The 2nd Edition: Signal and Image Processing in Biomedical Applications Using Machine Learning

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 304

Special Issue Editors

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Guest Editor
Research Centre in Digitalization and Intelligent Robotics (CEDRI), Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Interests: speech synthesis; prosody; speech systems; modulation; prediction with neural networks; DNN; LSTM; time series forecast and biological signals analysis; namely EEG; ECG and voice
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering (SEL), São Carlos School of Engineering (EESC), University of São Paulo (USP), São Carlos 13566-590, Brazil
Interests: analog and digital integrated circuits; micromachining and micro/nanofabrication technologies for mixed-mode/RF systems; solid-state integrated sensors; microactuators and microsystems; micro/nanodevices for industrial and biomedical applications; wireless systems for sensors and actuators; optical sensors and actuators; material technology for microsystems; microprocessor/microcomputer-based instrumentation and data-acquisition systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dramatic improvement in biomedical sensing technology has allowed us to acquire more and better information about the human body. The data sources encompass an enormous spectrum of areas, ranging from large phenomena, such as human gait analysis from wearable sensors or eye movement analysis for disease detection, to nano-scale phenomena, such as cell identification in histological microscopy or observing bone growth using microCT imaging. Hence, signal and image processing techniques have a central role in the extraction of meaningful information from such sources. In fact, advancements in signal and image processing techniques have allowed us to obtain improvements at a faster pace than the evolution of hardware. Such improvements, in such a wide landscape of data sources, have enhanced the need for advanced and specific technologies, tailored to each situation, either to improve quality or to estimate high-level information.

In addition, in recent years, artificial intelligence has been shown to offer high-performance mechanisms to deal with these situations, offering robust data models that are able to cope with large, non-linear data spaces. Training algorithms have also become increasingly efficient, being able to keep up with the evolution of data models. Good generalization capabilities and high fidelity can be achieved, even with apparently limited or sparse data. Many of these systems outperform human capacities and their use is becoming an established standard.

However, with such a fast evolution pace, the application landscape continues to grow, while many challenges still remain. For each type of signal or image source, improvements can be pursued in the following areas:

  • Data collection, compression and visualization;
  • Data exploration;
  • Feature extraction, selection, enhancement and analysis;
  • Data augmentation;
  • Model selection, tuning and explainability;
  • Transfer learning;
  • Parameter space exploration.

The possibility of improving disease detection or enhancing therapies, boosting the quality of life of many people, makes this one of the most exciting current research areas.

In this Special Issue, prospective authors are invited to submit innovative research aimed to solve challenges in application areas such as, inter alia, clinical (diagnostic, rehabilitation, monitoring) and biomedical research (histology, anatomy, physiology) and human–machine interfacing (acquisition technologies, stimulation). Some of the encompassed data sources include, but are not limited to, the following:

  • Signals: EEG, EMG, ECG, EOG, electroretinogram (ERG), evoked potentials, local field potentials, deep brain stimulation (open/closed-loop), magnetoencephalography (MEG), actigraphy, gait analysis;
  • Medical imaging: X-ray, PET, CT or micro-CT, PET-CT, MRI, SPECT;
  • Biological and molecular imaging: photoacoustic/coherence tomography (PAT/OCT), MRS, mass spectrometry, optical imaging, phase-contrast imaging, laser scanning confocal microscopy (LSCM);
  • Human–machine interaction: wearable data (gaze, dynamics, heart rate), stimulation (touch, vision), emotion, disease, altered states (drunk, sleepiness).

Dr. Luis Coelho
Prof. Dr. João Paulo Ramos Teixeira
Dr. João Paulo Pereira do Carmo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • signal processing
  • image processing
  • machine learning

Published Papers

This special issue is now open for submission.
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