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Sensors and Devices for Biomedical Image Processing
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Sensors and Devices for Biomedical Image Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 4052

Special Issue Editors

Dr. Paulo Jorge Coelho

E-Mail Website
Guest Editor
1. School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
2. Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), 3030-290 Coimbra, Portugal
Interests: computer vision and image processing; artificial intelligence and deep learning in health systems; medical image analysis; biosensors; sensor-based systems; industrial automation systems; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Ivan Pires

E-Mail Website
Guest Editor
Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal
Interests: ambient assisted living; human activity recognition; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technologies for monitoring human health have recently become increasingly popular. New approaches for identifying, tracking, and monitoring health-related biomarkers have been made possible by hardware and software system developments. The topic of “Sensors and Devices for Biomedical Image Processing” is a crucial area in the fields of biomedical engineering and medical imaging technology. It delves into developing and applying various sensors and devices designed specifically for capturing, processing, and analyzing biomedical images. These technologies are integral to modern medicine, enabling more accurate diagnoses, real-time monitoring, and effective treatments.

This Special Issue, therefore, aims to gather original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of sensors and devices focused on biomedical imaging processing.

Potential topics include, but are not limited to, the following:

  • Biomedical imaging and sensing;
  • Human health;
  • Biomedical sensors;
  • Detectors;
  • Medical Imaging;
  • Multimodal monitoring;
  • Portable technology;
  • Machine learning;
  • Data-driven analysis;
  • Image processing and analysis for disease detection (MRI, X-ray, PET, etc.).

Dr. Paulo Jorge Coelho
Dr. Ivan Pires
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. Sensors 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 2600 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

  • sensor
  • biomedical
  • image processing

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Published Papers (2 papers)

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Research

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24 pages, 4377 KiB  
Article
Feasibility Study on the Use of Infrared Cameras for Skin Cancer Detection under a Proposed Data Degradation Model
by Ricardo F. Soto and Sebastián E. Godoy
Sensors 2024, 24(16), 5152; https://doi.org/10.3390/s24165152 - 9 Aug 2024
Viewed by 1165
Abstract
Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For [...] Read more.
Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications. Full article
(This article belongs to the Special Issue Sensors and Devices for Biomedical Image Processing)
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Review

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38 pages, 7919 KiB  
Review
Compressed Sensing for Biomedical Photoacoustic Imaging: A Review
by Yuanmao Wang, Yang Chen, Yongjian Zhao and Siyu Liu
Sensors 2024, 24(9), 2670; https://doi.org/10.3390/s24092670 - 23 Apr 2024
Cited by 2 | Viewed by 2448
Abstract
Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after [...] Read more.
Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed. Full article
(This article belongs to the Special Issue Sensors and Devices for Biomedical Image Processing)
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