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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 10816

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Special Issue Editor

Dr. Xiaojun Yu

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: bio-optical imaging; biomedical image processing and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging and signal processing stand at the forefront of medical innovation, providing unprecedented insights into human physiology and pathology. The continuous evolution of imaging technologies generates increasingly complex datasets, while sophisticated sensors capture a wide array of physiological signals. Effectively harnessing this wealth of information is crucial for enhancing diagnostic accuracy, tailoring personalized treatments, and improving patient monitoring. The true potential, however, lies in the synergistic application of advanced processing techniques to extract meaningful patterns from both images and signals.

This Special Issue, “Advanced Biomedical Imaging and Signal Processing”, aims to bring together leading research on cutting-edge methodologies that address the challenges and opportunities in acquiring, processing, analyzing, and interpreting complex biomedical data. We seek contributions that push the boundaries of current techniques, explore novel applications, and foster the integration of imaging and signal data for a more holistic understanding of health and disease.

We invite submissions of high-quality original research articles, comprehensive reviews, perspective pieces, and methodological papers. Topics of interest include, but are not limited to:

Novel Algorithms for Image Analysis:

Advanced image reconstruction techniques for MRI, CT, PET, SPECT, ultrasound, optical imaging (e.g., OCT, microscopy), and X-ray.
AI-driven image segmentation, registration, and feature extraction.
Quantitative imaging biomarkers and radiomics/radio-genomics.
Machine learning and deep learning approaches for image enhancement, classification, and interpretation.

Advanced Biomedical Signal Processing:

Sophisticated processing techniques (e.g., deep learning, statistical signal processing, source separation, time-frequency analysis, and nonlinear dynamics) applied to physiological signals.
Analysis of electrophysiological signals: EEG, MEG, ECG, EMG, and EOG.
Analysis of cardiovascular signals: blood pressure waveforms, PPG, and heart sounds (PCG).
Analysis of respiratory signals: airflow, respiratory effort, and SpO2.
Analysis of neuromechanical signals: motion capture, force/pressure sensor data.
Analysis of other physiological signals: GSR, body temperature, acoustic signals (e.g., lung sounds, cough sounds).
Wearable sensor data processing and artifact removal.
Real-time signal analysis for monitoring and feedback systems.

Integration and Multimodal Analysis:

Techniques for fusing information from multiple imaging modalities (e.g., PET-MRI, CT-Ultrasound).
Methods for integrating biomedical imaging data with physiological signals (e.g., combining fMRI with EEG, correlating imaging biomarkers with ECG features).
Development of computational models informed by both imaging and signal data.
AI and machine learning frameworks for multimodal data analysis in diagnostics, prognostics, and treatment response prediction.

Applications and Systems:

Development of novel imaging systems or sensor technologies coupled with advanced processing.
Application of advanced techniques in specific clinical areas (e.g., neurology, cardiology, oncology, ophthalmology).
Point-of-care diagnostic systems leveraging imaging and signal processing.
Computational tools and platforms for biomedical image and signal analysis.

We look forward to receiving your valuable contributions showcasing the latest advancements at the intersection of biomedical imaging and signal processing.

Dr. Xiaojun Yu
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 250 words) can be sent to the Editorial Office for assessment.

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

bio-optical imaging
biomedical image denoising
biomedical signal processing
computer-aided disease diagnosis
biomedical image segmentation techniques

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Published Papers (9 papers)

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Research

19 pages, 11501 KB

Open AccessArticle

PSAML: A Methodological Approach for Noninvasive Computerized Hydration Level Estimation

by Xin Liu, Xuezhao Kang, Liqun He, Jianrui Zhang, Huyan Ting and Xiaojun Yu

Sensors 2026, 26(11), 3362; https://doi.org/10.3390/s26113362 - 26 May 2026

Viewed by 143

Abstract

Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 4992 KB

Open AccessArticle

Research on Denoising Methods for Laser Doppler Blood Flow Signals Based on Time-Domain Noise Perception and DWT

by Quanxin Sun, Jie Duan, Hui Guo and Aoyan Guo

Sensors 2026, 26(5), 1500; https://doi.org/10.3390/s26051500 - 27 Feb 2026

Viewed by 445

Abstract

Addressing the challenges of composite noise (speckle noise, thermal noise, and random pulse interference) and non-stationarity in laser Doppler flow (LDF) signal processing, as well as the technical limitation of traditional threshold methods in balancing noise suppression and signal fidelity, this study proposes an adaptive denoising algorithm integrating temporal noise perception and discrete wavelet transform (DWT). A composite noise model is first established to characterize the interference. The signal undergoes a five-level DWT decomposition, where a local energy detection mechanism distinguishes signal-dominant from noise-dominant regions. An SNR-driven dynamic thresholding strategy is generated by combining inter-layer adaptive allocation with coefficient-level local weighting, followed by processing with an improved smoothing function to effectively suppress reconstruction artifacts. Simulations at a 1 dB input signal-to-noise ratio (SNR) yielded a 15.45 dB output SNR and a 0.05634 root mean square error (RMSE), outperforming traditional wavelet methods and modern benchmarks such as local variance and variational mode decomposition (VMD). Applied to a practical signal from an isolated vascular phantom with an initial SNR of

