Laboratory for
Climatology and Remote Sensing

Abstract:

Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

Abstract:

The aim of this study was to proof applicability of hyperspectral imaging for the analysis and classification of human mucosal surfaces in vivo. The larynx as a prototypical anatomically well-defined surgical test area was analyzed by microlaryngoscopy with a polychromatic lightsource and a synchronous triggered monochromatic CCD-camera. Image stacks (5 benign, 7 malignant tumors) were analyzed by established software (principal component analysis PCA, hyperspectral classification, spectral profiles). Hyperspectral image datacubes were analyzed and classified by conventional software. In PCA, images at 590–680 nm loaded most onto the first PC which typically contained 95% of the total information. Hyperspectral classification clustered the data highlighting altered mucosa. The spectral profiles clearly differed between the different groups. Hyperspectral imaging can be applied to mucosal surfaces. This approach opens the way to analyze spectral characteristics of histologically different lesions in order to build up a spectral library and to allow non-touch optical biopsy.