Laboratory for
Climatology and Remote Sensing

Abstract:

BACKGROUND: Early detection of cancerous lesions is still crucial for a patient's prognosis. Although diagnostic access to the oral cavity and oropharynx is comparably easy, the incidence of resulting disease remains high. This is due to the fact that in many cases, malignity is recognized too late on a purely visual basis. Previously, we discussed the application of hyperspectral imaging for early detection of precancerous and cancerous lesions of the larynx. This time, we evaluate the method in the oral cavity and oropharynx. MATERIALS AND METHODS: In 85 patients scheduled for endoscopy, hyperspectral imaging was performed. We used a rigid 0-degree endoscope, a light-adjustable monochromator, and a hyperspectral camera. For evaluation of the method, 3 patients were chosen exemplarily. Training sites from physiological and cancerous tissues were marked. Hyperspectral data from 1 patient were used to train a classifier, which was then used for automatic detection of precancerous and cancerous lesions in another 2 patients. RESULTS: Intraoperative hyperspectral imaging was performed without any problems. Classification showed sensitivities of 61 and 43%, and a specificity of 100%. CONCLUSION: This proof-of-concept study underscores the high potential of hyperspectral imaging for early recognition of cancer in the mouth and oropharynx. Besides a better prognosis for cancer patients, this approach could lead to higher cost efficiency in the health system.

Abstract:

Hyperspectral imaging (HSI) is a technology with high potential in the field of non-invasive detection of cancer. However, in complex imaging situations like HSI of the larynx with a rigid endoscope, various image interferences can disable a proper classification of cancerous tissue. We identified three main problems: i) misregistration of single images in a HS cube due to patient heartbeat ii) image noise and iii) specular reflections (SR). Consequently, an image pre-processor is developed in the current paper to overcome these image interferences. It encompasses i) image registration ii) noise removal by minimum noise fraction (MNF) transformation and iii) a novel SR detection method. The results reveal that the pre-processor improves classification performance, while the newly developed SR detection method outperforms global thresholding technique hitherto used by 46%. The novel pre-processor will be used for future studies towards the development of an operational scheme for HS-based larynx cancer detection.

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.