title: Knowledge-based femur detection in conventional radiographs of the pelvis.
abstract: In this paper we present a knowledge-based femur detection algorithm. The algorithm uses femur corpus constraints, Canny edge detection and Hough lines. For optimal femur template placement in the local area we use cross-correlation. The segmentation itself is done with an optimized active shape modeling technique. Using the knowledge-based technique we have located 95% of the femur shapes of N=117 X-rays. From those 83% of the target femur shapes have been segmented successfully (point-to-point error: approximately 14 pixels, point-to-boundary error = approximately 9 pixels).
authors: Institute for Applied Systems Research and Development, Medical Informatics and Technology, University for Health Sciences, A-6060 Hall in Tyrol, Austria. roland.pilgram@umit.at <roland.pilgram@umit.at>
authors: Roland
authors: R
authors: Pilgram
authors: Claudia
authors: C
authors: Walch
authors: Michael
authors: M
authors: Blauth
authors: Werner
authors: W
authors: Jaschke
authors: Rainer
authors: R
authors: Schubert
authors: Volker
authors: V
authors: Kuhn
mesh_terms: D000465:Algorithms; D005269:Femur / Q000000981:diagnostic imaging*; D006801:Humans; D015233:Models, Statistical; D010388:Pelvis / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*