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Applied Sciences
Special Issues
Image Processing and Computer Vision for Biomedical Applications
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Image Processing and Computer Vision for Biomedical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 2222

Special Issue Editor

Dr. Athanasios Nikolaidis

E-Mail Website
Guest Editor
Department of Informatics, Computer and Telecommunications Engineering, International Hellenic University, Terma Magnesias Str., 62124 Serres, Greece
Interests: multimedia systems; digital image processing; digital signal processing; computer vision; computer graphics; virtual, augmented and mixed reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, information technology has been integrated into several medical procedures with the aim of assisting medical doctors in disease prevention, diagnosis, treatment and surgery.

Biomedical informatics is a relatively new field at the intersection of computer science and biology, which aims to provide healthcare solutions. Of all the fields of informatics involved in biomedical applications, image processing and computer vision seem to be the most prominent, as visual content is known to possess the richest and most valuable information for any application field.

Examples of the exploitation of these computer science fields in medicine is the early diagnosis of breast cancer from mammograms, the early diagnosis of macular degeneration from optical coherence tomography or the early identification of the best embryos for IVF implantation. Machine learning and, most recently, deep learning have boosted the performance of the proposed techniques, especially in the case where large, annotated training datasets exist.

The present Special Issue encourages potential authors to submit their research on image processing and computer vision techniques for biomedical applications, either as full articles or reviews, on any of the following (non-exclusive) topics:

  • Brain and spinal cord anomaly detection.
  • The detection of injuries and abnormalities in joints.
  • Tumor and cyst detection.
  • Breast cancer screening.
  • Abdominal organs disease diagnosis.
  • Heart problems detection.
  • Aortic aneurysm screening.

Dr. Athanasios Nikolaidis
Guest Editor

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. Applied Sciences 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

  • computer vision
  • image processing
  • biomedical applications
  • MRI
  • CT
  • ultrasound

Published Papers (2 papers)

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Research

13 pages, 7246 KiB  
Article
Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
by Yuta Suganuma, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita, Yuki Suzuki, Noriyuki Tomiyama and Shoji Kido
Appl. Sci. 2023, 13(19), 10765; https://doi.org/10.3390/app131910765 - 27 Sep 2023
Viewed by 873
Abstract
PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden [...] Read more.
PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden on radiologists during diagnosis. Thus, the development of computer-aided diagnosis (CAD) and technologies assisting in diagnosis has been requested. However, because FDG accumulation in PET images differs for each organ, recognizing organ regions is essential for developing lesion detection and analysis algorithms for PET/CT images. Therefore, we developed a method for automatically extracting organ regions from PET/CT images using U-Net or DenseUNet, which are deep-learning-based segmentation networks. The proposed method is a hybrid approach combining morphological and functional information obtained from LDCT and PET images. Moreover, pre-training using ImageNet and RadImageNet was performed and compared. The best extraction accuracy was obtained by pre-training ImageNet with Dice indices of 94.1, 93.9, 91.3, and 75.1% for the liver, kidney, spleen, and pancreas, respectively. This method obtained better extraction accuracy for low-quality PET/CT images than did existing studies on PET/CT images and was comparable to existing studies on diagnostic contrast-enhanced CT images using the hybrid method and pre-training. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision for Biomedical Applications)
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12 pages, 7402 KiB  
Article
Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
by Kamil Szostek, Julia Lasek and Adam Piórkowski
Appl. Sci. 2023, 13(17), 9805; https://doi.org/10.3390/app13179805 - 30 Aug 2023
Viewed by 961
Abstract
Medical simulations have proven to be highly valuable in the education of healthcare professionals. This significance was particularly evident during the COVID-19 pandemic, where simulators provided a safe and effective means of training healthcare practitioners in the principles of lung ultrasonography without exposing [...] Read more.
Medical simulations have proven to be highly valuable in the education of healthcare professionals. This significance was particularly evident during the COVID-19 pandemic, where simulators provided a safe and effective means of training healthcare practitioners in the principles of lung ultrasonography without exposing them to the risk of infection. This further emphasizes another important advantage of medical simulation in the field of medical education. This paper presents the principles of ultrasound simulation in the context of inflammatory lung conditions. The propagation of sound waves in this environment is discussed, with a specific focus on key diagnostic artifacts in lung imaging. The simulated medium was modeled by assigning appropriate acoustic characteristics to the tissue components present in the simulated study. A simulation engine was developed, taking into consideration the requirements of easy accessibility through a web browser and high-performance simulation through GPU-based computing. The obtained images were compared with real-world examples. An analysis of simulation parameter selection was conducted to achieve real-time simulations while maintaining excellent visual quality. The research findings demonstrate the feasibility of real-time, high-quality visualization in ultrasound simulation, providing valuable insights for the development of educational tools and diagnostic training in the field of medical imaging. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision for Biomedical Applications)
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