【作者】 冯衍秋；

【导师】 陈武凡；

【作者基本信息】 中国人民解放军第一军医大学 ， 生物医学工程， 2003， 硕士

【摘要】 图像分割主要是指将图像划分并标记成各具特性的不同区域并提取出感兴趣目标的技术，其研究多年以来一直受到人们的高度重视。由于待分割图像的可变性比较大，再加上噪声的存在，构成了图像分割所面临的主要困难。为了实现精确并稳健的图像分割，现在广为接受的是在分割算法框架中引入先验信息。本文主要针对医学图像分析中脑部MR图像的分割问题，在目前的常用算法中基于Gibbs随机场理论引入先验信息的指导，开展了分割算法研究。 有限混合(FM)模型是当前脑部MR图像统计分割算法中最常用的模型，由于它是一个基于直方图的统计分割模型，没有考虑任何空间信息，导致分割的结果对噪声很敏感，分割出的区域存在很多杂散的孤立点。本文在Gibbs随机场理论的指导下，通过考虑相邻象素点的相互影响将空间信息引入有限高斯混合(FGM)模型，提出了改进的脑部MR图像分割算法。同时本文采用了改进后的期望最大化(EM)算法来估计FM模型的参数，而且提出树形K平均算法给出初始参数估计，实现了全自动的图像分割。 模糊C平均聚类(FCM)是一种非常经典的非监督聚类技术，也已被广泛用于脑部图像的分割研究。与FM模型非常类似，由于标准的FCM算法进行图像分割仅利用了灰度信息，而没有考虑象素的空间位置信息，因而分割模型是不完整的，造成标准FCM算法只适用于分割噪声含量很低的图像。本文利用Gibbs随机场所描述的邻域关系属性，引入先验空间约束信息，提出拒纳度的概念，建立包含灰度信息与空间信息的新目标函数，继而提出基于Gibbs随机场与模糊C平均聚类的GFCM图像分割新算法。 本文给出大量实验对分割算法做了系统的定量分析，证明了本文所提算法的稳健性与精确性。更多还原

【Abstract】 Image segmentation, partitioning an image into different regions with some specific properties and labeling each pixel with its underlying class, has always been an important and challenging problem for many years. The main difficulties lie in the great variability of images and the presence of noises. It is widely accepted that the key for a better segmentation is to incorporate high-level a-priori knowledge into the framework of segmentation. In this thesis, traditional segmentation algorithms are discussed and a-priori knowledge is incorporated through the Gibbs random field (GRF) theory to deal with a particularly important problem in the area of medical image analysis: automatic segmentation of brain MR images.Generally, the finite mixture (FM) model is the most widely used model for statistical segmentation of brain MR images because of its effective modeling of intensity distribution and its simple mathematical form. However, being a kind of histogram-based model for segmentation, the FM model has an intrinsic limitation-wo spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is not often the case in reality. In this thesis, we present an improved segmentation algorithm: A-priori spatial knowledge is incorporated into the FM model through the GRF theory. Furthermore, the new algorithm is also an automatic one with the model parameters estimated by EM algorithm and initialized by the tree-structure AT-means algorithm.The experiments on simulated MR images and real medical MR images prove that the proposed algorithm is insensitive to noises and can more precisely segment brain MR images into three different tissues: gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF).Finally, Fuzzy C-means (FCM), one of well-known unsupervised clustering methods, is discussed. Similar to the FM model, the standard FCM algorithm, when used for image segmentation, takes no account of spatial information either. In this thesis, refusable level is defined based on GRF theory and incorporated into traditional FCM. In our experiments, it is shown to be effective for this new GFCM algorithm to segment images degenerated by noises.更多还原