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J. Imaging
Special Issues
Image Segmentation Techniques: Current Status and Future Directions (2nd Edition)
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Image Segmentation Techniques: Current Status and Future Directions (2nd Edition)

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Image and Video Processing".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1248

Special Issue Editors

Dr. Xiaohao Cai

E-Mail Website
Guest Editor
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: computer vision; image processing; machine/deep learning; scientific computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gaohang Yu

E-Mail Website
Guest Editor
Department of Mathematics, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: image processing; optimization; tensor analysis; computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image segmentation, as a fundamental and challenging task in many subjects such as image processing and computer vision, is of great importance but is constantly challenging to deliver. Briefly speaking, it is the process of assigning a label to every pixel in an image according to certain characteristics such as intensity, biometrics and semantics. It is generally a prerequisite and plays a key role in its ubiquitous practical applications, such as machine vision, medical imaging, detection, recognition, and autonomous driving. Researchers are increasing their efforts to develop new segmentation techniques based on, e.g., mathematical/statistical models, biometrics, and machine learning via deep neural networks to tackle existing and upcoming challenges.

This Special Issue aims to gather innovative research on image segmentation techniques, ranging from the current status to future directions, and from hand-crafted techniques to deep learning, etc. We also welcome submissions including, but not limited to, applications in digital imaging, medical imaging, object detection, and recognition tasks, among other related topics.

Dr. Xiaohao Cai
Prof. Dr. Gaohang Yu
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. Journal of Imaging 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 1800 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 segmentation
  • image processing
  • classification
  • recognition
  • variational regularization algorithms
  • neural networks
  • machine learning
  • deep learning
  • digital imaging
  • medical imaging

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Published Papers (2 papers)

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Research

13 pages, 6518 KiB  
Article
Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images
by Parvaneh Aliniya, Mircea Nicolescu, Monica Nicolescu and George Bebis
J. Imaging 2024, 10(12), 331; https://doi.org/10.3390/jimaging10120331 (registering DOI) - 22 Dec 2024
Viewed by 97
Abstract
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the [...] Read more.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the development of supervised methods and the utilization of deep learning. Training deep learning-based models using segmentation masks will also be a powerful tool for removing pectoral muscle for unseen data. To test the validity of this idea, we trained AU-Net separately on the INbreast and CBIS-DDSM for the segmentation of the pectoral muscle. We used cross-dataset testing to evaluate the performance of the models on an unseen dataset. In addition, the models were tested on all of the images in the MIAS dataset. The experimental results show that cross-dataset testing achieves a comparable performance to the same-dataset experiments. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions (2nd Edition))
16 pages, 5582 KiB  
Article
Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities
by Ioannis Stathopoulos, Luigi Serio, Efstratios Karavasilis, Maria Anthi Kouri, Georgios Velonakis, Nikolaos Kelekis and Efstathios Efstathopoulos
J. Imaging 2024, 10(12), 296; https://doi.org/10.3390/jimaging10120296 - 21 Nov 2024
Viewed by 1020
Abstract
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of [...] Read more.
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection. In this study, we evaluate the diagnostic performance of six fundamental MRI sequences in detecting tumor-involved brain slices using four distinct CNN architectures enhanced with transfer learning techniques. Our dataset comprises 1646 MRI slices from the examinations of 62 patients, encompassing both tumor-bearing and normal findings. With our approach, we achieved a classification accuracy of 98.6%, underscoring the high potential of CNN-based models in this context. Additionally, we assessed the performance of each MRI sequence across the different CNN models, identifying optimal combinations of MRI modalities and neural networks to meet radiologists’ screening requirements effectively. This study offers critical insights into the integration of deep learning with MRI for brain tumor detection, with implications for improving diagnostic workflows in clinical settings. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions (2nd Edition))
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