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Electronics
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Deep Learning in Image Processing and Segmentation
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Deep Learning in Image Processing and Segmentation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 978

Special Issue Editor

Dr. Prashan Premaratne

E-Mail Website
Guest Editor
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: electrical engineering; image processing; computer vision; pattern recognition; Artificial Intelligenc

Special Issue Information

Dear Colleagues,

Image processing has been traditionally attempted using number of approaches that have been vital for the growth of computer vision. The applications that have emerged from such traditional approaches have changed the way we live, from vehicle number plate recognition to medical imaging, and without the growth of image processing, our modern lives will cease to exist. The traditional image processing tasks involve:

  • Image enhancement;
  • Image restoration;
  • Wavelets and multi-resolution processing;
  • Image compression;
  • Morphological processing;
  • Representation and description;
  • Object detection and recognition;
  • Knowledge base.

Image segmentation is also one of these image processing tasks; however, this journal Special Issue will treat these two sections separately as it wants to discover the new trends in these two traditional fields using deep learning or artificial intelligence-based approaches. This volume calls for recent research progress using deep learning in realizing the above image processing tasks or approaches that would result in image segmentation similar to traditional watershed algorithms, region-growing algorithms or any modern approaches.

Dr. Prashan Premaratne
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. Electronics is an international peer-reviewed open access semimonthly 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

  • image enhancement
  • image restoration
  • wavelets and multi-resolution processing
  • image compression
  • morphological processing
  • representation and description
  • object detection and recognition
  • knowledge base

Published Papers (1 paper)

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Research

19 pages, 11581 KiB  
Article
CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion
by Yanan Gu, Ruyi Cao, Dong Wang and Bibo Lu
Electronics 2023, 12(23), 4743; https://doi.org/10.3390/electronics12234743 - 22 Nov 2023
Viewed by 732
Abstract
Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–decoder framework, [...] Read more.
Retinal vessel segmentation plays a critical role in the diagnosis and treatment of various ophthalmic diseases. However, due to poor image contrast, intricate vascular structures, and limited datasets, retinal vessel segmentation remains a long-term challenge. In this paper, based on an encoder–decoder framework, a novel retinal vessel segmentation model called CMP-UNet is proposed. Firstly, the Coarse and Fine Feature Aggregation module decouples and aggregates coarse and fine vessel features using two parallel branches, thus enhancing the model’s ability to extract features for vessels of various sizes. Then, the Multi-Scale Channel Adaptive Fusion module is embedded in the decoder to realize the efficient fusion of cascade features by mining the multi-scale context information from these features. Finally, to obtain more discriminative vascular features and enhance the connectivity of vascular structures, the Pyramid Feature Fusion module is proposed to effectively utilize the complementary information of multi-level features. To validate the effectiveness of the proposed model, it is evaluated on three publicly available retinal vessel segmentation datasets: CHASE_DB1, DRIVE, and STARE. The proposed model, CMP-UNet, reaches F1-scores of 82.84%, 82.55%, and 84.14% on these three datasets, with improvements of 0.76%, 0.31%, and 1.49%, respectively, compared with the baseline. The results show that the proposed model achieves higher segmentation accuracy and more robust generalization capability than state-of-the-art methods. Full article
(This article belongs to the Special Issue Deep Learning in Image Processing and Segmentation)
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