Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting
| Autor: | Daniel Schäle, Steffen Emmert, Christoph Hornberger, Redwan Abdo A. Mohammed |
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| Rok vydání: | 2018 |
| Předmět: |
Self-organizing map
Spectral signature hyperspectral imaging business.industry Computer science hyperspectral image classification Biomedical Engineering Hyperspectral imaging Pattern recognition Image (mathematics) 030207 dermatology & venereal diseases 03 medical and health sciences 0302 clinical medicine spectral signatures spectral unmixing 030220 oncology & carcinogenesis Hyperspectral image classification Medicine Artificial intelligence business |
| Zdroj: | Current Directions in Biomedical Engineering, Vol 4, Iss 1, Pp 419-422 (2018) |
| ISSN: | 2364-5504 |
| DOI: | 10.1515/cdbme-2018-0100 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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