Diagnostic system for intestinal motility disfunctions using video capsule endoscopy

Authors:
Santi Seguí;Laura Igual;Fernando Vilariño;Petia Radeva;Carolina Malagelada;Fernando Azpiroz;Jordi Vitrià
Affiliations:
Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain and Departament de Matemàtica Aplicada i Anàlisis, Universitat de Barcelona, Barcelona, Spain;Digestive System Research Unit, University Hospital Vall d'Hebron and Ciberehd and Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain;Digestive System Research Unit, University Hospital Vall d'Hebron and Ciberehd and Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain and Departament de Matemàtica Aplicada i Anàlisis, Universitat de Barcelona, Barcelona, Spain
Venue:
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Year:
2008

Citing 5
Cited 0

Self-organizing maps

Self-organizing maps
The image processing handbook (3rd ed.)

The image processing handbook (3rd ed.)
The Earth Mover's Distance as a Metric for Image Retrieval

International Journal of Computer Vision
Computer-aided tumor detection in endoscopic video using color wavelet features

IEEE Transactions on Information Technology in Biomedicine
An overview of statistical learning theory

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are provided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learning framework in order to obtain the characterization of diverse motility events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.