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Electronics
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Biomedical Image Processing and Classification, 2nd Edition
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Biomedical Image Processing and Classification, 2nd Edition

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 6928

Special Issue Editor

Dr. Luca Mesin

E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Polytechnic University of Turin, Turin, Italy
Interests: biomedical signal and image processing and classification; biophysical modelling; clinical studies; mathematical biology and physiology; noninvasive monitoring of the volemic status of patients; nonlinear biomedical signal processing; optimal non-uniform down-sampling; systems for human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical image processing is an interdisciplinary field that spans several disciplines, including electrical engineering, computer science, physics, mathematics, physiology, and medicine. Various imaging techniques have been developed, providing many approaches to study the body, including X-rays for computed tomography, magnetic resonance imaging, ultrasound, positron emission tomography, elastography, single-photon emission computed tomography, functional near-infrared spectroscopy, endoscopy, thermography, and photoacoustic imaging. Bioelectric sensors, when used in high-density systems (for example, in electroencephalography or electromyography), can also provide maps that can be studied using image processing methods. Biomedical image processing has an increasing number of important applications, for example, in studying the internal structure or function of an organ and in diagnosing or treating disease. When combined with classification methods, it can support the development of computer-aided diagnostic (CAD) systems, for example, for the identification of diseased tissue or a specific lesion or malformation. Recent developments in deep learning approaches have also allowed information to be directly extracted (e.g., via segmentation or classification) from medical images.

The aim of this Special Issue is to collect high-quality contributions that document a wide range of image processing applications to solve biomedical problems. The topics of interest include (but are not limited to) image enhancement, registration, segmentation, restoration, compression, and movement tracking, with the aim of identifying the tissue properties or pathology of a patient. 

Dr. Luca Mesin
Guest Editor

Manuscript Submission Information

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Keywords

  • image registration
  • image segmentation
  • motion tracking
  • computer-added diagnosis
  • deep learning
  • machine learning and classification
  • patient-specific diagnosis

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Related Special Issue

Published Papers (4 papers)

