Local polynomial approximation for unsupervised segmentation of endoscopic images

Abstract

In this paper we present a novel technique for unsupervised texture segmentation of wireless capsule endoscopic images of the human gastrointestinal tract. Our approach integrates local polynomial approximation algorithm with the well-founded methods of color texture analysis and clustering (k-means) leading to a robust segmentation procedure which produces fine-grained segments well matched to the image contents. © 2010 Springer-Verlag Berlin Heidelberg.