- 1.04

dB, the algorithm achieved a 13.86 dB output SNR and a 0.00258 RMSE. Results confirm the algorithm’s effectiveness for high-fidelity waveform capture in complex noise environments, offering a robust solution for vascular hemodynamic monitoring Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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19 pages, 5786 KB

Open AccessArticle

Center of Pressure Measurement Sensing System for Dynamic Biomechanical Signal Acquisition and Its Self-Calibration

by Ni Li, Jianrui Zhang and Keer Zhang

Sensors 2026, 26(3), 910; https://doi.org/10.3390/s26030910 - 30 Jan 2026

Viewed by 474

Abstract

The development of highly dynamic bipedal robots demands sensing capable of capturing key contact-related signals in real time, particularly the Center of Pressure (CoP). CoP is fundamental for locomotion control and state estimation and is also of interest in biomedical applications such as gait analysis and lower-limb assistive devices. To enable reliable CoP acquisition under dynamic walking, this paper presents a foot-mounted measurement system and an online self-calibration method that adapts sensor scale and bias parameters during locomotion using both external foot sensors and the robot’s proprioceptive measurements. We demonstrate an online self-calibration pipeline that updates foot-sensor scale and bias parameters during a walking experiment on a NAO-V5 platform using a sliding window optimization. The reported results indicate improved within-trial consistency relative to an offline-calibrated reference baseline under the tested walking conditions. In addition, the framework reconstructs a digitized estimate of the vertical ground reaction force (vGRF) from load-cell readings; due to ADC quantization and the discrete offline calibration dataset, the vGRF signal may exhibit stepwise behavior and should be interpreted as a reconstructed (digitized) quantity rather than laboratory-grade continuous force metrology. Overall, the proposed sensing-and-calibration pipeline offers a practical solution for dynamic CoP acquisition with low-cost hardware. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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28 pages, 19149 KB

Open AccessArticle

Dynamic Thermography-Based Early Breast Cancer Detection Using Multivariate Time Series

by María-Angélica Espejel-Rivera, Carina Toxqui-Quitl, Alfonso Padilla-Vivanco and Raúl Castro-Ortega

Sensors 2025, 25(24), 7649; https://doi.org/10.3390/s25247649 - 17 Dec 2025

Viewed by 1326

Abstract

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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18 pages, 1070 KB

Open AccessArticle

Advancing Real-Time Polyp Detection in Colonoscopy Imaging: An Anchor-Free Deep Learning Framework with Adaptive Multi-Scale Perception

by Wanyu Qiu, Xiao Yang, Zirui Liu and Chen Qiu

Sensors 2025, 25(24), 7524; https://doi.org/10.3390/s25247524 - 11 Dec 2025

Cited by 3 | Viewed by 995

Abstract

Accurate and real-time detection of polyps in colonoscopy is a critical task for the early prevention of colorectal cancer. The primary difficulties include insufficient extraction of multi-scale contextual cues for polyps of different sizes, inefficient fusion of multi-level features, and a reliance on hand-crafted anchor priors that require extensive tuning and compromise generalization performance. Therefore, we introduce a one-stage anchor-free detector that achieves state-of-the-art accuracy whilst running in real-time on a GTX 1080-Ti GPU workstation. Specifically, to enrich contextual information across a wide spectrum, our Cross-Stage Pyramid Pooling module efficiently aggregates multi-scale contexts through cascaded pooling and cross-stage partial connections. Subsequently, to achieve a robust equilibrium between low-level spatial details and high-level semantics, our Weighted Bidirectional Feature Pyramid Network adaptively integrates features across all scales using learnable channel-wise weights. Furthermore, by reconceptualizing detection as a direct point-to-boundary regression task, our anchor-free head obviates the dependency on hand-tuned priors. This regression is supervised by a Scale-invariant Distance with Aspect-ratio IoU loss, substantially improving localization accuracy for polyps of diverse morphologies. Comprehensive experiments on a large dataset comprising 103,469 colonoscopy frames substantiate the superiority of our method, achieving 98.8% mAP@0.5 and 82.5% mAP@0.5:0.95 at 35.8 FPS. Our method outperforms widely used CNN-based models (e.g., EfficientDet, YOLO series) and recent Transformer-based competitors (e.g., Adamixer, HDETR), demonstrating its potential for clinical application. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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16 pages, 61402 KB

Open AccessArticle

Low-Cost Spinning Disk Confocal Microscopy with a 25-Megapixel Camera

by Guy M. Hagen, Brian Lewis, Summer Levis, Joseph R. Hamilton and Tristan C. Paul

