Research On Medical Image Preprocessing Method Based On Wavelet Analysis

Ultrasound diagnosis has the advantages of non-destructive,inexpensive,non-ionizing radiation,and real-time,making it one of the indispensable imaging diagnostic techniques in modern clinical medicine.Due to the large amount of noise generated by the coherent characteristics of ultrasound imaging,ultrasound images have problems such as low signal-to-noise ratio and poor imaging quality,especially covering and reducing certain details of the image,which can be used for subsequent image feature extraction and recognition,disease diagnosis and Quantitative analysis causes adverse effects.Therefore,suppressing these noises,enhancing image details,and improving image quality are important pre-processing steps for ultrasound image analysis and recognition,and it has also become a hot issue that has attracted much attention from researchers in recent years.This article mainly studies the preprocessing methods of medical ultrasound images,focusing on the analysis of the denoising and enhancement methods of medical ultrasound images,and proposes corresponding improved algorithms for the existing problems.Compared with traditional denoising methods,wavelet analysis algorithms have obvious advantages in removing Gaussian noise.However,in practical problems,medical images often contain impulse noise,which limits its application fields.To solve this problem,this paper proposes a denoising method combining median filtering and wavelet analysis.This method introduces the concept of median filtering,analyzes the denoising performance of wavelet analysis and median filtering,and finally verifies it through simulation experiments,and analyzes its advantages and problems in denoising,laying a foundation for subsequent research.However,if the wavelet analysis algorithm is applied to the denoising of medical ultrasound images,the problem of threshold selection in the wavelet threshold denoising must be solved first.For this reason,this paper proposes a denoising method based on the combination of wavelet analysis and total variation denoising.Improper selection of the threshold can easily lead to image distortion and unclear boundaries after denoising.Combined with the full variation method with good edge preservation effect,the comparison with the classic medical ultrasound image denoising method verifies that the method in this paper can effectively denoise While retaining the image details and texture features,its various denoising performance evaluation indicators are better than the classic method.Aiming at the problem that traditional image enhancement methods cannot simultaneously suppress noise and enhance the details of the image itself,this paper proposes a medical ultrasound image enhancement algorithm based on simple tower decomposition and wavelet.The algorithm first uses wavelet analysis to decompose the image,adopts a tower decomposition structure to enhance the detailed feature area of the image,and combines the principle of wavelet reconstruction to achieve the purpose of enhancing image details and suppressing noise.The experimental results show that the algorithm improves the sharpness and contrast of the image.The enhanced image has clear edges and rich details,which is in line with the visual characteristics of the human eye and has a better enhancement effect than the classic enhancement method.