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: ∼ 14 pixels, point-to-boundary error = ∼ 9 pixels).
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