Computer Vision for Biomedical Image Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5582

Special Issue Editor


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Guest Editor
1. Zhejiang Institution of Standardization, Hangzhou 310006, China
2. Department of Computer Science and Technology, Fudan University, Shanghai 200433, China
Interests: digitalisation; standardisation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development and progress of biomedical imaging technology, biomedical image processing has become more and more widely used in biomedical research and clinical medicine. Additionally, computer vision has recently revolutionized the methods used for biomedical image processing due to automated feature discovery and the superior results that can be obtained. The aim of this Special Issue is to collect both review articles and original research papers describing novel methods and applications of biological and medical image processing, with special emphasis on efforts related to the applications of computer vision problems. Typical biomedical image datasets include and are not limited to magnetic resonance, ultrasound, computed tomography, X-ray, optical and confocal microscopy, and video and range data images.

The topics of interest for this Special Issue include but are not limited to:

  • Representation of pictorial data
  • Visualization
  • Feature extraction
  • Segmentation
  • Detection
  • 3D reconstruction
  • Image-guided surgery and intervention
  • Shape and motion measurements
  • Digital anatomical atlases
  • Virtual and augmented reality for therapy planning and guidance
  • Telemedicine with medical images

Dr. Guyue Zhang
Guest Editor

Manuscript Submission Information

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Keywords

  • computer vision
  • artificial intelligence
  • deep learning
  • machine learning

Published Papers (3 papers)

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Research

18 pages, 2928 KiB  
Article
GSCEU-Net: An End-to-End Lightweight Skin Lesion Segmentation Model with Feature Fusion Based on U-Net Enhancements
by Shengnan Hao, Haotian Wu, Yanyan Jiang, Zhanlin Ji, Li Zhao, Linyun Liu and Ivan Ganchev
Information 2023, 14(9), 486; https://doi.org/10.3390/info14090486 - 01 Sep 2023
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Abstract
Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized complex modules to improve their performance for skin [...] Read more.
Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized complex modules to improve their performance for skin disease image segmentation. However, limited computational resources have hindered their practical application in clinical environments. To address this challenge, this paper proposes a lightweight model, named GSCEU-Net, which is able to achieve superior skin lesion segmentation performance at a lower cost. GSCEU-Net is based on the U-Net architecture with additional enhancements. Firstly, the partial convolution (PConv) module, proposed by the FasterNet model, is modified to an SConv module, which enables channel segmentation paths of different scales. Secondly, a newly designed Ghost SConv (GSC) module is proposed for incorporation into the model’s backbone, where the Separate Convolution (SConv) module is aided by a Multi-Layer Perceptron (MLP) and the output path residuals from the Ghost module. Finally, the Efficient Channel Attention (ECA) mechanism is incorporated at different levels into the decoding part of the model. The segmentation performance of the proposed model is evaluated on two public datasets (ISIC2018 and PH2) and a private dataset. Compared to U-Net, the proposed model achieves an IoU improvement of 0.0261 points and a DSC improvement of 0.0164 points, while reducing the parameter count by 190 times and the computational complexity by 170 times. Compared to other existing segmentation models, the proposed GSCEU-Net model also demonstrates superiority, along with an advanced balance between the number of parameters, complexity, and segmentation performance. Full article
(This article belongs to the Special Issue Computer Vision for Biomedical Image Processing)
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19 pages, 3187 KiB  
Article
U-Net_dc: A Novel U-Net-Based Model for Endometrial Cancer Cell Image Segmentation
by Zhanlin Ji, Dashuang Yao, Rui Chen, Tao Lyu, Qinping Liao, Li Zhao and Ivan Ganchev
Information 2023, 14(7), 366; https://doi.org/10.3390/info14070366 - 28 Jun 2023
Viewed by 1132
Abstract
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions, and improving the efficiency [...] Read more.
Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions, and improving the efficiency of treatment and the useful effect of drugs. However, traditional image segmentation models are not ideal solutions for cancer cell image segmentation due to the fact that cancer cells are highly dense and vary in shape and size. To tackle this problem, this paper proposes a novel U-Net-based image segmentation model, named U-Net_dc, which expands twice the original U-Net encoder and decoder and, in addition, uses a skip connection operation between them, for better extraction of the image features. In addition, the feature maps of the last few U-Net layers are upsampled to the same size and then concatenated together for producing the final output, which allows the final feature map to retain many deep-level features. Moreover, dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) modules are introduced between the encoder and decoder, which helps the model obtain receptive fields of different sizes, better extract rich feature expression, detect objects of different sizes, and better obtain context information. According to the results obtained from experiments conducted on the Tsinghua University’s private dataset of endometrial cancer cells and the publicly available Data Science Bowl 2018 (DSB2018) dataset, the proposed U-Net_dc model outperforms all state-of-the-art models included in the performance comparison study, based on all evaluation metrics used. Full article
(This article belongs to the Special Issue Computer Vision for Biomedical Image Processing)
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15 pages, 360656 KiB  
Article
Efficient SCAN and Chaotic Map Encryption System for Securing E-Healthcare Images
by Kiran, H. L. Gururaj, Meshari Almeshari, Yasser Alzamil, Vinayakumar Ravi and K. V. Sudeesh
Information 2023, 14(1), 47; https://doi.org/10.3390/info14010047 - 12 Jan 2023
Cited by 3 | Viewed by 1837
Abstract
The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed [...] Read more.
The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed by worn-out radiologists. Transmission of these medical data increases in frequency due to patient overflow, and protecting confidentiality, along with integrity and availability, emerges as one of the most crucial components of security. Medical images generally contain sensitive information about patients and are therefore vulnerable to various security threats during transmission over public networks. These images must be protected before being transmitted over this network to the public. In this paper, an efficient SCAN and chaotic-map-based image encryption model is proposed. This paper describes pixel value and pixel position manipulation based on SCAN and chaotic theory. The SCAN method involves translating an image’s pixel value to a different pixel value and rearranging pixels in a predetermined order. A chaotic map is used to shift the positions of the pixels within the block. Decryption follows the reverse process of encryption. The effectiveness of the suggested strategy is evaluated by computing the histogram chi-square test, MSE, PSNR, NPCR, UACI, SSIM, and UQI. The efficiency of the suggested strategy is demonstrated by comparison analysis. The results of analysis and testing show that the proposed program can achieve the concept of partial encryption. In addition, simulation experiments demonstrate that our approach has both a faster encryption speed and higher security when compared to existing techniques. Full article
(This article belongs to the Special Issue Computer Vision for Biomedical Image Processing)
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