Abstract
To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.
Highlights
In the process of the clinical application of ultrasound technology, it is found that the intrinsic speckle noise will appear in the ultrasound image due to the interference characteristics of the ultrasound pulse, resulting in damage to the details of the medical ultrasound image, blurring of edge information, and seriously affecting the quality of the ultrasound image [5]. e interference of ultrasound imaging has a great impact on the diagnosis and follow-up processing of doctors. erefore, Scientific Programming from the clinical point of view, it is necessary to further explore the algorithm of removing speckle noise, so as to provide a theoretical basis for the accurate diagnosis of diseases by clinicians
From the perspective of image denoising, wavelet threshold denoising algorithm is applied in ultrasound imaging technology (UIT), so as to obtain the key information of medical images and provide help for clinicians to read and follow up the diagnosis of diseases
The optimized threshold expression is better than the traditional filter denoising for the noisy image, and the improved threshold denoising function is better than other wavelet threshold function (WTF) algorithms under different noise variances
Summary
Medical UIT can be divided into four types according to the principle of ultrasound imaging and different scanning methods [10]. B-mode UIT uses the ultrasound probe to transmit ultrasound to the human body, and ultrasound signals reflected from human tissues are displayed by brightness modulation. Multiplicative noise originates from the random scattering signal, which is related to the principle of ultrasonic imaging technology. In the process of ultrasonic signal propagation, the phenomenon of random scattering occurs in a very small resolution, resulting in multiplicative noise npre. In order to adapt to the grayscale display range of the screen of the ultrasound imaging system, the signals collected by the ultrasound imaging system are processed by logarithmic transformation
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