In this paper, a 3D model-based approach is proposed for tracking 3D motion (positions and orientations) of the knee from sequences of 2D radiographs. Conventional methods using external skin markers or body model do not accurately reflect motion of the underlying bone. In contrast, our method is to use sequences of radiographs for direct visualization of bone motion during activities. A 3D texture-mapped volume rendering is used to simulate a radiograph image, a 2D projected image of the 3D model data. A Quadtree-based normalized correlation algorithm is employed to measure similarity between the projected 2D model image and the pre-processed radiograph image. An optimization routine iterates the six motion parameters until the optimal similarity is obtained. This method has been evaluated using test data collected from an anatomically accurate radiographic knee phantom, specifically femur part of the phantom. Further testing is underway using in-vivo radiograph image sequences of a canine hindlime during treadmill walking.
Color is one of the most widely used features for image similarity retrieval. Most of the existing image similarity retrieval schemes employ either global or local color histogramming. In this paper, we explore the use of localized dominant hue and saturation values for color-based image similarity retrieval. This scheme results in a relatively compact representation of color images for similarity retrieval. Experimental results comparing the proposed representation with global and local color histogramming are presented to show the efficacy of the suggested scheme.
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