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Biomimetics
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New Biomimetic Advances in Signal and Image Processing for Biomedical...
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New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 1587

Special Issue Editors

Prof. Dr. Jong-Myon Kim

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: fault diagnosis; prognosis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Naveed Rehman

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, DK-8200 Aarhus, Denmark
Interests: time series analysis; signal processing; machine learning; medical image and signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zahoor Ahmad

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: fault detection and diagnosis; signal processing; multiscale signal analysis; sensor fusion; signal to image conversion and analysis; artificial intelligence; explainable machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomimetic principles in signal and image processing techniques for biomedical applications represent a promising field that draws inspiration from nature to enhance human understanding and the manipulation of biological data. These approaches utilize the principles of natural systems such as neural networks, genetic algorithms, and swarm intelligence to process signals and images with increased efficiency and accuracy.

Recent years have witnessed a growing interest in utilizing biomimetic principles in signal and image processing techniques for advancing medical diagnostics, personalized medicine, and therapeutic interventions, ultimately contributing to better patient care and outcomes.

This Special Issue aims to explore the latest advances in biomimetic approaches applied to signal and image processing within the realm of biomedical research. The scope of this Special Issue encompasses a wide range of topics including, but not limited to, bio-inspired algorithms for signal and image processing; bio-inspired intelligent techniques for medical image classification, detection, localization, and segmentation; biomimetic sensor design and signal processing for medical diagnostics; medical signal processing; medical image processing; and bio-inspired artificial intelligence for signal and image analysis. Furthermore, review articles and research concerning recent advances in bio-inspired techniques for signal and image processing are also encouraged.

Prof. Dr. Jong-Myon Kim
Dr. Naveed Rehman
Dr. Zahoor Ahmad
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. Biomimetics 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 2200 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

  • signal processing
  • medical signal (e.g., EEG, ECG) processing
  • image processing
  • medical image (e.g., CT, MRI, ultrasound) processing
  • nature-inspired algorithms
  • bio-inspired algorithms
  • artificial intelligence
  • deep learning
  • bio-inspired intelligent algorithms
  • biomimetic sensor design

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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21 pages, 5444 KiB  
Article
Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning
by Alka Jalan, Deepti Mishra, Marisha and Manjari Gupta
Biomimetics 2025, 10(7), 449; https://doi.org/10.3390/biomimetics10070449 - 7 Jul 2025
Viewed by 402
Abstract
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, [...] Read more.
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025)
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12 pages, 2660 KiB  
Article
Fast and Fractionated: Correlation of Dose Attenuation and the Response of Human Cancer Cells in a New Anthropomorphic Brain Phantom
by Bernd Frerker, Elette Engels, Jason Paino, Vincent de Rover, John Paul Bustillo, Marie Wegner, Matthew Cameron, Stefan Fiedler, Daniel Häusermann, Guido Hildebrandt, Michael Lerch and Elisabeth Schültke
Biomimetics 2025, 10(7), 440; https://doi.org/10.3390/biomimetics10070440 - 3 Jul 2025
Viewed by 301
Abstract
The results of radiotherapy in patients with primary malignant brain tumors are extremely dissatisfactory: the overall survival after a diagnosis of glioblastoma is typically less than three years. The development of spatially fractionated radiotherapy techniques could help to improve this bleak prognosis. In [...] Read more.
The results of radiotherapy in patients with primary malignant brain tumors are extremely dissatisfactory: the overall survival after a diagnosis of glioblastoma is typically less than three years. The development of spatially fractionated radiotherapy techniques could help to improve this bleak prognosis. In order to develop technical equipment and organ-specific therapy plans, dosimetry studies as well as radiobiology studies are conducted. Although perfect spheres are considered optimal phantoms by physicists, this does not reflect the wide variety of head sizes and shapes in our patient community. Depth from surface and X-ray dose absorption by tissue between dose entry point and target, two key parameters in medical physics planning, are largely determined by the shape and thickness of the skull bone. We have, therefore, designed and produced a biomimetic tool to correlate measured technical dose and biological response in human cancer cells: a brain phantom, produced from tissue-equivalent materials. In a first pilot study, utilizing our phantom to correlate technical dose measurements and metabolic response to radiation in human cancer cell lines, we demonstrate why an anthropomorphic phantom is preferable over a simple spheroid phantom. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025)
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19 pages, 844 KiB  
Article
Optimizing Class Imbalance in Facial Expression Recognition Using Dynamic Intra-Class Clustering
by Qingdu Li, Keting Fu, Jian Liu, Yishan Li, Qinze Ren, Kang Xu, Junxiu Fu, Na Liu and Ye Yuan
Biomimetics 2025, 10(5), 296; https://doi.org/10.3390/biomimetics10050296 - 8 May 2025
Viewed by 537
Abstract
While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that [...] Read more.
While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article
(This article belongs to the Special Issue New Biomimetic Advances in Signal and Image Processing for Biomedical Applications 2025)
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