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Electronics
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Medical Image Analysis and Computer Vision
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Medical Image Analysis and Computer Vision

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 5095

Special Issue Editors

Dr. Nantheera Anantrasirichai

E-Mail Website
Guest Editor
Department of Computer Science, University of Bristol, Bristol, UK
Interests: image analysis and enhancement; image and video restoration; image fusion; machine learning; inverse problem; remote sensing; medical imaging; image and video coding
Dr. Oktay Karakuş

E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff, UK
Interests: environmental remote sensing; computational imaging; marine pollution mapping; land cover/land use; multi-modal image fusion; inverse problems; bayesian methods; sports analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical image analysis aims to increase the efficiency of clinical examination and medical intervention. It helps to identify abnormalities and also understand its causes and impact. Computer vision has proven its performance on image classification, segmentation, object detection, 3D rendering, etc. It exploits texture, shape, contour and prior knowledge along with contextual information from image sequencing and provides 3D and 4D information that aids better human understanding. Recent computer vision technologies have been applied to all types and modalities of medical imaging, including computer tomography (CT), magnetic resonance imaging (MRI), ultrasound, X-ray, microscopic imaging, nuclear medicine imaging, etc. These powerful tools have brought much needed quantitative information that provides enhanced medical information that benefits the patients without adding to the already high medical costs.

The aim of this Special Issue of Electronics is to present state-of-the-art methods used to analyse and process medical images through computer vision techniques. We invite researchers to contribute original and unique articles, as well as sophisticated review articles. The topics of interest include but are not limited to:

  • Medical image enhancement
  • Medical image restoration
  • Object detection in medical images
  • Medical image classification
  • 2D/3D medical image segmentation
  • 2D/3D medical image registration
  • Computational medical imaging
  • Machine learning/deep learning for medical imaging
  • Geometric data processing in medical imaging
  • Image fusion

Dr. Nantheera Anantrasirichai
Dr. Oktay Karakuş
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. 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

  • medical imaging
  • computer vision
  • image processing
  • machine learning
  • deep learning
  • object recognition
  • segmentation
  • classification
  • ultrasound
  • microscopy
  • MRI
  • CT

Published Papers (2 papers)

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Research

20 pages, 1682 KiB  
Article
CellNet: A Lightweight Model towards Accurate LOC-Based High-Speed Cell Detection
by Xianlei Long, Idaku Ishii and Qingyi Gu
Electronics 2022, 11(9), 1407; https://doi.org/10.3390/electronics11091407 - 28 Apr 2022
Cited by 1 | Viewed by 1804
Abstract
Label-free cell separation and sorting in a microfluidic system, an essential technique for modern cancer diagnosis, resulted in high-throughput single-cell analysis becoming a reality. However, designing an efficient cell detection model is challenging. Traditional cell detection methods are subject to occlusion boundaries and [...] Read more.
Label-free cell separation and sorting in a microfluidic system, an essential technique for modern cancer diagnosis, resulted in high-throughput single-cell analysis becoming a reality. However, designing an efficient cell detection model is challenging. Traditional cell detection methods are subject to occlusion boundaries and weak textures, resulting in poor performance. Modern detection models based on convolutional neural networks (CNNs) have achieved promising results at the cost of a large number of both parameters and floating point operations (FLOPs). In this work, we present a lightweight, yet powerful cell detection model named CellNet, which includes two efficient modules, CellConv blocks and the h-swish nonlinearity function. CellConv is proposed as an effective feature extractor as a substitute to computationally expensive convolutional layers, whereas the h-swish function is introduced to increase the nonlinearity of the compact model. To boost the prediction and localization ability of the detection model, we re-designed the model’s multi-task loss function. In comparison with other efficient object detection methods, our approach achieved state-of-the-art 98.70% mean average precision (mAP) on our custom sea urchin embryos dataset with only 0.08 M parameters and 0.10 B FLOPs, reducing the size of the model by 39.5× and the computational cost by 4.6×. We deployed CellNet on different platforms to verify its efficiency. The inference speed on a graphics processing unit (GPU) was 500.0 fps compared with 87.7 fps on a CPU. Additionally, CellNet is 769.5-times smaller and 420 fps faster than YOLOv3. Extensive experimental results demonstrate that CellNet can achieve an excellent efficiency/accuracy trade-off on resource-constrained platforms. Full article
(This article belongs to the Special Issue Medical Image Analysis and Computer Vision)
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13 pages, 3186 KiB  
Article
Textural Feature Analysis of Optical Coherence Tomography Phantoms
by Mukhit Kulmaganbetov, Ryan J. Bevan, Nantheera Anantrasirichai, Alin Achim, Irina Erchova, Nick White, Julie Albon and James E. Morgan
Electronics 2022, 11(4), 669; https://doi.org/10.3390/electronics11040669 - 21 Feb 2022
Cited by 8 | Viewed by 2295
Abstract
Optical coherence tomography (OCT) is an imaging technique based on interferometry of backscattered lights from materials and biological samples. For the quantitative evaluation of an OCT system, artificial optical samples or phantoms are commonly used. They mimic the structure of biological tissues and [...] Read more.
Optical coherence tomography (OCT) is an imaging technique based on interferometry of backscattered lights from materials and biological samples. For the quantitative evaluation of an OCT system, artificial optical samples or phantoms are commonly used. They mimic the structure of biological tissues and can provide a quality standard for comparison within and across devices. Phantoms contain medium matrix and scattering particles within the dimension range of target biological structures such as the retina. The aim was to determine if changes in speckle derived optical texture could be employed to classify the OCT phantoms based on their structural composition. Four groups of phantom types were prepared and imaged. These comprise different concentrations of a medium matrix (gelatin solution), different sized polystyrene beads (PBs), the volume of PBs and different refractive indices of scatterers (PBs and SiO2). Texture analysis was applied to detect subtle optical differences in OCT image intensity, surface coarseness and brightness of regions of interest. A semi-automated classifier based on principal component analysis (PCA) and support vector machine (SVM) was applied to discriminate the various texture models. The classifier detected correctly different phantom textures from 82% to 100%, demonstrating that analysis of the texture of OCT images can be potentially used to discriminate biological structure based on subtle changes in light scattering. Full article
(This article belongs to the Special Issue Medical Image Analysis and Computer Vision)
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