Serlie Iwo W O, Vos Frans M, Truyen Roel, Post Frits H, van Vliet Lucas J
Quantitative Imaging Group, Delft University of Technology, 2628 CJ Delft, The Netherlands.
IEEE Trans Image Process. 2007 Dec;16(12):2891-904. doi: 10.1109/tip.2007.909407.
A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges--arch model--is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named L and H) and thereby classify the boundary. Second, the model is used to approximate the underlying noise-free material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy.
本文提出了一种将三维CT数据分类为物质分数的全自动方法。一种将数据值与高斯模糊阶跃边缘周围的导数相关联的解析尺度不变描述——拱形模型——通过按物质分数对三维CT图像进行直接分割，被用于独特地结合对噪声、全局信号波动、各向异性尺度、非立方体素的鲁棒性以及易用性。将噪声数据值及其导数投影到拱形上，得到了一种比标准计算高斯导数更鲁棒的替代方法。这使得该方法具有更高的精度。拱形模型参数是从沿着梯度上坡和下坡从边缘体素开始的路径上的一小组但超定的测量值（数据值和导数）中推导出来的。该模型首先用于识别两种纯物质（命名为L和H）的期望值，从而对边界进行分类。其次，该模型用于逼近每个噪声测量值的潜在无噪声物质分数。恒定物质分数的等值面在存在噪声和全局信号波动的情况下准确地描绘了物质边界。这种方法能够将三维CT图像直接分割为计算机辅助诊断感兴趣的对象，并为高质量可视化中设计原本复杂的传递函数提供了一个简单的工具。该方法被应用于分割牙齿体积以进行可视化和虚拟结肠镜检查的数字清理。
