Knowledge-based femur detection in conventional radiographs of the pelvis.
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).
Pilgram
Roland
R
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>
Walch
Claudia
C
Blauth
Michael
M
Jaschke
Werner
W
Schubert
Rainer
R
Kuhn
Volker
V
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*