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 386

Special Issue Editors


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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

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

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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 (1 paper)

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Research

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 94
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
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