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Biomedical Imaging, Sensing and Signal Processing
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Biomedical Imaging, Sensing and Signal Processing

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

Deadline for manuscript submissions: 20 January 2025 | Viewed by 6761

Special Issue Editor

Dr. Saideh Ferdowsi

E-Mail Website
Guest Editor
School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, UK
Interests: biomedical signal and image processing; data fusion; blind source separation and machine/deep learning; EEG; fMRI; ECG
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging is a dynamic field that has led to significant improvements in our understanding of body and brain functions, the development of medicine, the development of rehabilitation methods, and many other areas relevant to health and wellbeing. Biomedical imaging includes a diverse array of modalities such as X-rays, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, electroencephalograms (EEGs), functional near-infrared spectroscopy (fNIRS), and positron emission tomography (PET). These imaging techniques provide detailed visualizations of internal anatomical structures, facilitating non-invasive diagnosis and precise medical interventions.

Sensing technologies enable us to capture real-time data related to physiological states, biomarkers, and environmental factors. This involves the usage of wearable devices, biosensors, and other cutting-edge technologies to monitor and analyze vital signs, allowing personalized and proactive healthcare.

It is undeniable that signal processing and machine learning methods play a pivotal role in removing noise and extracting meaningful information from biomedical data. Techniques such as signal filtering, feature extraction, and pattern recognition enhance the accuracy of diagnostics and enable the development of smart healthcare systems.

The objective of this Special Issue is to attract the most recent research and findings on the design, development, and experimentation of healthcare-related technologies and computational methodologies. This Special Issue welcomes contributions that engage but are not limited to any of the following topics:

  • biomedical signal and image processing
  • smart monitoring and assisted living systems
  • deep learning for healthcare data
  • sensor fusion of biomedical data
  • brain–computer interface
  • mental health
  • computational neuroscience
  • electroencephalograms (EEGs)
  • magnetic resonance spectroscopy (MRI)
  • functional magnetic resonance spectroscopy (fMRI)
  • functional near-infrared spectroscopy (fNIRS)
  • magnetoencephalograms (MEGs)
  • electromyography (EMG)
  • health signal processing/monitoring
  • machine learning and artificial intelligent applications in health and wellbeing
  • physiological signal processing

Dr. Saideh Ferdowsi
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. 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 imaging
  • neuroimage
  • signal processing
  • biosensors
  • machine learning
  • artificial intelligence
  • deep learning

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Published Papers (5 papers)

