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A model-based algorithm for long bone segmentation from digital X-Ray images is introduced. The model is based on statistical variations of anatomical data collected after examining diverse bone shapes. This method extends the centroid to boundary distance shape analysis approach. A bone is modeled by two centroid points, one for each of the two epiphysis, and a range of weighted values for the distances between the centroid and the boundary points. To locate the bone in an image, a strong edge belonging to the boundary of the shape should be present within the calculated ranges after edge detection has been performed. The algorithm is scale and rotation invariant. Preliminary results show that the method can identify complete or partial bones, which makes it applicable to detecting common bone fractures.