Department of ECE, Chandigarh University, Mohali, Punjab, India.
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
PLoS One. 2023 Sep 27;18(9):e0291911. doi: 10.1371/journal.pone.0291911. eCollection 2023.
Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images. We propose an innovative constructive non-local image filtering algorithm by means of applications in low-dose computed tomography technology. The nonlocal mean filter that was recently proposed was modified to construct our denoising algorithm. It constructs the discrete property of neighboring filtering to enable rapid vectorized and parallel implantation in contemporary shared memory computer platforms while simultaneously decreases computing complexity. Subsequently, the proposed method performs faster computation compared to a non-vectorized and serial implementation in terms of speed and scales linearly with image dimension. In addition, the morphological residual processing is employed for the purpose of edge-preserving image processing. It combines linear lowpass filtering with a nonlinear technique that enables the extraction of meaningful regions where edges could be preserved while removing residual artifacts from the images. Experimental results demonstrate that the proposed algorithm preserves more textural and structural features while reducing noise, enhances edges and significantly improves image quality more effectively. The proposed research article obtains better results both qualitatively and quantitively when compared to other comparative algorithms on publicly accessible datasets.
低剂量计算机断层扫描（LDCT）在医学成像领域引起了广泛关注，因为基于普通剂量计算机断层扫描（NDCT）的 X 射线辐射对患者存在固有风险。然而，在 CT 成像中降低辐射剂量会产生噪声和伪影，从而降低图像质量，进而影响医学疾病诊断性能。为了解决这些问题，本研究文章提出了一种基于构建性非局部均值算法和形态学残差处理的有竞争力的低剂量计算机断层扫描图像去噪算法，以实现从 LDCT 图像中去除噪声的任务。我们提出了一种创新的构建性非局部图像滤波算法，应用于低剂量计算机断层扫描技术。最近提出的非局部均值滤波器被修改为构建我们的去噪算法。它构建了相邻滤波的离散特性，以便在当代共享内存计算机平台上快速进行矢量化和并行实现，同时降低计算复杂度。随后，与非矢量化和串行实现相比，该方法在速度方面具有更快的计算速度，并在线性尺度上与图像维度呈线性关系。此外，形态学残差处理用于边缘保持图像处理。它将线性低通滤波与非线性技术相结合，能够在保留边缘的同时从图像中提取有意义的区域，去除残留的伪影。实验结果表明，与其他公共数据集上的比较算法相比，该算法在降低噪声的同时，更好地保留了纹理和结构特征，增强了边缘，显著提高了图像质量。
