Computer-aided classification of zoom-endoscopical images using Fourier filters.

This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two- and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.

Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors.

Feature extraction techniques based on selection of highly discriminant Fourier filters have been developed for an automated classification of magnifying endoscope images with respect to pit patterns of colon lesions. These are applied to duodenal imagery for diagnosis of celiac disease. Features are extracted from the Fourier domain by selecting the most discriminant features using an evolutionary algorithm. Subsequent classification is performed with various standard algorithms (KNN, SVM, Bayes classifier) and combination of several Fourier filters and classifiers which is called multiclassifier. The obtained results are promising, due to a high specificity for the detection of mucosal damage typical of untreated celiac disease.