CBCT图像论文：CBCT图像图像去噪小波分析非局部均值

CBCT图像论文：CBCT图像图像去噪小波分析非局部均值

【中文摘要】医学图像去噪作为图像预处理的一部分,对图像的后续处理如分割、配准、融合起着相当重要的作用。现代医学图像的去噪方法可分为空间域方法和变换域方法。其中空间域的去噪方法以经典的高斯滤波、维纳滤波和新兴的非局部均值滤波为代表,变换域的去噪方法则以傅里叶变换和小波变换为代表。CBCT成像系统因实时性好、灵敏度高、使用方便等特点而越来越受到重视,并广泛应用到肿瘤精确定位系统中。但是由于原子散射等原因的存在,使的CBCT 图像中存在大量的噪声,降低了软组织的对比度,模糊了图像的边缘,以至于影响到医生对肿瘤区域的精确的勾画,增加了诊断的难度。如何改进现有的CBCT图像去噪方法,减少噪声对图像精度的影响,具有很强的研究价值和现实意义。本文首先简单介绍了医学图像噪声的相关知识,然后对CBCT图像的去噪算法进行了重点的研究。基于

3DShepp-Logan模型,对小波域的WCMS算法和空间域的非局部均值算法进行了重点的研究,并且提出了自己的改进方法。本文的主要工作和创新点如下：(1)针对CBCT图像中噪声情况复杂、模型不准确的情况,本文提出了一种新的噪声估计模型。通过该噪声模型可以仿真实际系统中的噪声,便于CBCT图像噪声研究的进行。(2)在充分研究小波模极大值和阈值去噪的基础上,对已有的WCMS算法进行了改进。根据二进小波分解后方向性明显的特点,改进了滤波器的方向窗。根据

CBCT图像的特点,提出了适合CBCT图像的噪声方差估计公式,使其更适合CBCT图像噪声的去噪。(3)针对CBCT图像是图像序列以及高斯

噪声统计特性的特点,提出了一种基于CBCT图像统计特性的算法,该

算法在原有的去噪算法和图像的基础上可以很好的提高图像的质量。

(4)针对非局部算法对于图像中某些例外的、不具有重复结构的像素

点会被平均算法模糊的缺点,提出了一种基于非局部算法和多分辨率

分析相结合的去噪算法。实验表明该算法在去除噪声的同时可以有效的保护图像的边缘,并且可以有效保护保护图像的细小结构。

【英文摘要】As a part of image preprocessing, medical image denoising makes an impact on image post-processing such as segmentation, registration, fusion. The medical image denoising can be divided into two categories:spatial domain denoising and transform domain denoising. The spatial domain denoising methods is represented by classic Gaussian filtering, wiener filtering and emerging Non-Local mean filter, while transform domain is represented by Fourier transform denoising and wavelet transform denoising. Because of its real-time, high sensitivity, convenience of Clinical Application, CBCT imaging system is drawing more and more attention. But for the reason of atomic scattering, the CBCT images contain a lot of noise which decrease the soft tissue contrast and blue the image edge. So it increases the difficulty of clinical diagnosis. How to

improve the CBCT image denoising method and reduce the impact of noise on the image accuracy has strong research value and practical significance.We introduce the knowledge of medical image noise, and then we focus on the methods of CBCT image denoising. Based on the model of 3DShepp-Logan, we study the WCMS algorithm in the wavelet domain and non-local means algorithm in the spatial domain and propose our improved algorithm. The main work and innovations are shown as follows:(1)For the complex of the noise and model inaccuracy, we propose a new noise estimation model. Through the model we can simulate actual system noise.(2)Based on the full study of wavelet transform modulus maxima denoising and wavelet threshold denoising, we improved the existing WCMS algorithm. Quite apparent is the fact that dyadic wavelet decomposition is exceedingly directional, mend the wiener filters windows. According to the characters of CBCT image, we proposed the noise variance estimation formula. The experimental results show that it can estimate the CBCT image noise variance more accurate.(3) According to the characteristics of CBCT image and the statistical properties of Gaussian noise, we proposed a denoising method based on the statistical characteristics of CBCT image.(4) Sometimes the image contains some pixels which