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In this paper the authors consider the problems encountered when applying snake models to detect the contours of the carpal bones in 3-D MR images of the wrist. In order to improve the performance of the original snake model introduced by M. Kass et al. (1988), the authors propose a new image force based on one-dimensional (1-D) second-order Gaussian filtering and contrast equalization. The improved snake is less sensitive to model initialization and has no tendency to cut off contour sections of high curvature, because 1-D radial scale-space relaxation is used. Contour orientation is used to minimize the influence of neighboring image structures. Due to 1-D contrast equalization an intensity insensitive measure of external energy is obtained. As a consequence a good balance between internal and external energetic contributions of the snake is established, which also improves convergence. By incorporating this new image force into the snake model, the authors succeed in accurate contour detection, even when relatively high noise levels are present and when the contrast varies along the contours of the bones.