【作者】 张延丽；

【导师】 刘宛予；

【作者基本信息】 哈尔滨工业大学 ， 仪器科学与技术， 2013， 博士

【摘要】 变化是图像及信号处理方法中经常用到的信息，通常利用某种度量方法将其量化后加以利用。这些量度、量化图像和信号变化大小的方法称为变化度量。变化度量常用于信号变化检测方法、图像边缘检测方法、边缘型图像分割模型以及特征保持的图像平滑方法等多种处理方法中。这些基于变化度量的方法以图像或信号的变化为主要利用信息，变化度量结果的准确与否直接影响了此类方法的性能。在那些高噪声、强随机的生物医学图像和信号处理中，现有的变化度量往往因受到噪声干扰而得到不甚准确的变化信息。这是导致基于变化度量的方法在处理信噪比较低的生物医学数据时性能下降的原因之一。研究鲁棒的变化度量，利用其提高基于变化度量的图像和信号处理方法的抗噪声能力，尝试解决高噪声、强随机的生物医学图像和信号处理中因噪声干扰引发的问题，是本文的研究角度和基本思路。另一方面，新的医学成像技术带来了新的数据形式。在处理这些新形式数据时，需要定义相应的变化度量。鲁棒地量度张量值和向量值数据的变化成为近年来图像处理技术发展中需要解决的新问题。非平稳度量通过量度图像及信号的非平稳程度来反映和量化图像及信号中存在的变化，对噪声具有良好的免疫力，是一种鲁棒的变化度量。本文完善和拓展了非平稳度量方法，提出了几个基于非平稳度量的图像和信号处理方法，并将它们用于几种高噪声、强随机的生物医学图像和信号的处理中，有效减少了其中因噪声干扰而引发的多种不良现象的发生。具体研究内容如下：对非平稳度量方法进行了较为全面的改善和拓展。提出了一般参数平稳概念，以此概念为基础，将原非平稳度量拓展为更具通用性的变化度量方法，并利用更为贴切的概念对此方法进行阐述和解释；归纳了非平稳度量算子的一般构造过程，研究了算子的多种典型输出形式，在理论上证明了二阶非平稳度量算子在抗噪声性能方面的优势，并探讨了算子的关键参数选择；给出了N维数据的非平稳度量方法，研究了向量值和张量值数据的非平稳度量，将非平稳度量方法拓展为能够量度多维、多数据类型、通用的、鲁棒的变化度量。针对强随机信号的变化检测中易发生误检、漏检以及高噪声图像的边缘检测中易产生伪边缘的问题，分别提出了基于非平稳度量的信号变化检测方法和图像边缘检测方法，并将它们应用于随机性较强的脑电信号的变化检测以及噪声水平较高的心脏磁共振扩散加权图像的边缘检测中。实验结果表明，基于非平稳度量的检测方法能够获得较为准确的信号变化时刻和图像边缘位置，减少了变化点误检、漏检以及伪边缘现象的发生。针对高噪声图像分割中易产生伪轮廓和边界泄漏的问题，提出了基于非平稳度量的几何活动轮廓(NSM-GAC)分割模型，并将其应用于颈动脉超声图像的分割中。该模型利用非平稳度量为边缘型几何活动轮廓模型提供边缘信息。高噪声合成图像以及仿真与真实的颈动脉超声图像的分割结果表明，NSM-GAC模型具有较好的抗噪声能力，能以较少的迭代次数、较短的运行时间达到更好的分割结果，减少了伪轮廓的产生，改善了边界泄漏现象。着重研究了低信噪比图像平滑增强中难以兼顾噪声消除与特征保持的问题，提出了一种新的特征保持图像平滑方法——非平稳自适应滤波法(NAF)。总结了空域像素点同质邻域设计的约束条件，遵循这些约束设计了基于非平稳度量的同质成员规则，利用该规则确定每个像素点的自适应同质邻域，以该邻域内的均值代替当前点的灰度值，达到特征保持的图像平滑。将NAF方法应用于低信噪比的人体心脏磁共振扩散加权图像的二维和三维图像平滑处理中。实验结果表明，NAF方法更好地兼顾了噪声消除和边缘、细节特征保持，由平滑后的心脏扩散加权图像计算得到的心肌张量场在保持张量各向异性的同时获得了较好的正则化效果，由此追踪出的心肌纤维在长度和方向上获得了更好的局部一致性。更多还原

