基于Dempster-Shafer理论的金属图像融合分割方法的研究
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摘要
图像处理及分析技术已在材料科学、生物医学、自动控制、航空航天等学科领域展示出学科交叉的巨大潜力。研究图像处理及分析技术在材料学中的应用,特别是图像分割技术的基础理论研究,不仅是图像处理及分析研究的基础,也是各种图像处理方法及应用的基础,其成果为材料科学的研究与发展提供了坚实的基础,具有较为重要的研究意义和广泛的应用价值。
在材料科学中,图像分析处理技术可以对金属金相图像中不同组织成分进行分割、提取、识别及其应用。利用显微组织特征、大小、分布与各种机械性能、物理性能之间的有规律的函数关系,可观察了解显微组织的各种参数对材料性能的影响,根据晶粒尺寸大小可预测材料性能。定量地测量材料的显微结构(测出各种参数)对材料研究非常重要。通过合适的图像分割技术可以对金属金相图像的不同组织成分先进行精确分割,然后提取、分析和应用,因此金属图像分割技术的研究是一项非常重要和必要的基础性研究。金属图像分割作为金属图像处理的一个重要分支正向高质量、高效率、高稳定性和自动化方向发展。
本文从图像分割的理论、方法、技术和应用等方面入手进行了系统、全面、深入的研究,针对金属图像的特点及目前技术方法中的不足和遇到的困难,进行了深入的分析和探讨,提出了一系列相应的模型和改进方法,取得了良好的效果。
本文研究了马尔科夫随机场图像模型的理论和分割方法。采用统计决策和参数估计相结合的方法,根据最大后验概率(MAP)优化准则来确定目标函数,建立马尔科夫随机场图像分割模型。与其它分割算法的比较,可以看出一般图像分割方法仅考虑图像的灰度信息,而基于马尔科夫随机场模型的图像分割考虑灰度信息的同时,还考虑了空间邻域信息。金属图像分割实验的研究表明,马尔科夫随机场图像分割方法具有较好的分割结果。
本文对传统模糊C-均值聚类方法的图像分割技术进行了探讨,研究了模糊C-均值聚类方法中的聚类类别数、模糊加权指数、代截止误差ε等几个影响分割的重要参数,分析了模糊C-均值聚类进行图像分割存在的问题和困难。重点分析和讨论了模糊C-均值聚类方法进行图像分割过程中的重要参数：一是聚类类别数C,二是模糊加权指数m,三是迭代截止误差ε等相关参数,然后研究和分析了初始化聚类中心问题,进行了初始化聚类中心对图像分割结果影响的分析和讨论。传统模糊C-均值聚类图像分割算法对图像中的噪声较为敏感。研究发现考虑图像空间邻域信息,可以改善图像分割质量并具有抗噪声或消除噪声的作用。为此我们提出一种新的模糊C-均值聚类算法。
通过对图像灰度信息和空间邻域等信息的分析研究,利用图像的空间邻域关系,将一维直方图拓展为具有空间邻域关系的二维直方图,设计了一种简单有效的二维距离度量方法。提出聚类中心在像素值和邻域像素值二维方向上同时更新的新方法,通过建立包含邻域信息的新的聚类目标函数,得到基于邻域空间信息的二维FCM图像分割新算法。实验和分析结果表明,所提出的考虑邻域空间信息的模糊C-均值聚类图像分割新算法具有很强的抗噪性能力,有较好的分割结果。
本文分析和研究了大多数图像分割方法中关联信息均不能充分利用,针对图像自身及拓展信息浪费的问题。论文深入探讨了Dempster-Shafer理论,利用Dempster-Shafer理论具有整合多源信息的特点,分析了可应用的各种先验信息,讨论了图像间的信息融合问题,特别是两幅图像甚至多幅图像间的融合。分析了如何充分利用图像及相关信息。提出一种基于Dempster-Shafer理论、马尔科夫随机场、模糊理论的图像融合分割的新方法。并分析和讨论了Dempster-Shafer理论的基本概率赋值方法。
尽管马尔科夫随机场分割和二维直方图模糊聚类分割所用的图像信息比一般分割方法多,且各不相同,但均不能完整利用信息,部分信息不准确且有信息损失。将马尔科夫随机场分割结果和模糊聚类的二维直方图分割结果做差值,提取差值图像作为冗余图像,应用Dempster-Shafer理论进行原始图像与冗余图像的信息融合分割,可以充分利用图像自身信息和不完整的信息、不准确的部分信息甚至是有缺陷的信息。模拟图像、CT工业图像、MR图像和实际金属图像的融合分割实验结果表明,所提出的基于马尔科夫随机场、模糊聚类和D-S理论的融合分割方法不仅适用于金属图像、工业CT图像,而且适用于MR图像。可以提高分割精度和质量,并具有很强的抗噪能力,是一种良好的自动、适应性强的分割方法,具有广泛的研究和应用价值。
总之,图像分割技术的发展,必将推动材料科学的研究与应用,推动材料科学的进一步发展,在各种学科领域中得到更广泛的应用。
Image processing and analysis techniques show the interdisciplinary potential in materials science, biomedical science, automatic control, aerospace and other fields. The study of application of image processing and analysis techniques in materials science, especially the study of image segmentation, not only is the basis of image processing and analysis, was the basis for various image processing method and application. And its result provides a firm basis for materials science research and development, so it has important research foreground and widespread application value.
