A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Adaptive Control of Video Display for Diagnostic Assistance by Analysis of Capsule Endoscopic Images
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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
Linear radial patterns characterization for automatic detection of tonic intestinal contractions
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Identification of intestinal motility events of capsule endoscopy video analysis
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Anisotropic feature extraction from endoluminal images for detection of intestinal contractions
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Hi-index | 0.00 |
Human intestinal motility is presented by the propagation of peristaltic waves with their frequencies gradually decreasing along the length of the small bowel. This paper describes a heuristic method, which can be used towards interpreting intestinal motility through recognizing their frequency characteristics from capsule endoscopy image sequences. First, image features that reflect peristaltic activities are extracted to build a functional signal. Then, a Multi-Resolution Analysis technique in the wavelet domain is used to decompose the functional signal taking into account the non-stationary nature of intestinal motility. For peristaltic waveform recognition, the method relies on the principle of peak detections from the decomposed signals. Each waveform is detected when it exceeds a baseline level. The frequency characteristics are interpreted through analysis of the waveform appearance and their velocity propagation. Three healthy sequences were tested in experiments. The estimated trends of the peristaltic wave propagation from the experimental results show a frequency gradient, which follows the well-recognized characteristics of intestinal motility propagation. Therefore, this study is the first demonstration of a detailed interpretation of intestinal motility, and we suggest that further research focuses on intestinal motility dysfunctions.