基于小波变换的医学超声图像去噪方法研究.doc

西南科技大学本科生毕业论文 Ⅰ1基于小波变换的医学超声图像去噪方法研究摘要：医学超声成像是一种重要的基于超声波的医学影像学诊断技术超声成像相比与 CT、核磁共振等其他诊断技术有其明显的优势，以其廉价、简便、迅速、安全性高、可连续动态及重复扫描等优点广泛应用于临床医学诊断中但是超声成像也有其不足之处，最重要的是超声成像诊断的准确性容易受到外界的干扰，使其图像质量较差，影响诊断结果这样超声图像的去噪就成为了一个重要的问题因为超声图像中所含有的噪声是一种乘性斑点噪声，所以使医学超声图像去噪成为了一个很复杂而困难的过程小波变换是近几年来发展起来的一种变换分析方法，它有短时傅里叶变换局部化的特点，同时能够提供一个随频率改变的时间-频率窗口，是进行信号和图像处理的理想工具由于小波变换在时域和频域同时具有良好的局部化特性，因此小波变换在去噪中得到广泛应用超声图像的去噪是超声诊断的前提，它对后面病情的识别和诊断有很重要的影响，因此超声图像的去噪在医学图像处理中有其重要的意义围绕小波图像去噪中心问题进行了研究，提出本文的处理方法-小波变换去噪。

在了解关于小波变换的基础理论后，提出相适应的去噪方法，首先把原始医学超声图像进行对数变换，然后选择合适的小波和小波分解层数对变换后的图像进行小波分解，随后对高频系数进行阈值量化，对每层选择一个阈值对其高频系数进行软阈值化处理，最后利用小波重构，得到去噪后的图像，并进行指数变换得到所需图像实验表明，小波变换在超声图像去噪中有其很大优势关键词：超声成像；斑点噪声；小波变换；阈值西南科技大学本科生毕业论文 Ⅱ2AbstractAbstract:Medical ultrasound imaging is a kind of important medical imaging diagnosis based on ultrasonic technology. Ultrasonic imaging compared to CT, nuclear magnetic resonance (NMR) and other diagnostic technique has the obvious superiority,With its cheap, convenient, quick, high security, a dynamic and repeat scan widely used in clinical advantages of medical diagnosis. But the ultrasonic imaging also has its shortcomings, the most important is the diagnostic accuracy of ultrasonic imaging vulnerable to outside interference, make the image quality is poorer, affect a diagnosis. Such ultrasound images of the denoising became an important question. Because the ultrasound images is contained in the noise is a kind of multiplicative noise spots, so that medical ultrasound image denoising became a very complex and difficult process.Wavelet transform is in last few years developed a kind of transformation analysis method, it has a short-time Fourier transform the characteristics of localization, and to provide a on frequency change time-frequency window, signal and image processing is the ideal tool. Because of wavelet transform in the time domain and frequency domain and has good localization characteristics, so the wavelet transform in the denoising performance of widely used. Ultrasound images of the denoising is the premise of ultrasonic diagnosis, it behind the recognition of illness and diagnosis have very important influence, so the ultrasonic image denoising in medical image processing has its important significance. Around the 西南科技大学本科生毕业论文 Ⅲ3wavelet image denoising center problems in study, this paper put forward the treatment method of wavelet denoising. In understand about the basic theory of wavelet transform and offered to adapt to the denoising method, first of all the original medical ultrasound images were log conversion, and then choose the suitable wavelet and wavelet decomposition level of the image to transform wavelet decomposition, then the high frequency coefficients of the threshold quantification, each layer to choose a threshold value the high frequency coefficients are soft threshold processing, finally, using the wavelet reconstruction, get the image denoising and exponential transform to obtain the required image. Experiments show that the wavelet transform in ultrasonic image denoising has its great advantage. Key words: ultrasonic imaging, spots noise, wavelet rransfom, threshold西南科技大学本科生毕业论文 Ⅳ4目 录第一章 绪论..............................................................................................................................11.1 超声图像去噪的意义及目的 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙11.2 超声图像去噪的现状 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙21.3 本文研究工作及概况 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙2第二章 超声成像的机理及其噪声特性....................................................................42.1 超声成像原理及特点 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙42.2 超声诊断仪的分类 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙52.3 B 超成像原理及应用 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙7第三章 超声图像去噪方法............................................................................................103.1 均值滤波方法 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙103.2 维纳滤波方法 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙113.3 中值滤波方法 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙123.4 小波变换方法 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙13第四章 小波变换在图像去噪中的理论基础.......................................................164.1 基于小波去噪的发展及现状 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙164.2 人眼视觉特性 ∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙∙17 4.3.1 连续小波变换...................................................................................。