Multi-spectral video endoscopy system for the detection of cancerous tissue

Multi-spectral video endoscopy provides considerable potential for early stage cancer detection. Previous multi-spectral image acquisition systems were of limited use for endoscopy due to (i) the necessary spatial scanning of push-broom approaches or (ii) the impractical long switching times of liquid crystal tunable filters. Recent technological advances in the field of tuneable filters, in particular fast acousto-optical tunable filters (AOTF), make switching times below 1 ms feasible. Thus, AOTFs represent a suitable technology for the acquisition of hyper-spectral image and multi-spectral video data with excellent spatial and temporal resolution. In this paper, we propose a hyper-spectral imaging endoscope using a fast AOTF synchronized with a highly sensitive EMCCD camera for the detection of cancerous tissue. The setup demonstrates that the acquisition of hyper-spectral image and multi-spectral video data is feasible and enables the augmentation of endoscopic videos with overlays indicating cancerous tissue regions. Using hyper-spectral measurements from biopsies acquired with the setup in a clinical environment it is shown that the spectral characteristic of cancerous regions is tissue dependent. Even a sophisticated classifier such as a Support Vector Machines (SVM) or a Mixture of Gaussian Classifier (MOGC) cannot generalize the discriminative information if the training set contains measurements from different tissue types (e.g. larynx vs. parotid). In contrast, a training data selection scheme that chooses similar training sets for a given test set achieves a better prediction accuracy using an approach based on a Quadratic Discriminant Classifier (QDC) with the important advantage of improved robustness and less liability to overtraining. Combined with an image registration removing motion-based acquisition artefacts, the spectral information allows the augmentation of the video stream with overlays indicating cancerous tissue regions. Keywords: Multi-spectral video; Multi-spectral image; Acousto-optical tunable filter; Endoscopy; Multivariate pattern recognition; Training data selection