title: Color treatment in endoscopic image classification using multi-scale local color vector patterns.
abstract: In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable Local Binary Patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier. To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both.
authors.affiliation: St. Elisabeth Hospital, Department for Internal Medicine, Vienna, Austria.
authors.forename: M
authors.initials: M
authors.lastname: Häfner
authors.forename: M
authors.initials: M
authors.lastname: Liedlgruber
authors.forename: A
authors.initials: A
authors.lastname: Uhl
authors.forename: A
authors.initials: A
authors.lastname: Vécsei
authors.forename: F
authors.initials: F
authors.lastname: Wrba
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003111:Colonic Polyps / Q000473:pathology*; D003116:Color; D003124:Colorimetry / Q000379:methods*; D016099:Endoscopy, Gastrointestinal / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity