Combining Gaussian Markov random fields with the discrete-wavelet transform for endoscopic image classification

In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed to the wavelet domain using the pyramidal discrete wavelet transform. Then, Gaussian Markov random fields are used to extract features from the resulting wavelet coefficients. Finally, these features are used for a classification using the k-NN classifier and the Bayes classifier.