%0 Book Section
%T Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions
%A Panagiota Spyridonos
%A Fernando Vilariño
%A Jordi Vitria
%A Fernando Azpiroz
%A Petia Radeva
%E R. Larsen, M. Nielsen
%B 9th International Conference on Medical Image Computing and Computer–Assisted Intervention
%D 2006
%V 4191
%I Springer Verlag
%C Berlin Heidelberg
%F Panagiota Spyridonos2006
%O MV;OR;MILAB;SIAI
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=725), last updated on Fri, 11 Nov 2016 12:39:46 +0100
%X Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
%U http://refbase.cvc.uab.es/files/SVV2006.pdf
%U http://dx.doi.org/10.1007/11866763_20
%P 161–168