Sensors 2025, 25(23), 7183; https://doi.org/10.3390/s25237183 - 25 Nov 2025

Cited by 1 | Viewed by 1976

Abstract

Spinning disk confocal microscopy enables fast optical sectioning with low phototoxicity but is often inaccessible due to high hardware costs. We present a lower-cost solution using a 25-megapixel machine vision CMOS camera and a custom-built spinning disk. This camera uses a back-illuminated sensor with high quantum efficiency and low read noise. High-resolution images of Thy1-GFP mouse brain slices, Drosophila embryos and larvae, and H&E-stained rat testis verified performance across 3D tissue volumes. The measured resolution was 215.8 nm in X, Y and 521.9 nm in Z with a 60×/1.42 NA objective. The custom disk, made with 18 µm pinholes (180 µm pitch) on a chrome photomask and mounted to an optical chopper motor, enables stable, near-telecentric imaging at lower magnifications. Micromanager software integration allows synchronized control of all hardware, which demonstrates that affordable CMOS sensors can potentially replace sCMOS in spinning disk microscopy, offering an open-access, scalable solution for advanced imaging. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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17 pages, 8430 KB

Open AccessArticle

Robust Audio–Visual Speaker Localization in Noisy Aircraft Cabins for Inflight Medical Assistance

by Qiwu Qin and Yian Zhu

Sensors 2025, 25(18), 5827; https://doi.org/10.3390/s25185827 - 18 Sep 2025

Cited by 3 | Viewed by 1255

Abstract

Active Speaker Localization (ASL) involves identifying both who is speaking and where they are speaking from within audiovisual content. This capability is crucial in constrained and acoustically challenging environments, such as aircraft cabins during in-flight medical emergencies. In this paper, we propose a novel end-to-end Cross-Modal Audio–Visual Fusion Network (CMAVFN) designed specifically for ASL under real-world aviation conditions, which are characterized by engine noise, dynamic lighting, occlusions from seats or oxygen masks, and frequent speaker turnover. Our model directly processes raw video frames and multi-channel ambient audio, eliminating the need for intermediate face detection pipelines. It anchors spatially resolved visual features with directional audio cues using a cross-modal attention mechanism. To enhance spatiotemporal reasoning, we introduce a dual-branch localization decoder and a cross-modal auxiliary supervision loss. Extensive experiments on public datasets (AVA-ActiveSpeaker, EasyCom) and our domain-specific AirCabin-ASL benchmark demonstrate that CMAVFN achieves robust speaker localization in noisy, occluded, and multi-speaker aviation scenarios. This framework offers a practical foundation for speech-driven interaction systems in aircraft cabins, enabling applications such as real-time crew assistance, voice-based medical documentation, and intelligent in-flight health monitoring. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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29 pages, 2830 KB

Open AccessArticle

BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding

by Muhammad Zulkifal Aziz, Xiaojun Yu, Xinran Guo, Xinming He, Binwen Huang and Zeming Fan

Sensors 2025, 25(15), 4657; https://doi.org/10.3390/s25154657 - 27 Jul 2025

Cited by 8 | Viewed by 2192

Abstract

Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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11 pages, 1898 KB

Open AccessCommunication

Simulation Design of an Elliptical Loop-Microstrip Array for Brain Lobe Imaging with an 11.74 Tesla MRI System

by Daniel Hernandez, Taewoo Nam, Eunwoo Lee, Yeji Han, Yeunchul Ryu, Jun-Young Chung and Kyoung-Nam Kim

Sensors 2025, 25(13), 4021; https://doi.org/10.3390/s25134021 - 27 Jun 2025

Cited by 3 | Viewed by 1091

Abstract

Magnetic resonance imaging (MRI) is a powerful medical imaging technique used for acquiring high-resolution anatomical and functional images of the human body. With the development of an 11.74 Tesla (T) human MRI system at our facility, we are designing novel radiofrequency (RF) coils optimized for brain imaging at ultra-high fields. To meet specific absorption rate (SAR) safety limits, this study focuses on localized imaging of individual brain lobes rather than whole-brain array designs. Conventional loop coils, while widely used, offer limited |B₁|-field uniformity at 500 MHz—the Larmor frequency at 11.74 T, which can reduce image quality. Therefore, it is important to develop antennas and coils for highly uniform fields. As an alternative, we propose an elliptical microstrip design, which combines the compact resonant properties of microstrips with the enhanced field coverage provided by loop geometry. We simulated a three-element elliptical microstrip array and compared its performance with a conventional loop coil. The proposed design demonstrated improved magnetic field uniformity and coverage across targeted brain regions. Preliminary bench-top validation confirmed the feasibility of resonance tuning at 500 MHz, supporting its potential for future high-field MRI applications. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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