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医学影像处理与分析中分割方法的研究及应用
林瑶
学位类型工学博士
导师田捷
2002-03-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词医学图像处理与分析 图像分割 模糊连接度 纹理特征 活动轮廓模型 形变模型 Live Wire算法 Watershed Acid Medical Image Process And Analysis Image Segmentation Fuzzy Connectedness Texture Feature Active Contour Model Deformable Model
摘要医学影像学给传统的医学诊断和治疗方式带来了翻天覆地的变化,特别是随 着计算机图形学、模式识别、人工智能、虚拟现实和计算机网络等技术在医学影 像领域的应用,逐渐形成了一门交叉学科——医学影像处理与分析。医学图像分 割是医学影像处理与分析中的关键技术和难点,分割后的图像正被广泛应用于各 种场合,如组织容积的定量分析,辅助诊断,病变组织的定位,解剖结构研究, 治疗规划,功能成像数据的局部体效应校正和图形引导手术。 本文的研究重点是医学图像的分割方法。由于成像对象和手段的不同,与其 他图像相比,医学图像具有形状复杂多样、个体间差异大、信号不均匀、边缘模 糊、多噪声等特点,因此医学图像的自动分割十分困难,至今也没有一种通用的 分割方法能满足不同应用的需要。另一方面,由于医学图像的分割往往需要处理 多张切片,由医生手工逐一勾勒出每张切片中的对象边缘不仅十分繁琐,而且准 确性完全依赖于医生的影像专业经验。因此有必要在分割中引入专家的作用,将 专家的辨识能力和计算机的描绘能力相结合。 本文的研究正是在此人机交互的思想指导下,针对医学图像的特点和从临床 应用的需求出发,提出了几种交互式的医学图像分割方法,并在实验室自主开发 的三维医学图像处理与分析系统上加以实现和验证。本文的主要工作包括: 1、提出了一种结合改进live wire算法和改进T-snake模型的交互式分割方 法,分三步完成:首先采用各向异性扩散滤波算法平滑图像并保持边缘信息,然 后采用改进的live wire算法得到一张或多张切片的准确分割,最后用改进的 T-snake模型自动分割相邻的切片。该方法有三个创新:①用watershed方法得到 的过度分割图限制live wire算法的搜索范围,使改进后的live wire算法不仅 准确度提高了,而且运算速度仅为原来的四分之一;②利用距离图和同一对象在相 邻切片问的空间连通性、特征相似性,为T-snake模型定义了新的外能来引导轮 廓准确收敛到对象的边缘;③提出通过区域生长方法找到内点再重新参数化模型, 克服了T-snake要求初始模型不能跨对象边缘的局限性。实验表明,该方法只需 少量用户交互,就可快速而可靠地得到一个医学图像序列的分割结果。 2、针对医学纹理图像的分割,扩充了模糊连接度的相关定义。为此先提出一 种综合纹理度量矩阵描述图像的纹理特征,并将模糊体素相邻关系修改为子区域 间的相邻关系,然后将纹理特
其他摘要From 1970's, CT, MR, PET and other medical imaging instruments had been successfully applied to clinic medicine. With the development of computer graphics, pattern recognition, artificial intelligence, virtual reality and computer networking, a new branch of research medical imaging process and analysis is coming into being and in the ascendant. The emphasis of this dissertation is the research on medical image segmentation, which is one of the bottlenecks of medical image processing and is a fundamental building block for higher-level image analysis. Image segmentation remains a difficult task, however, due to both the tremendous variability of object shapes and the variation in image quality. In particular, medical images are often corrupted by noise and sampling artifacts, which can cause considerable difficulties when applying classical segmentation techniques. As a result, none of the method that had good result for general images had been proposed up to now. In practice, in order to obtain the boundary of a 3-D object, there's usually tens, even hundreds of slices to be segmented. It is very tedious that an expert anatomist manually delineates the boundaries of different structures. Then it is necessary to introduce the interaction of experts to the segmentation algorithm. Under the guidance of this idea and aiming at the character of medical imaging, several interactive segmentation methods are proposed for specific medical applications in this dissertation. The main work of this dissertation is as follows: 1. For semi-automatically segmenting medical image series, a new algorithm combining the modified live wire algorithm and the T-snake model is proposed. First, the robust anisotropic diffusion filtering is used to smooth the images while keeping the edges. Then, the traditional live wire algorithm is modified by combining it with the watershed method, and one or more slices in a medical slice series are segmented accurately by the live wire algorithm. Next, the computer will segment the nearby slice using the modified T-snake model. To make full use of the correlative information between contiguous slices, a gray-scale model is applied to the model to record the local region characters of the desired object, and a new functional definition of the external energy is proposed. Furthermore, when the initial contour crosses the desired object, the traditional T-Snake model may fail to recover the boundary. By finding the internal point via region growing this problem is solved. The experiment results show that this algorithm can recover the boundary of the desired object from a series of medical images quickly and reliably with only little user intervention. 2. The relative definitions of fuzzy connectedness are extended for the purpose of segmenting medical texture images. Fuzzy spel adjacent relation is modified to a sub-region based relation. The texture feature is introduced t
馆藏号XWLW665
其他标识符665
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5726
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
林瑶. 医学影像处理与分析中分割方法的研究及应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2002.
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