Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx

International Journal of Health Geographics

Andreas OH Gerstner¹  Boris Thies²  Ron Martin² 

[1] Department of Otorhinolaryngology/Head and Neck Surgery, University of Bonn, Bonn, 53127, Germany;Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg, Marburg 35037, Germany

关键词: Laryngeal disorders;    Mucosal surfaces;    Tissue characterization;    Endoscopy;    Automatic target detection;    Signature extraction;    Hyperspectral imaging;   

Others  :  811414
DOI  :  10.1186/1476-072X-11-21

 received in 2012-03-19, accepted in 2012-05-29,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Martin et al.; licensee BioMed Central Ltd.

【 图 表 】

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