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Biomedical Data and Imaging: Sensing, Understanding and Applications
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Biomedical Data and Imaging: Sensing, Understanding and Applications

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 6574

Special Issue Editors

Dr. Hatem Rashwan

E-Mail Website
Guest Editor
Departament d’Enginyeria Informatica i Matemátiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
Interests: image processing; computer vision; machine learning; perception models; scene analysis; pattern recognition
Prof. Dr. Domenec Puig

E-Mail Website
Guest Editor
Departament d’Enginyeria Informatica i Matemátiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain
Interests: image processing; pattern recognition; computer vision; medical image analysis; machine learning; perception models; scene analysis; mobile robotics and socially assistive robotics

Special Issue Information

Dear Colleagues,

Biomedical data and image sensing and processing are essential tools in modern healthcare, providing valuable insights into patient health and aiding in diagnosing and treating a wide range of medical conditions. This Special Issue of Sensors aims to showcase the latest research and developments in biomedical data and image sensing and processing, focusing on both the underlying theory and its practical applications.

We invite original research papers, reviews, and case studies that address the following topics:

  • Machine learning and deep learning approaches for biomedical data and image processing;
  • Signal and image processing and feature extraction techniques for biomedical data and imaging;
  • Emerging technologies for biomedical data sensing, acquisition, and analysis;
  • Applications of biomedical data and imaging in disease diagnosis and treatment;
  • Privacy and security considerations in biomedical data processing.

Manuscripts will be subject to a rigorous peer-review process to ensure high-quality publications. We welcome submissions from researchers, clinicians, and engineers in medicine, computer science, electrical engineering, and related areas.

The expected outcomes of this Special Issue include:

  • Advancing the state of the art in biomedical data and image processing;
  • Promoting collaboration between researchers and practitioners in the field;
  • Providing a platform for researchers to showcase their work;
  • Informing policymakers and the public about the potential of biomedical data and image processing to improve healthcare outcomes.

We hope that this Special Issue will inspire new research ideas and collaborations and provide a valuable resource for researchers and practitioners in the field of biomedical data and image sensing and processing.

Dr. Hatem Rashwan
Prof. Dr. Domenec Puig
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

  • biomedical data
  • medical imaging
  • image processing
  • signal processing
  • machine learning
  • deep learning
  • computer-aided diagnosis
  • feature extraction
  • pattern recognition
  • data acquisition
  • data sensing
  • privacy and security
  • healthcare applications
  • clinical decision making
  • disease diagnosis
  • treatment planning
  • data analytics
  • sensor technology
  • multimodal imaging
  • quantitative analysis

Published Papers (5 papers)

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Research

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29 pages, 5402 KiB  
Article
Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST)
by Valentino Šafran, Simon Lin, Jama Nateqi, Alistair G. Martin, Urška Smrke, Umut Ariöz, Nejc Plohl, Matej Rojc, Dina Bēma, Marcela Chávez, Matej Horvat and Izidor Mlakar
Sensors 2024, 24(4), 1101; https://doi.org/10.3390/s24041101 - 08 Feb 2024
Viewed by 944
Abstract
The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity [...] Read more.
The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app’s usability as above satisfactory (i.e., 7.9 on 1–10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1–10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care. Full article
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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15 pages, 4186 KiB  
Article
Visualization of Subcutaneous Blood Vessels Based on Hyperspectral Imaging and Three-Wavelength Index Images
by Mohammed Hamza, Roman Skidanov and Vladimir Podlipnov
Sensors 2023, 23(21), 8895; https://doi.org/10.3390/s23218895 - 01 Nov 2023
Cited by 1 | Viewed by 1375
Abstract
Blood vessel visualization technology allows nursing staff to transition from traditional palpation or touch to locate the subcutaneous blood vessels to visualized localization by providing a clear visual aid for performing various medical procedures accurately and efficiently involving blood vessels; this can further [...] Read more.
Blood vessel visualization technology allows nursing staff to transition from traditional palpation or touch to locate the subcutaneous blood vessels to visualized localization by providing a clear visual aid for performing various medical procedures accurately and efficiently involving blood vessels; this can further improve the first-attempt puncture success rate for nursing staff and reduce the pain of patients. We propose a novel technique for hyperspectral visualization of blood vessels in human skin. An experiment with six participants with different skin types, race, and nationality backgrounds is described. A mere separation of spectral layers for different skin types is shown to be insufficient. The use of three-wavelength indices in imaging has shown a significant improvement in the quality of results compared to using only two-wavelength indices. This improvement can be attributed to an increase in the contrast ratio, which can be as high as 25%. We propose and implement a technique for finding new index formulae based on an exhaustive search and a binary blood-vessel image obtained through an expert assessment. As a result of the search, a novel index formula was deduced, allowing high-contrast blood vessel images to be generated for any skin type. Full article
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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17 pages, 3429 KiB  
Article
Investigating the Joint Amplitude and Phase Imaging of Stained Samples in Automatic Diagnosis
by Houda Hassini, Bernadette Dorizzi, Marc Thellier, Jacques Klossa and Yaneck Gottesman
Sensors 2023, 23(18), 7932; https://doi.org/10.3390/s23187932 - 16 Sep 2023
Viewed by 866
Abstract
The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on [...] Read more.
The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition. Full article
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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Review

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17 pages, 578 KiB  
Review
A Review on 3D Scanners Studies for Producing Customized Orthoses
by Rui Silva, Bruna Silva, Cristiana Fernandes, Pedro Morouço, Nuno Alves and António Veloso
Sensors 2024, 24(5), 1373; https://doi.org/10.3390/s24051373 - 20 Feb 2024
Viewed by 1034
Abstract
When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, as well as excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic environment and difficulty in [...] Read more.
When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, as well as excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic environment and difficulty in keeping the injury clean during treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all of these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which is a time-consuming and error-prone method. From another point of view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but which induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation, have emerged. This article describes the use of various types of scanners capable of digitizing the human body to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology into clinical environment. Full article
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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34 pages, 853 KiB  
Review
Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review
by David Jiménez-Murillo, Andrés Eduardo Castro-Ospina, Leonardo Duque-Muñoz, Juan David Martínez-Vargas, Jazmín Ximena Suárez-Revelo, Jorge Mario Vélez-Arango and Maria de la Iglesia-Vayá
Sensors 2023, 23(16), 7072; https://doi.org/10.3390/s23167072 - 10 Aug 2023
Viewed by 1398
Abstract
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure [...] Read more.
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain—is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management. Full article
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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