances in segmentation of medical images of any modality.

Τhis Special Issue aims to publish high-quality research papers, as well as review articles addressing emerging trends in medical image segmentation. Original contributions, not currently under review for a journal or a conference, are solicited in relevant areas including, but not limited to, the following:

  • Machine learning
  • Deep learning
  • Kernel methods
  • Shape modeling
  • Soft computing methods
  • Knowledge-based segmentation
  • Fuzzy segmentation
  • High performance computing implementations (e.g. GPU, GRID, CLOUD)
  • Review/surveys of segmentation methods
  • New image datasets
  • Different image modalities (e.g. CT, MRI, PET)
  • Evaluation metrics

Prof. Dr. George A. Papakostas
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.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3833 KiB  
Article
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks
by Yun Jiang, Hai Zhang, Ning Tan and Li Chen
Symmetry 2019, 11(9), 1112; https://doi.org/10.3390/sym11091112 - 03 Sep 2019
Cited by 66 | Viewed by 4466
Abstract
Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the [...] Read more.
Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests. Full article
(This article belongs to the Special Issue Advances in Medical Image Segmentation 2019)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop