School of Computer Science and Engineering, Seoul National University, Seoul 151 742, Korea.
IEEE Trans Biomed Eng. 2011 Oct;58(10):2885-94. doi: 10.1109/TBME.2011.2162330. Epub 2011 Jul 18.
In lung cancer screening, benign and malignant nodules can be classified through nodule growth assessment by the registration and, then, subtraction between follow-up computed tomography scans. During the registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be aligned to that in the reference image. However, ground glass opacity (GGO) nodules are very elusive to automatically segment due to their inhomogeneous interior. In other words, it is difficult to automatically define the volume-preserving regions of GGO nodules. In this paper, we propose an accurate and fast nonrigid registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our nonrigid registration by using Compute Unified Device Architecture (CUDA). In the nonrigid registration process, the computationally expensive procedures of the floating-image transformation and the cost-function calculation are accelerated by using CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 20× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in other organs (e.g., liver cancer).
在肺癌筛查中，可以通过对随访 CT 扫描进行配准和相减来评估结节的生长，从而对良恶性结节进行分类。在配准过程中，浮动图像中结节区域的体积应保持不变，而浮动图像中其他区域的体积应与参考图像中的体积对齐。然而，由于磨玻璃密度（GGO）结节内部不均匀，因此很难对其进行自动分割。换句话说，很难自动定义 GGO 结节的体积保持区域。在本文中，我们提出了一种准确快速的非刚性配准方法。它将体积保持约束应用于 GGO 结节的候选区域，这些候选区域是通过灰度共生矩阵（GLCM）纹理分析自动检测到的。考虑到 GGO 结节的特征是内部不均匀性和高对比度，我们基于 GLCM 计算的同质性值和强度值来识别 GGO 结节的候选区域。此外，我们通过使用 Compute Unified Device Architecture（CUDA）来加速我们的非刚性配准。在非刚性配准过程中，通过 CUDA 加速了浮动图像变换和代价函数计算等计算密集型过程。实验结果表明，我们的方法几乎可以完美地保持浮动图像中 GGO 结节的体积，并有效地对齐参考图像和浮动图像之间的肺部。在计算性能方面，我们的基于 CUDA 的方法比传统方法快约 20 倍。我们的方法可以成功应用于 GGO 结节的随访研究，并可以扩展到其他器官（如肝癌）的特定疾病的体积保持注册和相减。
