Next Article in Journal
Spirocyclic Motifs in Natural Products
Next Article in Special Issue
Advances in Near-Infrared Spectroscopy and Related Computational Methods
Previous Article in Journal
Isolation and Characterization of Melanoidins from Dulce de Leche, A Confectionary Dairy Product
Previous Article in Special Issue
Comparison of Multivariate Regression Models Based on Water- and Carbohydrate-Related Spectral Regions in the Near-Infrared for Aqueous Solutions of Glucose
Open AccessArticle

Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine

1
Klinik für Plastische und Handchirurgie und Brandverletztenzentrum, BG-Klinikum Bergmannstrost, D-06002 Halle (Saale), Germany
2
Institute of Applied Bioscience and Process Management, University of Applied Science Anhalt, D-06366 Köthen (Anhalt), Germany
3
Diaspective Vision GmbH, D-18233 Am Salzhaff, Germany
4
Clinic of Plastic, Hand and Aesthetic Surgery, Medical Center Dessau, University of Applied Science Anhalt, D-06847 Dessau, Germany
5
Clinic of Dermatology, Immunology and Allergology, Medical Center Dessau, Medical University Brandenburg “Theodor Fontane“ Medical Center Dessau, D-06847 Dessau, Germany
6
Klinik für Plastische, Wiederherstellende und Handchirurgie, Zentrum für Schwerbrandverletzte, Klinikum Nürnberg, D-90471 Nürnberg, Germany
7
Klinik und Poliklinik für Hautkrankheiten, Universitätsmedizin Greifswald, D-17475 Greifswald, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Christian Huck and Krzysztof B. Bec
Molecules 2019, 24(22), 4164; https://doi.org/10.3390/molecules24224164
Received: 12 September 2019 / Revised: 30 October 2019 / Accepted: 14 November 2019 / Published: 17 November 2019
Background: Hyperspectral Imaging (HSI) has a strong potential to be established as a new contact-free measuring method in medicine. Hyperspectral cameras and data processing have to fulfill requirements concerning practicability and validity to be integrated in clinical routine processes. Methods: Calculating physiological parameters which are of significant clinical value from recorded remission spectra is a complex challenge. We present a data processing method for HSI remission spectra based on a five-layer model of perfused tissue that generates perfusion parameters for every layer and presents them as depth profiles. The modeling of the radiation transport and the solution of the inverse problem are based on familiar approximations, but use partially heuristic methods for efficiency and to fulfill practical clinical requirements. Results: The parameter determination process is consistent, as the measured spectrum is practically completely reproducible by the modeling sequence; in other words, the whole spectral information is transformed into model parameters which are easily accessible for physiological interpretation. The method is flexible enough to be applicable on a wide spectrum of skin and wounds. Examples of advanced procedures utilizing extended perfusion representation in clinical application areas (flap control, burn diagnosis) are presented. View Full-Text
Keywords: hyperspectral image processing; perfusion measurements; clinical classifications hyperspectral image processing; perfusion measurements; clinical classifications
Show Figures

Figure 1

MDPI and ACS Style

Marotz, J.; Kulcke, A.; Siemers, F.; Cruz, D.; Aljowder, A.; Promny, D.; Daeschlein, G.; Wild, T. Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine. Molecules 2019, 24, 4164.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop