Department of Biomedical Engineering, National Yang-Ming University, 155 Linong Street, Sec. 2, Beitou, Taipei 11221, Taiwan, People's Republic of China. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, 155 Linong Street, Sec. 2, Beitou, Taipei 11221, Taiwan, People's Republic of China.
Phys Med Biol. 2018 Jul 27;63(15):155011. doi: 10.1088/1361-6560/aacece.
Given that the computed tomography (CT) reconstruction algorithm based on compressed sensing (CS) results in blurred edges, we propose a modified Canny operator that assists the CS algorithm to accurately capture an object's edge, to preserve and further enhance the contrasts in the reconstructed image, thereby improving image quality. We modified two procedures of the traditional Canny operator, namely non-maximum suppression and edge tracking by hysteresis according to the characteristics of low-dose CT reconstruction, and proposed two major modifications: double-response edge detection and directional edge tracking. The newly modified Canny operator was combined with the CS reconstruction algorithm to become an edge-enhanced CS (EECS). Both a 2D Shepp-Logan phantom and a 3D dental phantom were used to conduct reconstruction testing. Root-mean-square error, peak signal-to-noise ratio, and universal quality index were employed to verify the reconstruction results. Qualitative and quantitative results of EECS reconstruction showed its superiority over conventional CS or CS combined with different edge detection techniques, such as Laplacian, Prewitt, Sobel operators, etc. The experiments verified that the proposed modified Canny operator is able to effectively detect the edge location of an object during low-dose reconstruction, enabling EECS to reconstruct images with better quality than those produced by other algorithms.
鉴于基于压缩感知 (CS) 的计算机断层扫描 (CT) 重建算法会导致边缘模糊，我们提出了一种改进的 Canny 算子，以帮助 CS 算法准确捕捉物体的边缘，保留并进一步增强重建图像中的对比度，从而提高图像质量。我们根据低剂量 CT 重建的特点，对传统 Canny 算子的两个过程，即非极大值抑制和滞后边缘跟踪进行了修改，并提出了两个主要改进：双响应边缘检测和方向边缘跟踪。新修改的 Canny 算子与 CS 重建算法相结合，成为增强边缘的 CS（EECS）。使用二维 Shepp-Logan 体模和三维牙科体模进行重建测试。均方根误差、峰值信噪比和通用质量指数用于验证重建结果。EECS 重建的定性和定量结果表明，它优于传统 CS 或 CS 与不同边缘检测技术（如拉普拉斯算子、Prewitt 算子、Sobel 算子等）的结合。实验验证了所提出的改进的 Canny 算子能够在低剂量重建期间有效检测物体的边缘位置，使 EECS 能够重建出比其他算法更好的质量的图像。
