MEDICAL IMAGE PROCESSING- BONE IMAGING

Abstract
The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, the reliability is used to further correct the normal direction. The proposed algorithm starts with initial seed search in the input image. Initial seeds can be obtained by thresholding or manual selection. Starting from an initial seed, to find an edge point near the seed or to correct the location of the seed we calculate the normal direction at the seed and construct a 1-D signal that consists of the intensity of the pixels along the normal direction centered at the seed. We then use an edge filter to find the edge point in the 1-D signal. The deviation of the edge point in the 1-D signal from the center is used to obtain the location of the edge point in the 2-D image. From the edge point, we predict the next edge point along the edge. The predicted point is taken as a seed and the process is repeated until no new edge point can be predicted. We then go to the next initial seed, and the entire process is repeated until all of the initial seeds are used up. Finally, the edge map is postprocessed to produce a better edge map of the bone. Normal-direction estimation is based on the observation that the intensity of pixels near an edge changes more rapidly along the normal direction than along the tangent direction.  2-D symmetrical Gaussian is widely used to obtain the normal direction. A predictor-corrector scheme is used to improve
The normal direction estimation. The optimal scale at each point is obtained while estimating the normal direction; this scale is then used in a simple edge detector. The reliability may become more stable from slice to slice when we use 3-D information to estimate the normal direction of bone edges. Thus, it is expected that the 3-D extension will shorten the segmentation time and improve the performance.

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