This paper presents a novel system for image matching in optical endoscopy. The proposed metamatching system approaches the challenge of matching images in a complex scene by incorporating multiple matchers and a decision function. Experiments are presented for Crohn's disease lesion matching in capsule endoscopy with a metamatcher consisting of five independent matchers. We compare the performance of six different types of decision functions. Results show that the F-measure of the metamatching system containing all five matchers is 4%-7% greater than the performance of using the best matcher only, with a maximum F-measure of 0.811. The robustness of the method is validated using simulated data generated by controlled deformations of the image. We also demonstrate how the addition of simulated data to the training set can be used to augment the performance of the metamatcher by up to 10%.

Capsule endoscopy (CE) is aimed at diagnosing disease in areas of the gastrointestinal (GI) tract beyond the reach of conventional endoscopy. Recent work has addressed various methods for reducing the complexity of CE diagnosis and the time needed for analyzing the data. This includes detection of lumen and its contractions, fluids such as blood and intestinal juices, as well as extraneous matter such as food and bubbles. This paper outlines our ongoing work to segment lesions (in particular Crohn's disease) and other abnormalities in CE images. In particular, here we describe the data collection and clinical analysis for our project and preliminary results for segmenting abnormal and extraneous images from a set of 10 CE studies.