title: Edge density based automatic detection of inflammation in colonoscopy videos.
abstract: Colon cancer is one of the deadliest diseases where early detection can prolong life and can increase the survival rates. The early stage disease is typically associated with polyps and mucosa inflammation. The often used diagnostic tools rely on high quality videos obtained from colonoscopy or capsule endoscope. The state-of-the-art image processing techniques of video analysis for automatic detection of anomalies use statistical and neural network methods. In this paper, we investigated a simple alternative model-based approach using texture analysis. The method can easily be implemented in parallel processing mode for real-time applications. A characteristic texture of inflamed tissue is used to distinguish between inflammatory and healthy tissues, where an appropriate filter kernel was proposed and implemented to efficiently detect this specific texture. The basic method is further improved to eliminate the effect of blood vessels present in the lower part of the descending colon. Both approaches of the proposed method were described in detail and tested in two different computer experiments. Our results show that the inflammatory region can be detected in real-time with an accuracy of over 84%. Furthermore, the experimental study showed that it is possible to detect certain segments of video frames containing inflammations with the detection accuracy above 90%.
authors.affiliation: Faculty of Electrical Engineering, University of Banja Luka, Patre 5, 78000 Banja Luka, Bosnia and Herzegovina. Electronic address: igor.sevo@etfbl.net.
authors.forename: I
authors.initials: I
authors.lastname: Ševo
authors.affiliation: Faculty of Electrical Engineering, University of Banja Luka, Patre 5, 78000 Banja Luka, Bosnia and Herzegovina; School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11000 Belgrade, Serbia. Electronic address: aleksej@etfbl.net.
authors.forename: A
authors.initials: A
authors.lastname: Avramović
authors.affiliation: The Intervention Centre, Oslo University Hospital, N-0027 Oslo, Norway; Department of Electronics and Telecommunications, Norwegian University of Science and Technology, N-7491 Trondheim, Norway. Electronic address: ilangko.balasingham@iet.ntnu.no.
authors.forename: I
authors.initials: I
authors.lastname: Balasingham
authors.affiliation: The Intervention Centre, Oslo University Hospital, N-0027 Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway. Electronic address: oelle@ous-hf.no.
authors.forename: O J
authors.initials: OJ
authors.lastname: Elle
authors.affiliation: The Intervention Centre, Oslo University Hospital, N-0027 Oslo, Norway. Electronic address: jacob.bergsland@ous-hf.no.
authors.forename: J
authors.initials: J
authors.lastname: Bergsland
authors.affiliation: Rikshospitalet University Hospital, 0027 Oslo, Norway. Electronic address: lars.aabakken@medisin.uio.no.
authors.forename: L
authors.initials: L
authors.lastname: Aabakken
mesh_terms: D000465:Algorithms; D001331:Automation; D003113:Colonoscopy / Q000379:methods*; D006801:Humans; D007249:Inflammation / Q000175:diagnosis*; D008962:Models, Theoretical
keywords: Automatic detection; Colonoscopy; Inflammation; Texture