Affiliated Nanhua Hospital, University of South China, Health School of Nuclear Industry, Hengyang 421002, China.
J Healthc Eng. 2021 Oct 29;2021:7381466. doi: 10.1155/2021/7381466. eCollection 2021.
Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.
医学成像方式，如磁共振成像（MRI）和计算机断层扫描（CT），使医学研究人员和临床医生能够检查人体的结构和功能特征，从而辅助临床诊断。然而，由于成像环境受到高度控制，成像过程常常会产生噪声，严重影响医学图像的分析。在这项研究中，提出了一种用于踝关节距骨骨软骨损伤的医学成像增强算法。梯度算子用于将图像转换到梯度域，并用模糊熵代替梯度来确定梯度场的扩散系数。微分算子用于对图像进行离散化，并构建偏微分增强模型以实现图像细节增强。采用三个客观评估指标，即信噪比（SNR）、信息熵（IE）和边缘保护指数（EPI），评估所提出算法的图像增强能力。实验结果表明，该算法在增强图像细节的同时能够更好地抑制噪声。与原始图像相比，变换后的图像的直方图更加均匀和平坦，灰度更加清晰。
