The nature of statistical learning theory
The nature of statistical learning theory
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Automatic classification of digestive organs in wireless capsule endoscopy videos
Proceedings of the 2007 ACM symposium on Applied computing
Computers in Biology and Medicine
Towards an Interpretation of Intestinal Motility Using Capsule Endoscopy Image Sequences
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Sudden Changes Detection in WCE Video
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Contraction detection in small bowel from an image sequence of wireless capsule endoscopy
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
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Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of contractions and to analyze the intestine motility. Feature extraction is essential 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 contraction 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. Features extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belonging 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.