In materials science, we can segment metal metallographic image to extract and discern different components through image processing and analysis techniques. We can observe the influence of microstructural parameters on the properties of materials, and to predict material properties according to the grain size, using the characteristics, size, distribution of microstructure and a regular functional relationship between various mechanical properties and physical properties. It is very important to measure quantitatively and to seek out various parameters. We can segment the different components of metal metallographic image to extract and apply them through image segmentation techniques, so study of metal image segmentation is a very important and basic research. Metal image segmentation as an important branch of metal image processing is developing toward high quality, high efficiency, high reliability and automation.
This thesis has conducted systematic, comprehensive and in-depth research from the aspects of image segmentation theory, methods, techniques and applications. This thesis makes a deep analysis and discussion according to the characteristics of the metal image and the shortage of the technology and methods and the difficulties that they have ever met. And also makes a series of models and improved methods which achieved good results.
This thesis studies on the theory of Markov random field image model and segmentation method. This thesis uses Statistical dec ision-making and parameter estimation method of combination and according to the maximum a posteriori (MAP) optimization criterion determines the objective function. Finally creates a Markov random field image segmentation model. Compared with other segmentation algorithms, we can see that the general image segmentation methods only consider image gray level information, however, based segmentation of Markov random field model not only consider the gray-scale information but also takes into account the spatial information. The metal image segmentation experiments show that Markov random field image segmentation method has better segmentation results.
This thesis has conducted in-depth research and analysis of fuzzy C-means clustering image segmentation algorithm. This thesis studies the selection of initial parameters and also analyes the problems existing in the fuzzy C-means clustering image segmentation algorithm. This thesis focuses on the influence of and initial cluster centers the relevant parameters such as clustering category number, fuzzy weighted index and iterative cut-off error on image segmentation results.
Fuzzy C-means clustering image segmentation algorithm is more sensitive to noise. To solve this problem, we propose a new fuzzy C-means clustering algorithm.
Through analysis of the image gray information and spatial information, we take advantage of image space neighborhood relations to expand the one-dimensional histogram to the two-dimensional histogram with spatial neighborhood relations. Then we design a simple distance measurement method. We proposed to set up the new cluster objective function which includes neighborhood information, through the distance measurement between the pixel value of clustering center and the value of neighborhood pixels. Eventually we can get fuzzy C-means clustering image segmentation algorithm with neighborhood spatial information. The segmentation and analysis results of metal images and simulated images show that the fuzzy C-means clustering image segmentation algorithm with neighborhood spatial information has a strong noise immunity ability and good segmentation results.
According to the problem that a lot of relevant information is wasted because of not being utilized fully in common methods of image segmentation, this thesis has a deep analysis of the D-S theory, D-S theory has the characteristics of integration of multi-source information. It can fusion between all kinds of prior information and image information, also it can fusion between two images and even more. So It can make full use of image and related information. This thesis presents a new method of image fusion segmentation based on D-S theory, Markov random field and fuzzy theory, and then analyzes and discusses the basic probability assignment method of D-S theory.
Although the image information each are not identical which Markov random field segmentation and two-dimensional histogram fuzzy cluster segmentation uses, they are not full use of information and some information is not accurate and some information lose. We put the difference between Markov random field segmentation results and fuzzy clustering two-dimensional histogram segmentation results as a redundant image. Then we use D-S theory to make up for defects caused by incompleteness, inaccuracy and uncertainty of information.
The results of fusion and segmentation experiments of metallographic images, CT images, MR images and simulated images and show that the method of image fusion segmentation which based on D-S theory, Markov random field and fuzzy theory can improve the accuracy of segmentation and quality. And the method has a strong anti-noise capability. It's a automatic, stable method so it has extensive research and application value.
In a word, the development of image segmentation technology will promote the research and the application of materials science, it also promotes the further development of materials science. And it get more extensive application in various fields.