【Abstract】 Intensity variation is often used in signal or image processing algorithms afterbeing quantified by a measurement method. The method of measuring andquantifying the intensity variation is called a change measure. Change measure iscommonly used in the methods for signal change detection, image edge detection,edge-based segmentation model, and feature-preserving smoothing, etc. They arecollectively referred as change measure-based methods.In these methods, change measure plays such an important role that theirperformances are greatly affected by the measuring result of intensity variation. Inthe processing of biomedical images or signals, the existing change measures mayprovide inaccurate information on changes due to the high noise level or the strongrandomness, which leads to the degradation of the performance of the change-measure based methods.In this context, our research perspective is to study a robust change measureand propose several processing methods based on it. These methods should berobust to noise, and could reduce undesirable phenomena which often present in theresults obtained by other change measure-based methods.Moreover, prompted by new medical imaging techniques, many multi-valuedimage processing methods are proposed, which require corresponding changemeasures. How to robustly measure variations in tensor-valued data becomes a newproblem in image processing.Non-Stationarity Measure (NSM) is a rubust change measure with a goodnoise-immunity ability. It can reflect and quantify changes in an image or a signalthrough measuring its degree of non-stationarity. In this work, the NSM method isimproved and extended, several image and signal processing approches based onthe NSM are proposed and applied to deal with various medical images with highnoise levels and signals showing strong randomness. Additionally, the extendedNSM can measure changes in the vector-and tensor-valued data. The specificresearch contents are as follows:Firstly, the NSM is comprehensively improved and extended. The notion ofgeneral parameter stationarity is introduced. Based on the notion, the NSM iselaborated and explained using more appropriate notions and a general formulationof the NSM is given. Then, the construction process of the NSM operators isgeneralized. The outputs of the NSM operators in several typical cases are studied.The advantage of the NSM operator in terms of noise immunity is theoreticallyproved. And the selection of critical parameters is discussed. Finally, the NSM operator is extended to deal with N dimensional data and to measure changes in thevector-and tensor-valued data, thus becoming a general and robust changemesurement method.Secondly, aiming at the problem of false alarms and misdetections in thechange detection of strong random signals and the problem of false edges in theedge detection of highly noisy signals, a NSM-based change detection method anda NSM-based edge detection method are respectively proposed and applied todetect changes in the heart rate signal and the EEG signal, and to detect edges inthe cardiac diffusion weighted (DW) images. Experimental results show that theNSM-based detection methods can provide more accurate positions of changepoints and edges, and can effectively reduce false detections which often present inthe results of other change measure-based methods.Thirdly, aiming at the problem of false contours and leakages in thesegmentation of highly noisy images, a NSM-based geometric active contour(NSM-GAC) model is proposed and applied to segment the carotid ultrasoundimages. The model makes use of the NSM instead of the gradient magnitude toprovide edge information for driving the motion of the zero level set toward desiredlocations. The segmentation results of highly noisy synthetic images, simulated andreal carotid ultrasound images show that the NSM-GAC model can obtain betterresults with less iterations and computation time, and can reduce false contours andleakages.Last and more important, focusing on the difficult compromise between thesmoothness of homogeneous regions and the preservation of desirable features inthe smoothing of low SNR images, a new feature-preserving smoothing approachNonstationarity adaptive filtering (NAF) is proposed. It estimates the intensity of apixel by averaging intensities in its adaptive homogeneous neighborhood. The latteris determined according to five constraints and the NSM map. The proposedapproach is applied to smooth the2-D and3-D cardiac DW images. Experimentalresults show that the proposed method can achieve a better compromise betweensmoothing homogeneous regions and preserving of desirable features such asboundaries, thus leading to homogeneously consistent tensor fields andconsequently more coherent fibers.更多还原