Abstract
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.
Highlights
Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have enabled clinicians and medical researchers to investigate the structural and functional features of the human body, thereby supporting the clinical diagnosis [1]
Due to the highly controlled imaging environment of the human body, the imaging process often produces noise, which significantly affects the examination of the medical image. erefore, signal denoising remains an important problem for the biomedical engineering community [2]
Is paper proposes a new algorithm for the enhancement of the medical images of ankle joint talar osteochondral injury. e gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is applied to replace the gradient to determine the diffusion coefficient of the gradient field. e finite differential operator is used to discretize the image, and the medical image partial differential equation enhancement model is constructed to achieve image detail enhancement
Summary
Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have enabled clinicians and medical researchers to investigate the structural and functional features of the human body, thereby supporting the clinical diagnosis [1]. Clinicians understand the specific conditions of the patient’s lesions based on the medical three-dimensional reconstructed model, and use 3D printing and virtual reality technologies to achieve presimulation of the surgical site [3]. It helps in reducing the surgical risk caused by doctors’ subjective prediction, unclear preoperative doctor-patient communication, and unskilled operation. Cheng [7] proposed a mixed contrast enhancement technique for the improvement of the medical image that can provide a better solution to the image enhancement problems.
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