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Electronics
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Recent Advances in Biomedical Image Processing and Analysis
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Recent Advances in Biomedical Image Processing and Analysis

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 12104

Special Issue Editors

Prof. Dr. Jyh-Cheng Chen

E-Mail Website
Guest Editor
Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao-Tung University, Taipei 112, Taiwan
Interests: tumor detection in nuclear medicine; hybrid imaging system development (PET/CT, FT/CT); image quality assessment and improvement; image processing and image quality analysis
Prof. Dr. David Dagan Feng

E-Mail Website
Guest Editor
Biomedical & Multimedia Information Technology Research Group, School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: biomedical & multimedia information technology; functional imaging; biomedical systems and multimedia computing

Special Issue Information

Dear Colleagues,

This Special Issue will provide a forum for publishing original research papers covering state-of-the-art and novel algorithms, methodologies, and applications in biomedical imaging. The Special Issue will also highlight the hot topics of clinical and preclinical PET, SPECT, CT, MR, US, OI, and discuss the latest developments in the field of molecular imaging (MI) and radiomics in addition to the rise of artificial intelligence (AI) techniques for biomedical image processing and analysis, ranging from classic ML (machine learning) to DL (deep learning).

Prof. Dr. Jyh-Cheng Chen
Prof. Dr. David Dagan Feng
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

  • PET
  • SPECT
  • CT
  • MR
  • US
  • OI
  • AI
  • DL
  • ML
  • MI
  • radiomics
  • biomedical image processing and analysis

Published Papers (4 papers)

