Detecting abnormal patterns in WCE images

This paper presents a methodology for detecting abnormal patterns in wireless capsule endoscopy (WCE) images. In particular, an average of 50,000 images are obtained during an WCE exam. Usually, these images are reviewed in a form of a video at speeds between 5 to 40 image-frames/sec. The time spent by a physician reading the results of WCE images varies between 45 to 180 minutes. This presents a major problem which is the reading process that consumes a significant amount of time and the results take several days before they become available since the physician has to find the time to study each video uninterrupted for up to 3 hours. The methodology presented here is based on the automatic detection of abnormal WCE patterns in an effort for reducing the reading time of the WCE images and the cost of the procedure as well. The methodology consists of a synergistic integration of image processing, analysis and recognition techniques for achieving the automatic detection of the WCE abnormal patterns.

[1]  Nikolaos G. Bourbakis,et al.  Segmentation of color images with highlights and shadows using fuzzy reasoning , 1995, Electronic Imaging.

[2]  Nikolaos G. Bourbakis,et al.  Object recognition using local-global graphs , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[3]  J. Barkin,et al.  Wireless capsule endoscopy , 2004 .

[4]  Nikolaos G. Bourbakis,et al.  Emulating human visual perception for measuring difference in images using an SPN graph approach , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[5]  D. Kavraki,et al.  Recording changes in biological in vivo cells by using the L-G methodology , 1999, Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446).

[6]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..