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Automatic segmentation of lesions for the computer-assisted detection in fluorescence urology

 
: Kage, A.; Legal, W.; Kelm, P.; Simon, J.; Bergen, T.; Münzenmayer, C.; Benz, M.

:

Ginneken, B. van ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2012. Computer-aided diagnosis. Vol.1 : 7 - 9 February 2012, San Diego, California, USA
Bellingham, WA: SPIE, 2012 (Proceedings of SPIE 8315)
ISBN: 978-0-8194-8964-7
ISSN: 1605-7422
Paper 83151O
Conference "Computer-Aided Diagnosis" <2012, San Diego/Calif.>
Conference "Medical Imaging" <2012, San Diego/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer IIS ()

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 le ads 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.

: http://publica.fraunhofer.de/dokumente/N-263996.html