Contraction Detection in Small Bowel from an Image Sequence of Wireless Capsule Endoscopy

This paper describes a method for automatic detection of contractions in the small bowel through analyzing Wireless Capsule Endoscopic images. Based on the characteristics of contraction images, a coherent procedure that includes analyzes of the temporal and spatial features is proposed. For temporal features, the image sequence is examined to detect candidate contractions through the changing number of edges and an evaluation of similarities between the frames of each possible contraction to eliminate cases of low probability. For spatial features, descriptions of the directions at the edge pixels are used to determine contractions utilizing a classification method. The experimental results show the effectiveness of our method that can detect a total of 83% of cases. Thus, this is a feasible method for developing tools to assist in diagnostic procedures in the small bowel.

[1]  Hayit Greenspan,et al.  A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing , 2002, ECCV.

[2]  M. Hansen,et al.  Small intestinal manometry. , 2002, Physiological research.

[3]  Lasse Riis Østergaard,et al.  Active Surface Approach for Extraction of the Human Cerebral Cortex from MRI , 2006, MICCAI.

[4]  G. Iddan,et al.  Wireless capsule endoscopy , 2003, Gut.

[5]  Fernando Vilariño,et al.  Identification of Intestinal Motility Events of Capsule Endoscopy Video Analysis , 2005, ACIVS.

[6]  José Francisco Martínez-Trinidad,et al.  Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007, Proceedings , 2008, CIARP.

[7]  Fernando Vilariño,et al.  Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions , 2006, CIARP.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[10]  P. Swain,et al.  Role of video endoscopy in managing small bowel disease , 2004, Gut.

[11]  Hansen Mb,et al.  Small intestinal manometry. , 2002 .

[12]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Fernando Vilariño,et al.  Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions , 2006, MICCAI.