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On-the-fly Detection of Images with Gastritis Aspects in Magnetically-Guided Capsule Endoscopy
 

Summary: On-the-fly Detection of Images with Gastritis Aspects in
Magnetically-Guided Capsule Endoscopy
P. W. Mewes12, D. Neumann2, A. Lj. Juloski2, E. Angelopoulou1, J. Hornegger1
1Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany
2Siemens Healthcare Sector, Erlangen, Germany
ABSTRACT
Capsule Endoscopy (CE) was introduced in 2000 and has since become an established diagnostic procedure for
the small bowel, colon and esophagus. For the CE examination the patient swallows the capsule, which then
travels through the gastrointestinal tract under the influence of the peristaltic movements. CE is not indicated
for stomach examination, as the capsule movements can not be controlled from the outside and the entire surface
of the stomach can not be reliably covered. Magnetically-guided capsule endoscopy (MGCE) was introduced in
2010. For the MGCE procedure the stomach is filled with water and the capsule is navigated from the outside
using an external magnetic field. During the examination the operator can control the motion of the capsule
in order to obtain a sufficient number of stomach-surface images with diagnostic value. The quality of the
examination depends on the skill of the operator and his ability to detect aspects of interest in real time. We
present a novel computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis pathologies in
the stomach during the examination. Our algorithm is based on pre-processing methods and feature vectors that
are suitably chosen for the challenges of the MGCE imaging (suspended particles, bubbles, lighting). An image
is classified using an ada-boost trained classifier. For the classifier training, a number of possible features were
investigated. Statistical evaluation was conducted to identify relevant features with discriminative potential.

  

Source: Angelopoulou, Elli - Department of Computer Science, Friedrich Alexander University Erlangen Nürnberg

 

Collections: Computer Technologies and Information Sciences