q Multispectral Endoscopy to Identify Precancerous Lesions in Gastric Mucosa

Precancerous lesions are in many situations not visible during white light gastroendoscopy. Different approaches have been proposed based on light tissue interaction in order to improve the visualization by creating false color images. However, these systems are limited to few wavelengths. In this paper, we propose a multispectral gastroendoscopic system and a methodology to identify precancerous lesions. The multispectral images collected during gastroendoscopy are used to compute statistical features from their spectrum. Pooled variance t-test is used to rank the features in order to train 3 classifiers with different number of features. The 3 classifiers are Neural Networks using Generalized Relevance Learning Vector Quantization (GRLVQ), SVM with a Gaussian kernel and K-nn. The performance is compared based on their ability to identify precancerous lesions, using as quantitative index the accuracy, specificity and sensitivity. SVM presents the best performance, showing the effectiveness of the method.