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Title of Journal: Int J CARS

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Abbravation: International Journal of Computer Assisted Radiology and Surgery

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Springer-Verlag

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1861-6429

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Automatic feature generation in endoscopic images

Authors: Ulrich Klank Nicolas Padoy Hubertus Feussner Nassir Navab
Publish Date: 2008/06/10
Volume: 3, Issue: 3-4, Pages: 331-339
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Abstract

Fiber optic endoscopy is essential for minimally invasive surgery but endoscopic images are very challenging for computer vision algorithms since they contain many effects like tissue deformations specular reflections smoke variable illumination and field of view We developed a method to extract features from endoscopic images usable for scene analysis and classification These features could be used with data from other sensors for workflow analysis and recognitionEvolutionary reinforcement learning that automatically computes good features making it possible to classify endoscopic images into their respective surgical phases It is especially designed to abstract the relevant information from the highly noisy images automaticallyAutomatic feature extraction was used to classify images from endoscopic cholecystectomies into their respective surgical phases These automatically computed features perform better than some classical features from computer vision The automated feature extraction process enables reasonable classification rates for complex and difficult images where no good features are known

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