@Inbook{PanagiotaSpyridonos2006,
author="Panagiota Spyridonos
and Fernando Vilari{\~n}o
and Jordi Vitria
and Fernando Azpiroz
and Petia Radeva",
editor="R. Larsen, M. Nielsen",
chapter="Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions",
title="9th International Conference on Medical Image Computing and Computer--Assisted Intervention",
year="2006",
publisher="Springer Verlag",
address="Berlin Heidelberg",
volume="4191",
pages="161--168",
abstract="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.",
optnote="MV;OR;MILAB;SIAI",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=725), last updated on Fri, 11 Nov 2016 12:39:46 +0100",
doi="10.1007/11866763_20",
file=":http://refbase.cvc.uab.es/files/SVV2006.pdf:PDF"
}