Fu, Biying ; Damer, Naser (2023)
Biometric Recognition in 3D Medical Images: A Survey.
In: IEEE Access, 11
doi: 10.1109/ACCESS.2023.3331118
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
This work explores the potential applications of biometric recognition in 3D medical imaging data. We investigate various 3D imaging techniques commonly used in the medical domain, including 3D ultrasound imaging, magnetic resonance imaging (MRI), computer tomography (CT) scans, and 3D near-infrared (NIR) imaging. For each technique, we provide an overview of its working principle and discuss the advantages of integrating biometrics into 3D medical imaging data. Major advantage of using biometrics in this context is motivated by the research that using biometrics could not only increase data security but, more importantly, decrease the mix-up errors in patient’s medical data and thus improving patient safety and patient care. Our analysis uncovers certain weaknesses in current algorithms and limitations in existing research. Possible reasons include insufficient data availability, the under-utilization of deep-learning-based approaches to enhance accuracy and performance, and the absence of standardized benchmarking databases to support research. Our survey frames existing works and lead to practical recommendations and motivates efforts to improve the current state of research. Beyond exploring the utilization of biometrics in 3D medical imaging data, our study touches on further potential interactions between them, such as extracting health information from biometric captures. This work is thus the first work to survey and present an overview on works proposing the use of medical images for biometric recognition.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Fu, Biying ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | Biometric Recognition in 3D Medical Images: A Survey |
| Sprache: | Englisch |
| Publikationsjahr: | 8 November 2023 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Access |
| Jahrgang/Volume einer Zeitschrift: | 11 |
| DOI: | 10.1109/ACCESS.2023.3331118 |
| Kurzbeschreibung (Abstract): | This work explores the potential applications of biometric recognition in 3D medical imaging data. We investigate various 3D imaging techniques commonly used in the medical domain, including 3D ultrasound imaging, magnetic resonance imaging (MRI), computer tomography (CT) scans, and 3D near-infrared (NIR) imaging. For each technique, we provide an overview of its working principle and discuss the advantages of integrating biometrics into 3D medical imaging data. Major advantage of using biometrics in this context is motivated by the research that using biometrics could not only increase data security but, more importantly, decrease the mix-up errors in patient’s medical data and thus improving patient safety and patient care. Our analysis uncovers certain weaknesses in current algorithms and limitations in existing research. Possible reasons include insufficient data availability, the under-utilization of deep-learning-based approaches to enhance accuracy and performance, and the absence of standardized benchmarking databases to support research. Our survey frames existing works and lead to practical recommendations and motivates efforts to improve the current state of research. Beyond exploring the utilization of biometrics in 3D medical imaging data, our study touches on further potential interactions between them, such as extracting health information from biometric captures. This work is thus the first work to survey and present an overview on works proposing the use of medical images for biometric recognition. |
| Freie Schlagworte: | Biometrics, Machine learning, 3D Medical data, Medical imaging, Health care |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 28 Nov 2023 13:11 |
| Letzte Änderung: | 29 Jan 2024 13:43 |
| PPN: | 515115738 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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