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Bioengineering
Special Issues
Recent Progress in Biomedical Image Processing and Analysis
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Recent Progress in Biomedical Image Processing and Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 4475

Special Issue Editors

Dr. Dildar Hussain

E-Mail Website
Guest Editor
Department of Data Science, Sejong University, Seoul 05006, Republic of Korea
Interests: biomedical imaging; image processing; image analysis; machine learning in healthcare; deep learning in medical imaging; AI in medical imaging; image reconstruction; segmentation and registration; pattern recognition; multi-modal imaging; diagnostic imaging tools; computer vision; radiomics; image enhancement techniques; image classification; healthcare data analysis; biomedical natural language processing
Dr. Jawad Khan

E-Mail Website
Guest Editor
School of Computing, Gachon University, Seongnam 461701, Republic of Korea
Interests: computer vision; medical image processing; text mining; cloud computing; IoT

Special Issue Information

Dear Colleagues,

Advances in biomedical image processing and analysis have revolutionized the field of medicine by allowing for more accurate diagnoses, personalized treatments, and improved patient outcomes. With the rapid development of imaging technologies such as MRI, CT, and PET scans, there is a growing need for robust image processing and analysis techniques to extract meaningful insights from these complex datasets. Researchers and scientists in the field are constantly exploring innovative algorithms and methodologies to enhance image quality, segment tissue regions, detect abnormalities, and track disease progression in real-time.

Some key areas of progress in biomedical image processing and analysis include the use of artificial intelligence and machine learning algorithms for automated image interpretation, 3D reconstruction techniques for enhanced visualization, and the fusion of multi-modal imaging for comprehensive diagnosis. As a result, healthcare professionals can now leverage these cutting-edge tools to make faster and more accurate clinical decisions, leading to improved patient care and outcomes. In conclusion, ongoing advancements in biomedical image processing and analysis are paving the way for a new era of precision medicine that is transforming the landscape of healthcare.

Dr. Dildar Hussain
Dr. Jawad Khan
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • biomedical imaging
  • image processing
  • image analysis
  • artificial intelligence
  • machine learning
  • medical diagnosis
  • precision medicine
  • healthcare technology

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Published Papers (2 papers)

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32 pages, 1456 KiB  
Article
A Study on Staging Cystic Echinococcosis Using Machine Learning Methods
by Tuvshinsaikhan Tegshee, Temuulen Dorjsuren, Sungju Lee and Dolgorsuren Batjargal
Bioengineering 2025, 12(2), 181; https://doi.org/10.3390/bioengineering12020181 - 13 Feb 2025
Viewed by 894
Abstract
Cystic echinococcosis (CE) is a chronic parasitic disease characterized by slow progression and non-specific clinical symptoms, often leading to delayed diagnosis and treatment. Early and precise diagnosis is crucial for effective treatment, particularly considering the five stages of CE as outlined by the [...] Read more.
Cystic echinococcosis (CE) is a chronic parasitic disease characterized by slow progression and non-specific clinical symptoms, often leading to delayed diagnosis and treatment. Early and precise diagnosis is crucial for effective treatment, particularly considering the five stages of CE as outlined by the World Health Organization (WHO). This study explores the development of an advanced system that leverages artificial intelligence (AI) and machine learning (ML) techniques to classify CE cysts into stages using various imaging modalities, including computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI). A total of ten ML algorithms were evaluated across these datasets, using performance metrics such as accuracy, precision, recall (sensitivity), specificity, and F1 score. These metrics offer diverse criteria for assessing model performance. To address this, we propose a normalization and scoring technique that consolidates all metrics into a final score, allowing for the identification of the best model that meets the desired criteria for CE cyst classification. The experimental results demonstrate that hybrid models, such as CNN+ResNet and Inception+ResNet, consistently outperformed other models across all three datasets. Specifically, CNN+ResNet, selected as the best model, achieved 97.55% accuracy on CT images, 93.99% accuracy on US images, and 100% accuracy on MRI images. This research underscores the potential of hybrid and pre-trained models in advancing medical image classification, providing a promising approach to improving the differential diagnosis of CE disease. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing and Analysis)
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Review

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35 pages, 1899 KiB  
Review
Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact
by Dildar Hussain, Naseem Abbas and Jawad Khan
Bioengineering 2024, 11(12), 1213; https://doi.org/10.3390/bioengineering11121213 - 30 Nov 2024
Cited by 1 | Viewed by 2952
Abstract
This review presents a detailed examination of the most recent advancements in positron emission tomography–computed tomography (PET-CT) multimodal imaging over the past five years. The fusion of PET and CT technologies has revolutionized medical imaging, offering unprecedented insights into both anatomical structure and [...] Read more.
This review presents a detailed examination of the most recent advancements in positron emission tomography–computed tomography (PET-CT) multimodal imaging over the past five years. The fusion of PET and CT technologies has revolutionized medical imaging, offering unprecedented insights into both anatomical structure and functional processes. The analysis delves into key technological innovations, including advancements in image reconstruction, data-driven gating, and time-of-flight capabilities, highlighting their impact on enhancing diagnostic accuracy and clinical outcomes. Illustrative case studies underscore the transformative role of PET-CT in lesion detection, disease characterization, and treatment response evaluation. Additionally, the review explores future prospects and challenges in PET-CT, advocating for the integration and evaluation of emerging technologies to improve patient care. This comprehensive synthesis aims to equip healthcare professionals, researchers, and industry stakeholders with the knowledge and tools necessary to navigate the evolving landscape of PET-CT multimodal imaging. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing and Analysis)
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