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Research

18 pages, 7815 KiB  
Article
Feasibility of Backscattering Coefficient Evaluation of Soft Tissue Using High-Frequency Annular Array Probe
by Jungtaek Choi, Jeffrey A. Ketterling, Jonathan Mamou, Cameron Hoerig, Shinnosuke Hirata, Kenji Yoshida and Tadashi Yamaguchi
Sensors 2024, 24(22), 7118; https://doi.org/10.3390/s24227118 - 5 Nov 2024
Viewed by 410
Abstract
The objective of this work is to address the need for versatile and effective tissue characterization in abdominal ultrasound diagnosis using a simpler system. We evaluated the backscattering coefficient (BSC) of several tissue-mimicking phantoms utilizing three different ultrasonic probes: a single-element transducer, a [...] Read more.
The objective of this work is to address the need for versatile and effective tissue characterization in abdominal ultrasound diagnosis using a simpler system. We evaluated the backscattering coefficient (BSC) of several tissue-mimicking phantoms utilizing three different ultrasonic probes: a single-element transducer, a linear array probe for clinical use, and a laboratory-made annular array probe. The single-element transducer, commonly used in developing fundamental BSC evaluation methods, served as a benchmark. The linear array probe provided a clinical comparison, while the annular array probe was tested for its potential in high-frequency and high-resolution ultrasonic observations. Our findings demonstrate that the annular array probe meets clinical demands by providing accurate BSC measurements, showcasing its capability for high-frequency and high-resolution imaging with a simpler, more versatile system. Full article
(This article belongs to the Special Issue Biomedical Imaging, Sensing and Signal Processing)
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9 pages, 6167 KiB  
Communication
A Pressure Sensing Device to Assist in Colonoscopic Procedures to Prevent Perforation—A Case Study
by Se-Eun Kim, Young-jae Kang, Chang-ho Jung, Yongho Jeon, Yunho Jung and Moon Gu Lee
Sensors 2024, 24(17), 5711; https://doi.org/10.3390/s24175711 - 2 Sep 2024
Viewed by 648
Abstract
Colonoscopy has a limited field of view because it relies solely on a small camera attached to the end of the scope and a screen displayed on a monitor. Consequently, the quality and safety of diagnosis and treatment depend on the experience and [...] Read more.
Colonoscopy has a limited field of view because it relies solely on a small camera attached to the end of the scope and a screen displayed on a monitor. Consequently, the quality and safety of diagnosis and treatment depend on the experience and skills of the gastroenterologist. When a novice attempts to insert the colonoscope during the procedure, excessive pressure can sometimes be applied to the colon wall. This pressure can cause a medical accident known as colonic perforation, which the physician should prevent. We propose an assisting device that senses the pressure applied to the colon wall, analyzes the risk of perforation, and warns the physician in real time. Flexible pressure sensors are attached to the surface of the colonoscope shaft. These sensors measure pressure signals during a colonoscopy procedure. A simple signal processor is used to collect and process the pressure signals. In the experiment, a colonoscope equipped with the proposed device was inserted into a simulated colon made from a colon extracted from a pig. The processed data were visually communicated to the gastroenterologist via displays and light-emitting diodes (LEDs). The device helps the physician continuously monitor and prevent excessive pressure on the colon wall. In this experiment, the device appropriately generated and delivered warnings to help the physicians prevent colonic perforation. In the future, the device is to be improved, and more experiments will be performed in live swine models or humans to confirm its efficacy and safety. Full article
(This article belongs to the Special Issue Biomedical Imaging, Sensing and Signal Processing)
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12 pages, 1912 KiB  
Article
Bedside Magnetocardiography with a Scalar Sensor Array
by Geoffrey Z. Iwata, Christian T. Nguyen, Kevin Tharratt, Maximilian Ruf, Tucker Reinhardt, Jordan Crivelli-Decker, Madelaine S. Z. Liddy, Alison E. Rugar, Frances Lu, Kirstin Aschbacher, Ethan J. Pratt, Kit Yee Au-Yeung and Stefan Bogdanovic
Sensors 2024, 24(16), 5402; https://doi.org/10.3390/s24165402 - 21 Aug 2024
Viewed by 1056
Abstract
Decades of research have shown that magnetocardiography (MCG) has the potential to improve cardiac care decisions. However, sensor and system limitations have prevented its widespread adoption in clinical practice. We report an MCG system built around an array of scalar, optically pumped magnetometers [...] Read more.
Decades of research have shown that magnetocardiography (MCG) has the potential to improve cardiac care decisions. However, sensor and system limitations have prevented its widespread adoption in clinical practice. We report an MCG system built around an array of scalar, optically pumped magnetometers (OPMs) that effectively rejects ambient magnetic interference without magnetic shielding. We successfully used this system, in conjunction with custom hardware and noise rejection algorithms, to record magneto-cardiograms and functional magnetic field maps from 30 volunteers in a regular downtown office environment. This demonstrates the technical feasibility of deploying our device architecture at the point-of-care, a key step in making MCG usable in real-world settings. Full article
(This article belongs to the Special Issue Biomedical Imaging, Sensing and Signal Processing)
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20 pages, 27344 KiB  
Article
DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries
by Binfeng Zou, Xingru Huang, Yitao Jiang, Kai Jin and Yaoqi Sun
Sensors 2024, 24(14), 4748; https://doi.org/10.3390/s24144748 - 22 Jul 2024
Cited by 2 | Viewed by 1274
Abstract
The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression [...] Read more.
The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494. Full article
(This article belongs to the Special Issue Biomedical Imaging, Sensing and Signal Processing)
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11 pages, 2937 KiB  
Article
The Importance of Preconditioning for the Sonographic Assessment of Plantar Fascia Thickness and Shear Wave Velocity
by Conor Costello, Panagiotis Chatzistergos, Helen Branthwaite and Nachiappan Chockalingam
Sensors 2024, 24(14), 4552; https://doi.org/10.3390/s24144552 - 14 Jul 2024
Viewed by 2824
Abstract
Plantar fasciopathy is a very common musculoskeletal complaint that leads to reduced physical activity and undermines the quality of life of patients. It is associated with changes in plantar fascia structure and biomechanics which are most often observed between the tissue’s middle portion [...] Read more.
Plantar fasciopathy is a very common musculoskeletal complaint that leads to reduced physical activity and undermines the quality of life of patients. It is associated with changes in plantar fascia structure and biomechanics which are most often observed between the tissue’s middle portion and the calcaneal insertion. Sonographic measurements of thickness and shear wave (SW) elastography are useful tools for detecting such changes and guide clinical decision making. However, their accuracy can be compromised by variability in the tissue’s loading history. This study investigates the effect of loading history on plantar fascia measurements to conclude whether mitigation measures are needed for more accurate diagnosis. The plantar fasciae of 29 healthy participants were imaged at baseline and after different clinically relevant loading scenarios. The average (±standard deviation) SW velocity was 6.5 m/s (±1.5 m/s) and it significantly increased with loading. Indicatively, five minutes walking increased SW velocity by 14% (95% CI: −1.192, −0.298, t(27), p = 0.005). Thickness between the calcaneal insertion and the middle of the plantar fascia did not change with the tissues’ loading history. These findings suggest that preconditioning protocols are crucial for accurate SW elastography assessments of plantar fasciae and have wider implications for the diagnosis and management of plantar fasciopathy. Full article
(This article belongs to the Special Issue Biomedical Imaging, Sensing and Signal Processing)
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