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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.

Open Access
Online
ISSN
2364-5504
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Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting

Redwan Abdo A. Mohammed
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  • Hochschule Wismar, Fakultät für Ingenieurwissenschaften, Philipp-Müller-Straße, Wismar, Germany
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/ Daniel Schäle
  • Hochschule Wismar, Fakultät für Ingenieurwissenschaften, Philipp-Müller-Straße, Wismar, Germany
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/ Christoph Hornberger
  • Hochschule Wismar, Fakultät für Ingenieurwissenschaften, Philipp-Müller-Straße, Wismar, Germany
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/ Steffen Emmert
  • Clinic for Dermatology and Venereology, University Medical Center Rostock, Strempelstrasse, Rostock, Germany
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Published Online: 2018-09-22 | DOI: https://doi.org/10.1515/cdbme-2018-0100

Abstract

The purpose of this study is to develop a method to discriminate spectral signatures in wound tissue. We have collected a training set of the intensity of the remitted light for different types of wound tissue from different patients using a TIVITA™ tissue camera. We used a neural network technique (self-organizing map) to group areas with the same spectral properties together. The results of this work indicates that neural network models are capable of finding clusters of closely related hyperspectral signatures in wound tissue, and thus can be used as a powerful tool to reach the anticipated classification. Moreover, we used a least square method to fit literature spectra (i.e. oxygenated haemoglobin (O2Hb), deoxygenated haemoglobin (HHb), water and fat) to the learned spectral classes. This procedure enables us to label each spectral class with the corresponding absorbance properties for the different absorbance of interest (i.e. O2Hb, HHb, water and fat). The calculated parameters of a testing set were consistent with the expected behaviour and show a good agreement with the results of a second algorithm which is used in the TIVITA™ tissue camera.

Keywords: hyperspectral imaging; spectral signatures; hyperspectral image classification; spectral unmixing

About the article

Published Online: 2018-09-22

Published in Print: 2018-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 4, Issue 1, Pages 419–422, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2018-0100.

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