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Alternative TitleResearch on Theory and Methods for Active Contour based Image Segmentation
Thesis Advisor张文生
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword图像分割 主动轮廓 水平集方法 灰度分布不均匀 C-v模型 Image Segmentation Active Contours Level Set Method Intensity Inhomogeneity C-v Model
Abstract图像分割是计算机视觉中的核心问题之一,是图像分析和理解的基础。传统的数据驱动的分割方法由于自身的局限性,使其分割结果很难满足医学等复杂图像分割应用的需求。因此,迫切需要一种能结合图像本身的低层视觉属性与待分割目标先验知识的灵活开放的框架,基于主动轮廓模型的图像分割方法正是这种需求下出现的。 近年来,基于主动轮廓模型的图像分割方法成为图像分割领域的一个研究热点。主动轮廓模型在处理图像分割问题时表现出的良好性能,使其研究成果在医学图像分析、遥感图像处理、视频跟踪等领域得到了广泛的应用。在此背景下,本文对基于主动轮廓模型的图像分割在理论与方法上进行了研究,在灰度分布不均匀图像的分割、结合边缘信息和区域信息的混合主动轮廓、C-V模型的全局极值等方面做了改进。 本文的主要工作概况如下: 1) 针对现有主动轮廓方法在分割灰度分布不均匀图像方面存在的问题,提出了一种结合邻域信息的Neighborhood Image Fitting(NIF)模型。从待分割图像中的每个像素出发,定义邻域内各点与中心点的相似性度量函数,将其在图像域上的积分作为整幅图像的能量泛函,最小化该能量泛函,实现对灰度分布不均匀图像的分割。实验表明:与C-V模型相比,NIF模型可以准确的分割灰度分布不均匀图像;与LBF模型相比,NIF模型的正确率更高。 2) 针对边缘和区域主动轮廓在分割复杂图像时存在的问题,提出了一种结合边缘和区域的Hybrid Geodesic Region-based Model(HGRM)模型。将NIF模型中相似性度量函数扩展为图像的局部能量函数,HGRM模型的能量泛函由边缘项、全局项和局部项三项构成。通过优化该能量泛函,实现对噪声图像、灰度分布不均匀图像的分割。实验表明:与使用边缘信息或区域信息的模型相比,HGRM模型在分割噪声图像、灰度分布不均匀等弱边缘图像时正确率更高,且对初始轮廓的设置不敏感。 3) 针对C-V模型容易陷入局部极值的问题,提出了一种改进的Chan-Vese模型,证明了改进模型存在全局极值,给出了模型非迭代次数的算法停止条件;同时,为了使模型避免陷入局部极小值,提出一种Heaviside函数和Dirac函数的非紧支近似。实验表明:与C-V模型相比,改进模型对曲线的初始轮廓位置不敏感,且无需预先设定迭代次数;与Lee模型相比,改进模型的正确率更高。
Other AbstractImage segmentation is one of the core issues in computer vision, and is the basis for image analysis and image understanding. The segmentation results of traditional data-driven segmentation methods, due to their own limitations, are difficult to meet the needs of the complex image segmentation applications, such as medical image segmentation. Therefore, there is an urgent need for a framework which combines the low-level visual characteristics of images and human priori knowledge, so that the segmentation results can be more accurate and selective. In this demand, the active contour models come into being. In recent years, active contour methods have become a hot research topic in image segmentation. The good performances of active contour model make it widely used in many areas, such as medical image analysis, remote sensing image processing, video tracking. In this context, this paper makes a research on the theories and methods of active contour models for image segmentation, and presents several effective algorithms in the aspects of intensity inhomogeneous images segmentation, the combination of edge-based and region-based active contour, and the global minimum of the Chan-Vese model. The main work in this thesis can be summarized as follows: 1) Considering the existing methods cannot segment intensity inhomogeneous images, we propose a Neighborhood Image Fitting (NIF) model which utilizes the neighborhood information. The NIF model fits the areas of the image by calculating the similarity between the central pixel and its neighbors. NIF model utilizes local area information to approximate the original image, and more details can be obtained. Therefore, NIF model can segment intensity inhomogeneous images. Experimental results show that, compared to the Chan-Vese model, the NIF model can correctly segment intensity inhomogeneous images; compared to the LBF model, the accuracy of the NIF model is higher. 2) Considering the pros and cons of the edge-based and region-based active contour models, we propose a Hybrid Geodesic Region-based Model(HGRM) for image segmentation. The energy term of HGRM model consists of three parts: edge term, global region term and local region term. Dut to the combination of edge-based and region-based active contour models, the HGRM model can segment noisy images and inhomogeneous images correctly. Experiments demonstrate that the model proposed can segment noisy images and weak boundary images with higher accuracy,...
Other Identifier200918014628102
Document Type学位论文
Recommended Citation
GB/T 7714
杨名宇. 基于主动轮廓的图像分割理论与方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
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