TY  - CHAP
AU  - Panagiota Spyridonos
AU  - Fernando Vilariño
AU  - Jordi Vitria
AU  - Fernando Azpiroz
AU  - Petia Radeva
A2  - MICCAI06
ED  - R. Larsen, M. Nielsen
PY  - 2006//
TI  - Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions
T2  - LNCS
BT  - 9th International Conference on Medical Image Computing and Computer–Assisted Intervention
SP  - 161–168
VL  - 4191
PB  - Springer Verlag
CY  - Berlin Heidelberg
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/SVV2006.pdf
UR  - http://dx.doi.org/10.1007/11866763_20
N1  - MV;OR;MILAB;SIAI
ID  - Panagiota Spyridonos2006
ER  -