Abstract
Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operator’s ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.
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Mewes, P.W., Neumann, D., Licegevic, O., Simon, J., Juloski, A.L., Angelopoulou, E. (2011). Automatic Region-of-Interest Segmentation and Pathology Detection in Magnetically Guided Capsule Endoscopy. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_18
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DOI: https://doi.org/10.1007/978-3-642-23626-6_18
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