Martin R, Thies B, Gerstner AO. Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx.
Int J Health Geogr 2012;
11:21. [PMID:
22720905 PMCID:
PMC3787854 DOI:
10.1186/1476-072x-11-21]
[Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Accepted: 05/29/2012] [Indexed: 11/10/2022] Open
Abstract
Background
In the field of earth observation, hyperspectral detector systems allow precise target detections of surface components from remote sensing platforms. This enables specific land covers to be identified without the need to physically travel to the areas examined. In the medical field, efforts are underway to develop optical technologies that detect altering tissue surfaces without the necessity to perform an excisional biopsy. With the establishment of expedient classification procedures, hyperspectral imaging may provide a non-invasive diagnostic method that allows determination of pathological tissue with high reliability. In this study, we examined the performance of a hyperspectral hybrid method classification for the automatic detection of altered mucosa of the human larynx.
Materials and methods
Hyperspectral Imaging was performed in vivo and 30 bands from 390 to 680 nm for 5 cases of laryngeal disorders (2x hemorrhagic polyp, 3x leukoplakia) were obtained. Image stacks were processed with unsupervised clustering (linear spectral unmixing), spectral signatures were extracted from unlabeled cluster maps and subsequently applied as end-members for supervised classification (spectral angle mapper) of further medical cases with identical diagnosis.
Results
Linear spectral unmixing clearly highlighted altered mucosa as single spectral clusters in all cases. Matching classes were identified, and extracted spectral signatures could readily be applied for supervised classifications. Automatic target detection performed well, as the considered classes showed notable correspondence with pathological tissue locations.
Conclusions
Using hyperspectral classification procedures derived from remote sensing applications for diagnostic purposes can create concrete benefits for the medical field. The approach shows that it would be rewarding to collect spectral signatures from histologically different lesions of laryngeal disorders in order to build up a spectral library and to prospectively allow non-invasive optical biopsies.
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