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Proceedings Paper

Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology
Author(s): Andreas Kage; Wolfgang Legal; Peter Kelm; Jörg Simon; Tobias Bergen; Christian Münzenmayer; Michaela Benz
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Paper Abstract

Bladder cancer is one of the most common cancers in the western world. The diagnosis in Germany is based on the visual inspection of the bladder. This inspection performed with a cystoscope is a challenging task as some kinds of abnormal tissues do not differ much in their appearance from their surrounding healthy tissue. Fluorescence Cystoscopy has the potential to increase the detection rate. A liquid marker introduced into the bladder in advance of the inspection is concentrated in areas with high metabolism. Thus these areas appear as bright "glowing". Unfortunately, the fluorescence image contains besides the glowing of the suspicious lesions no more further visual information like for example the appearance of the blood vessels. A visual judgment of the lesion as well as a precise treatment has to be done using white light illumination. Thereby, the spatial information of the lesion provided by the fluorescence image has to be guessed by the clinical expert. This leads to a time consuming procedure due to many switches between the modalities and increases the risk of mistreatment. We introduce an automatic approach, which detects and segments any suspicious lesion in the fluorescence image automatically once the image was classified as a fluorescence image. The area of the contour of the detected lesion is transferred to the corresponding white light image and provide the clinical expert the spatial information of the lesion. The advantage of this approach is, that the clinical expert gets the spatial and the visual information of the lesion together in one image. This can save time and decrease the risk of an incomplete removal of a malign lesion.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151O (23 February 2012); doi: 10.1117/12.911366
Show Author Affiliations
Andreas Kage, Fraunhofer-Institut für Integrierte Schaltungen (Germany)
Wolfgang Legal, Univ. Hospital Erlangen (Germany)
Peter Kelm, Klinikum Nürnberg Nord (United States)
Jörg Simon, Ortenau Klinikum Offenburg-Gengenbach (Germany)
Tobias Bergen, Fraunhofer-Institut für Integrierte Schaltungen (Germany)
Christian Münzenmayer, Fraunhofer-Institut für Integrierte Schaltungen (Germany)
Michaela Benz, Fraunhofer-Institut für Integrierte Schaltungen (Germany)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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