title: Can spectral-spatial image segmentation be used to discriminate experimental burn wounds?
abstract: Hyperspectral imaging (HSI) is a noncontact and noninvasive optical modality emerging the field of medical research. The goal of this study was to determine the ability of HSI and image segmentation to discriminate burn wounds in a preclinical porcine model. A heated brass rod was used to introduce burn wounds of graded severity in a pig model and a sequence of hyperspectral data was recorded up to 8-h postinjury. The hyperspectral images were processed by an unsupervised spectral–spatial segmentation algorithm. Segmentation was validated using results from histology. The proposed algorithm was compared to K-means segmentation and was found superior. The obtained segmentation maps revealed separated zones within the burn sites, indicating a variation in burn severity. The suggested image-processing scheme allowed mapping dynamic changes of spectral properties within the burn wounds over time. The results of this study indicate that unsupervised spectral–spatial segmentation applied on hyperspectral images can discriminate burn injuries of varying severity.
authors.affiliation: Norwegian University of Science and Technology, Faculty of Information Technology, Department of Electronics and Telecommunications, Mathematics and Electrical Engineering, NTNU, Trondheim 7491, Norway.
authors.forename: Lukasz A
authors.initials: LA
authors.lastname: Paluchowski
authors.affiliation: Trondheim University Hospital, St. Olavs Hospital, Department of Plastic Surgery, Trondheim 7030, Norway.
authors.forename: Håvard B
authors.initials: HB
authors.lastname: Nordgaard
authors.affiliation: Norwegian University of Science and Technology, Faculty of Information Technology, Department of Electronics and Telecommunications, Mathematics and Electrical Engineering, NTNU, Trondheim 7491, Norway.
authors.forename: Asgeir
authors.initials: A
authors.lastname: Bjorgan
authors.affiliation: Trondheim University Hospital, St. Olavs Hospital, Department of Pathology and Medical Genetics, Trondheim 7030, NorwaydNorwegian University of Science and Technology, Faculty of Medicine, NTNU, Department of Laboratory Medicine, Children's and Women's He.
authors.forename: Håkon
authors.initials: H
authors.lastname: Hov
authors.affiliation: Trondheim University Hospital, St. Olavs Hospital, Department of Pathology and Medical Genetics, Trondheim 7030, Norway.
authors.forename: Sissel M
authors.initials: SM
authors.lastname: Berget
authors.affiliation: Norwegian University of Science and Technology, Faculty of Information Technology, Department of Electronics and Telecommunications, Mathematics and Electrical Engineering, NTNU, Trondheim 7491, Norway.
authors.forename: Lise L
authors.initials: LL
authors.lastname: Randeberg
mesh_terms: D000465:Algorithms; D000818:Animals; D002056:Burns / Q000000981:diagnostic imaging*; D007091:Image Processing, Computer-Assisted / Q000592:standards*; D015203:Reproducibility of Results; D013057:Spectrum Analysis; D013552:Swine