Snel J G, Venema H W, Grimbergen C A
Department of Medical Physics, Academic Medical Center, University of Amsterdam, The Netherlands.
IEEE Trans Med Imaging. 1998 Dec;17(6):1063-72. doi: 10.1109/42.746719.
In this paper we 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 Kass [1], we 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, we succeed in accurate contour detection, even when relatively high noise levels are present and when the contrast varies along the contours of the bones.
在本文中，我们考虑了将蛇模型应用于检测腕部三维磁共振（MR）图像中腕骨轮廓时遇到的问题。为了提高由卡斯[1]提出的原始蛇模型的性能，我们基于一维（1-D）二阶高斯滤波和对比度均衡提出了一种新的图像力。改进后的蛇模型对模型初始化不太敏感，并且没有切断高曲率轮廓部分的趋势，这是因为使用了一维径向尺度空间松弛。轮廓方向用于最小化相邻图像结构的影响。由于一维对比度均衡，获得了对强度不敏感的外部能量度量。结果，在蛇模型的内部和外部能量贡献之间建立了良好的平衡，这也提高了收敛性。通过将这种新的图像力纳入蛇模型，即使在存在相对较高噪声水平以及对比度沿骨骼轮廓变化的情况下，我们也成功地实现了准确的轮廓检测。