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Research

33 pages, 11901 KiB  
Article
COVID-19-Associated Lung Lesion Detection by Annotating Medical Image with Semi Self-Supervised Technique
by Vinh Pham, Dung Dinh, Eunil Seo and Tai-Myoung Chung
Electronics 2022, 11(18), 2893; https://doi.org/10.3390/electronics11182893 - 13 Sep 2022
Cited by 1 | Viewed by 1543
Abstract
Diagnosing COVID-19 infection through the classification of chest images using machine learning techniques faces many controversial problems owing to the intrinsic nature of medical image data and classification architectures. The detection of lesions caused by COVID-19 in the human lung with properties such [...] Read more.
Diagnosing COVID-19 infection through the classification of chest images using machine learning techniques faces many controversial problems owing to the intrinsic nature of medical image data and classification architectures. The detection of lesions caused by COVID-19 in the human lung with properties such as location, size, and distribution is more practical and meaningful to medical workers for severity assessment, progress monitoring, and treatment, thus improving patients’ recovery. We proposed a COVID-19-associated lung lesion detector based on an object detection architecture. It correctly learns disease-relevant features by focusing on lung lesion annotation data of medical images. An annotated COVID-19 image dataset is currently nonexistent. We designed our semi-self-supervised method, which can extract knowledge from available annotated pneumonia image data and guide a novice in annotating lesions on COVID-19 images in the absence of a medical specialist. We prepared a sufficient dataset with nearly 8000 lung lesion annotations to train our deep learning model. We comprehensively evaluated our model on a test dataset with nearly 1500 annotations. The results demonstrated that the COVID-19 images annotated by our method significantly enhanced the model’s accuracy by as much as 1.68 times, and our model competes with commercialized solutions. Finally, all experimental data from multiple sources with different annotation data formats are standardized into a unified COCO format and publicly available to the research community to accelerate research on the detection of COVID-19 using deep learning. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing and Analysis)
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10 pages, 4297 KiB  
Article
Using Breast Tissue Information and Subject-Specific Finite-Element Models to Optimize Breast Compression Parameters for Digital Mammography
by Tien-Yu Chang, Jay Wu, Pei-Yuan Liu, Yan-Lin Liu, Dmytro Luzhbin and Hsien-Chou Lin
Electronics 2022, 11(11), 1784; https://doi.org/10.3390/electronics11111784 - 04 Jun 2022
Cited by 2 | Viewed by 1770
Abstract
Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for [...] Read more.
Digital mammography has become a first-line diagnostic tool for clinical breast cancer screening due to its high sensitivity and specificity. Mammographic compression force is closely associated with image quality and patient comfort. Therefore, optimizing breast compression parameters is essential. Subjects were recruited for digital mammography and breast magnetic resonance imaging (MRI) within a month. Breast MRI images were used to calculate breast volume and volumetric breast density (VBD) and construct finite element models. Finite element analysis was performed to simulate breast compression. Simulated compressed breast thickness (CBT) was compared with clinical CBT and the relationships between compression force, CBT, breast volume, and VBD were established. Simulated CBT had a good linear correlation with the clinical CBT (R2 = 0.9433) at the clinical compression force. At 10, 12, 14, and 16 daN, the mean simulated CBT of the breast models was 5.67, 5.13, 4.66, and 4.26 cm, respectively. Simulated CBT was positively correlated with breast volume (r > 0.868) and negatively correlated with VBD (r < –0.338). The results of this study provides a subject-specific and evidence-based suggestion of mammographic compression force for radiographers considering image quality and patient comfort. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing and Analysis)
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13 pages, 2262 KiB  
Article
Optimization of Spatial Resolution and Image Reconstruction Parameters for the Small-Animal Metis™ PET/CT System
by Jie Zhao, Qiong Liu, Chaofan Li, Yunfeng Song, Ying Zhang and Jyh-Cheng Chen
Electronics 2022, 11(10), 1542; https://doi.org/10.3390/electronics11101542 - 11 May 2022
Cited by 1 | Viewed by 1384
Abstract
Purpose: The aim of this study was to investigate the optimization of the spatial resolution and image reconstruction parameters related to image quality in an iterative reconstruction algorithm for the small-animal Metis™ PET/CT system. Methods: We used a homemade Derenzo phantom to evaluate [...] Read more.
Purpose: The aim of this study was to investigate the optimization of the spatial resolution and image reconstruction parameters related to image quality in an iterative reconstruction algorithm for the small-animal Metis™ PET/CT system. Methods: We used a homemade Derenzo phantom to evaluate the image quality using visual assessment, the signal-to-noise ratio, the contrast, the coefficient of variation, and the contrast-to-noise ratio of the 0.8 mm hot rods of eight slices in the center of the phantom PET images. A healthy mouse study was performed to analyze the influence of the optimal reconstruction parameters and the Gaussian post-filter FWHM. Results: In the phantom study, the image quality was the best when the phantom was placed at the end, keeping the central axis parallel to the X-axis of the system, and selecting between 30 and 40 iterations, a 0.314 mm reconstructed voxel size, and a 1.57 mm Gaussian post-filter FWHM. The optimization of the spatial resolution could reach 0.6 mm. In the animal study, it was suitable to choose a voxel size of 0.472 mm, between 30 and 40 iterations, and a 2.36 mm Gaussian post-filter FWHM. Conclusions: Our results indicate that the optimal imaging conditions and reconstruction parameters are very necessary to obtain high-resolution images and quantitative accuracy, especially for the high-precision recognition of tiny lesions. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing and Analysis)
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18 pages, 2467 KiB  
Article
Posteroanterior Chest X-ray Image Classification with a Multilayer 1D Convolutional Neural Network-Based Classifier for Cardiomegaly Level Screening
by Chia-Hung Lin, Feng-Zhou Zhang, Jian-Xing Wu, Ning-Sheng Pai, Pi-Yun Chen, Ching-Chou Pai and Chung-Dann Kan
Electronics 2022, 11(9), 1364; https://doi.org/10.3390/electronics11091364 - 25 Apr 2022
Cited by 6 | Viewed by 6585
Abstract
Palpitations, chest tightness, and shortness of breath are early indications of cardiomegaly, which is an asymptomatic disease. Their causes and treatment strategies are different due to differing indications. Hence, early screening of cardiomegaly levels can be used to make a strategy for administering [...] Read more.
Palpitations, chest tightness, and shortness of breath are early indications of cardiomegaly, which is an asymptomatic disease. Their causes and treatment strategies are different due to differing indications. Hence, early screening of cardiomegaly levels can be used to make a strategy for administering drugs and surgical treatments. In this study, we will establish a multilayer one-dimensional (1D) convolutional neural network (CNN)-based classifier for automatic cardiomegaly level screening based on chest X-ray (CXR) image classification in frontal posteroanterior view. Using two-round 1D convolutional processes in the convolutional pooling layer, two-dimensional (2D) feature maps can be converted into feature signals, which can enhance their characteristics for identifying normal condition and cardiomegaly levels. In the classification layer, a classifier based on gray relational analysis, which has a straightforward mathematical operation, is used to screen the cardiomegaly levels. Based on the collected datasets from the National Institutes of Health CXR image database, the proposed multilayer 1D CNN-based classifier with K-fold cross-validation has promising results for the intended medical purpose, with precision of 97.80%, recall of 98.20%, accuracy of 98.00%, and F1 score of 0.9799. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Image Processing and Analysis)
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