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Research

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17 pages, 6059 KiB  
Article
ECF-Net: Enhanced, Channel-Based, Multi-Scale Feature Fusion Network for COVID-19 Image Segmentation
by Zhengjie Ji, Junhao Zhou, Linjing Wei, Shudi Bao, Meng Chen, Hongxing Yuan and Jianjun Zheng
Electronics 2024, 13(17), 3501; https://doi.org/10.3390/electronics13173501 - 3 Sep 2024
Viewed by 1183
Abstract
Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients’ conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack attention to local [...] Read more.
Accurate segmentation of COVID-19 lesion regions in lung CT images aids physicians in analyzing and diagnosing patients’ conditions. However, the varying morphology and blurred contours of these regions make this task complex and challenging. Existing methods utilizing Transformer architecture lack attention to local features, leading to the loss of detailed information in tiny lesion regions. To address these issues, we propose a multi-scale feature fusion network, ECF-Net, based on channel enhancement. Specifically, we leverage the learning capabilities of both CNN and Transformer architectures to design parallel channel extraction blocks in three different ways, effectively capturing diverse lesion features. Additionally, to minimize irrelevant information in the high-dimensional feature space and focus the network on useful and critical information, we develop adaptive feature generation blocks. Lastly, a bidirectional pyramid-structured feature fusion approach is introduced to integrate features at different levels, enhancing the diversity of feature representations and improving segmentation accuracy for lesions of various scales. The proposed method is tested on four COVID-19 datasets, demonstrating mIoU values of 84.36%, 87.15%, 83.73%, and 75.58%, respectively, outperforming several current state-of-the-art methods and exhibiting excellent segmentation performance. These findings provide robust technical support for medical image segmentation in clinical practice. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification, 2nd Edition)
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17 pages, 4954 KiB  
Article
Medical Image Classification with a Hybrid SSM Model Based on CNN and Transformer
by Can Hu, Ning Cao, Han Zhou and Bin Guo
Electronics 2024, 13(15), 3094; https://doi.org/10.3390/electronics13153094 - 5 Aug 2024
Cited by 3 | Viewed by 3036
Abstract
Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its [...] Read more.
Medical image classification, a pivotal task for diagnostic accuracy, poses unique challenges due to the intricate and variable nature of medical images compared to their natural counterparts. While Convolutional Neural Networks (CNNs) and Transformers are prevalent in this domain, each architecture has its drawbacks. CNNs, despite their strength in local feature extraction, fall short in capturing global context, whereas Transformers excel at global information but can overlook fine-grained details. The integration of CNNs and Transformers in a hybrid model aims to bridge this gap by enabling simultaneous local and global feature extraction. However, this approach remains constrained in its capacity to model long-range dependencies, thereby hindering the efficient extraction of distant features. To address these issues, we introduce the MambaConvT model, which employs a state-space approach. It begins by locally processing input features through multi-core convolution, enhancing the extraction of deep, discriminative local details. Next, depth-separable convolution with a 2D selective scanning module (SS2D) is employed to maintain a global receptive field and establish long-distance connections, capturing the fine-grained features. The model then combines hybrid features for comprehensive feature extraction, followed by global feature modeling to emphasize on global detail information and optimize feature representation. This paper conducts thorough performance experiments on different algorithms across four publicly available datasets and two private datasets. The results demonstrate that MambaConvT outperforms the latest classification algorithms in terms of accuracy, precision, recall, F1 score, and AUC value ratings, achieving superior performance in the precise classification of medical images. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification, 2nd Edition)
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20 pages, 6119 KiB  
Article
Few-Shot Classification Based on the Edge-Weight Single-Step Memory-Constraint Network
by Jing Shi, Hong Zhu, Yuandong Bi, Zhong Wu, Yuanyuan Liu and Sen Du
Electronics 2023, 12(24), 4956; https://doi.org/10.3390/electronics12244956 - 10 Dec 2023
Viewed by 1262
Abstract
Few-shot classification algorithms have gradually emerged in recent years, and many breakthroughs have been made in the research of migration networks, metric spaces, and data enhancement. However, the few-shot classification algorithm based on Graph Neural Network is still being explored. In this paper, [...] Read more.
Few-shot classification algorithms have gradually emerged in recent years, and many breakthroughs have been made in the research of migration networks, metric spaces, and data enhancement. However, the few-shot classification algorithm based on Graph Neural Network is still being explored. In this paper, an edge-weight single-step memory-constraint network is proposed based on mining hidden features and optimizing the attention mechanism. According to the hidden distribution characteristics of edge-weight data, a new graph structure is designed, where node features are fused and updated to realize feature enrichment and full utilization of limited sample data. In addition, based on the convolution block attention mechanism, different integration methods of channel attention and spatial attention are proposed to help the model extract more meaningful features from samples through feature attention. The ablation experiments and comparative analysis of each training mode are carried out on standard datasets. The experimental results obtained prove the rationality and innovation of the proposed method. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification, 2nd Edition)
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Other

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12 pages, 7399 KiB  
Technical Note
Estimation of Inferior Vena Cava Size from Ultrasound Imaging in X-Plane
by Piero Policastro and Luca Mesin
Electronics 2024, 13(17), 3406; https://doi.org/10.3390/electronics13173406 - 27 Aug 2024
Cited by 1 | Viewed by 771
Abstract
Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood [...] Read more.
Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three-dimensional geometry. Using bi-dimensional (2D) B-mode views either in a long or short axis has improved the characterization of IVC dynamics compared to measurements along a single direction (M-mode). However, specific movements of the IVC can also challenge the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in the X-plane, are integrated here using an innovative method. It is tested on simulated videos of the IVC by performing complicated movements, which are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Classification, 2nd Edition)
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