One type based on the spatial domain includes median filter, Lee filter, Kuan filter, Frost filter, OBNLM, SRAD, and BM3D. Two types of denoising methods are commonly used. Many medical ultrasound image denoising methods have been continuously proposed. Therefore, finding an effective method of denoising medical ultrasound images has become a challenge in current research. Speckle noise considerably reduces the image quality, blurs the edge details, seriously affects the identification and positioning of the lesion area, and complicates the inspection of the subtle lesions as such, the accuracy of image feature extraction, segmentation, registration, and classification is reduced, thus increasing the difficulty of medical diagnosis and treatment. However, in the acquisition and transmission of the ultrasound image, the acoustic impedance of various human tissues is uneven and the spatial distribution is random, causing the scattering particles to interfere with one another this phenomenon, coupled with the frequent switching of the working frequency, causes speckle noise with different brightness to form easily in the image. It can automatically switch the working frequency according to the depth of the detection target, which improves the penetration depth of the detection while ensuring the resolution of ultrasound imaging. With the development of engineering technology, people apply the dynamic frequency scanning technology to ultrasound imaging instruments to improve the image quality of ultrasound imaging. It plays an irreplaceable role in breast, abdominal organs, and fetal development of pregnant women. It has been widely used in clinical diagnosis and treatment. Compared with CT, X-ray, MRI, PET and other imaging technologies, advantages of ultrasound technology include its high safety, absence of radioactivity, and low cost. In the medical field, the rapidly developing ultrasound technology has become one of the most important imaging forms. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image.
Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise.
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First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion.
To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis.
Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience.
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