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Energy-Efficient and Privacy-Preserving Biomedical Signal and Image Processing

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 574

Special Issue Editor


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Guest Editor
Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan
Interests: Internet of Things; biomedicine; artificial intelligence; digital image processing; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy-efficient and privacy-preserving biomedical signal and image processing is an emerging field that combines advancements in medical technology with sustainable and secure practices. With the increasing demand for wearable health devices, medical imaging systems, and remote health monitoring, it is critical to develop solutions that not only minimize energy consumption but also ensure the privacy of sensitive patient data. By leveraging techniques such as signal compression, data encryption, and AI-driven models, this field aims to provide accurate medical analysis while safeguarding privacy and reducing the environmental impact of healthcare technologies.

Dr. Shuo-Tsung Chen
Guest Editor

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Keywords

  • energy efficiency
  • privacy preservation
  • biomedical signal processing
  • image compression
  • data encryption
  • wearable health devices
  • secure medical imaging
  • remote health monitoring
  • AI in healthcare
  • sustainable healthcare technologies

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Published Papers (1 paper)

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Research

17 pages, 3035 KiB  
Article
Data-Driven Image-Based Protocol for Brain PET Image Harmonization
by Eva Štokelj, Urban Simončič and for the Alzheimer’s Disease Neuroimaging Initiative
Sensors 2025, 25(13), 4230; https://doi.org/10.3390/s25134230 - 7 Jul 2025
Viewed by 420
Abstract
Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain [...] Read more.
Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain images to estimate scanner-specific Gaussian smoothing filters, optimizing parameters via the structural similarity index (SSIM). Validation was performed using images from cognitively normal individuals and Alzheimer’s disease patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results demonstrated robust harmonization at moderate target resolutions (8 and 10 mm FWHM), with filter estimates consistently within 1.2 mm of phantom-derived ground truths. However, at higher resolutions (6 mm FWHM), discrepancies reached up to 3 mm, reflecting reduced accuracy. These deviations were particularly evident for high-resolution scanners like HRRT, likely due to elevated noise levels and smaller sample sizes. The presented harmonization method effectively reduces inter-scanner variability in retrospective FDG-PET studies, especially valuable when phantom scans are unavailable. Nonetheless, the current limitations at finer resolutions underline the necessity for methodological refinements to meet the demands of evolving high-resolution PET imaging technologies. Full article
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