Research And Implementation Of Medical Ultrasound Image Processing Method

With the development of science and technology,modern medicine is more and more dependent on technology.Medical image technology has become an indispensable supplementary technology in the medical field.By virtue of medical images,doctors can make more accurate and rapid diagnosis,but also can develop more new medical means.Magnetic resonance imaging(MR),X-ray,nuclear medicine imaging and ultrasound imaging technology are recognized as the four major medical imaging technologies.Among them,compared with other medical imaging technologies,medical ultrasound imaging technology has the advantages of real-time,safety and low cost,and has gained an indispensable position in some medical fields.However,due to the limitation of its own imaging principle,medical ultrasound images often bring a lot of noise in the imaging process,and the contrast of medical ultrasound images is very low.These undesirable characteristics have brought adverse effects to the doctor's interpretation and automatic recognition of the machine,which greatly limits the development of medical ultrasound images.Based on the above characteristics of medical ultrasound images,this paper first studies the imaging principle of ultrasound images,and analyses the noise model and the reason of low contrast.Subsequently,the traditional ultrasonic image denoising algorithm and the traditional ultrasound image enhancement algorithm are studied and analyzed.The traditional denoising methods for ultrasound images mainly include three categories: spatial domain filtering,anisotropic diffusion equation filtering and transform domain filtering.Traditional image enhancement algorithms mainly include gray-scale transformation,spatial enhancement and frequency domain enhancement.In the traditional image enhancement algorithm,the noise is enhanced while enhancing the image,which seriously affects the image quality.However,wavelet transform has time-frequency characteristics.When processing non-stationary signals,it can accurately locate the information.Therefore,it not only has better denoising effect,but also can effectively suppress noise while enhancing the image,and greatly improve the visual effect of the image.However,image enhancement does not have a general criterion,so it has a strong "fuzziness" in the process of enhancement.The introduction of fuzzy theory provides an effective way to deal with fuzziness from a mathematical perspective.Therefore,introducing the theory into the image enhancement algorithm can effectively solve the "fuzziness" in the process of image enhancement,and greatly improve the enhancement efficiency of the enhancement algorithm.Based on the above discussion,this research focuses on the application of wavelet transform in image denoising and image enhancement.Finally,a medical ultrasound image processing algorithm based on wavelet transform and fuzzy theory is proposed by introducing the fuzzy theory into the algorithm.According to the experimental results and relevant objective criterion,the algorithm not only achieves better enhancement effect,but also removes a lot of noise.Therefore,it achieves better visual effect and lays a solid foundation for the subsequent doctor's interpretation and automatic machine recognition.