Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging

• Published in: Diagnostics (Basel) 2023-01, Vol.13 (2), p.195
• Main Authors: Pathak, Priya, Chalopin, Claire, Zick, Laura, Köhler, Hannes, Pfahl, Annekatrin, Rayes, Nada, Gockel, Ines, Neumuth, Thomas, Melzer, Andreas, Jansen-Winkeln, Boris, Maktabi, Marianne
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Brain cancer; Classification; Data smoothing; Datasets; Deep learning; Entropy; gastrointestinal; Gastrointestinal surgery; hyperspectral data; Machine learning; Neural networks; Probability distribution; Similarity measures; spectral angle mapper; Surgical apparatus & instruments; Thyroid gland; thyroidectomy; tissue discrimination; Tissues
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13020195

Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification. In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials (gauze, instruments), collected from HS images of 100 patients during surgeries. The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%. In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images.