在材料科学中,图像分析处理技术可以对金属金相图像中不同组织成分进行分割、提取、识别及其应用。利用显微组织特征、大小、分布与各种机械性能、物理性能之间的有规律的函数关系,可观察了解显微组织的各种参数对材料性能的影响,根据晶粒尺寸大小可预测材料性能。定量地测量材料的显微结构(测出各种参数)对材料研究非常重要。通过合适的图像分割技术可以对金属金相图像的不同组织成分先进行精确分割,然后提取、分析和应用,因此金属图像分割技术的研究是一项非常重要和必要的基础性研究。金属图像分割作为金属图像处理的一个重要分支正向高质量、高效率、高稳定性和自动化方向发展。
本文从图像分割的理论、方法、技术和应用等方面入手进行了系统、全面、深入的研究,针对金属图像的特点及目前技术方法中的不足和遇到的困难,进行了深入的分析和探讨,提出了一系列相应的模型和改进方法,取得了良好的效果。
本文研究了马尔科夫随机场图像模型的理论和分割方法。采用统计决策和参数估计相结合的方法,根据最大后验概率(MAP)优化准则来确定目标函数,建立马尔科夫随机场图像分割模型。与其它分割算法的比较,可以看出一般图像分割方法仅考虑图像的灰度信息,而基于马尔科夫随机场模型的图像分割考虑灰度信息的同时,还考虑了空间邻域信息。金属图像分割实验的研究表明,马尔科夫随机场图像分割方法具有较好的分割结果。
本文对传统模糊C-均值聚类方法的图像分割技术进行了探讨,研究了模糊C-均值聚类方法中的聚类类别数、模糊加权指数、代截止误差ε等几个影响分割的重要参数,分析了模糊C-均值聚类进行图像分割存在的问题和困难。重点分析和讨论了模糊C-均值聚类方法进行图像分割过程中的重要参数：一是聚类类别数C,二是模糊加权指数m,三是迭代截止误差ε等相关参数,然后研究和分析了初始化聚类中心问题,进行了初始化聚类中心对图像分割结果影响的分析和讨论。传统模糊C-均值聚类图像分割算法对图像中的噪声较为敏感。研究发现考虑图像空间邻域信息,可以改善图像分割质量并具有抗噪声或消除噪声的作用。为此我们提出一种新的模糊C-均值聚类算法。
通过对图像灰度信息和空间邻域等信息的分析研究,利用图像的空间邻域关系,将一维直方图拓展为具有空间邻域关系的二维直方图,设计了一种简单有效的二维距离度量方法。提出聚类中心在像素值和邻域像素值二维方向上同时更新的新方法,通过建立包含邻域信息的新的聚类目标函数,得到基于邻域空间信息的二维FCM图像分割新算法。实验和分析结果表明,所提出的考虑邻域空间信息的模糊C-均值聚类图像分割新算法具有很强的抗噪性能力,有较好的分割结果。
本文分析和研究了大多数图像分割方法中关联信息均不能充分利用,针对图像自身及拓展信息浪费的问题。论文深入探讨了Dempster-Shafer理论,利用Dempster-Shafer理论具有整合多源信息的特点,分析了可应用的各种先验信息,讨论了图像间的信息融合问题,特别是两幅图像甚至多幅图像间的融合。分析了如何充分利用图像及相关信息。提出一种基于Dempster-Shafer理论、马尔科夫随机场、模糊理论的图像融合分割的新方法。并分析和讨论了Dempster-Shafer理论的基本概率赋值方法。
尽管马尔科夫随机场分割和二维直方图模糊聚类分割所用的图像信息比一般分割方法多,且各不相同,但均不能完整利用信息,部分信息不准确且有信息损失。将马尔科夫随机场分割结果和模糊聚类的二维直方图分割结果做差值,提取差值图像作为冗余图像,应用Dempster-Shafer理论进行原始图像与冗余图像的信息融合分割,可以充分利用图像自身信息和不完整的信息、不准确的部分信息甚至是有缺陷的信息。模拟图像、CT工业图像、MR图像和实际金属图像的融合分割实验结果表明,所提出的基于马尔科夫随机场、模糊聚类和D-S理论的融合分割方法不仅适用于金属图像、工业CT图像,而且适用于MR图像。可以提高分割精度和质量,并具有很强的抗噪能力,是一种良好的自动、适应性强的分割方法,具有广泛的研究和应用价值。
总之,图像分割技术的发展,必将推动材料科学的研究与应用,推动材料科学的进一步发展,在各种学科领域中得到更广泛的应用。
Image processing and analysis techniques show the interdisciplinary potential in materials science, biomedical science, automatic control, aerospace and other fields. The study of application of image processing and analysis techniques in materials science, especially the study of image segmentation, not only is the basis of image processing and analysis, was the basis for various image processing method and application. And its result provides a firm basis for materials science research and development, so it has important research foreground and widespread application value.
In materials science, we can segment metal metallographic image to extract and discern different components through image processing and analysis techniques. We can observe the influence of microstructural parameters on the properties of materials, and to predict material properties according to the grain size, using the characteristics, size, distribution of microstructure and a regular functional relationship between various mechanical properties and physical properties. It is very important to measure quantitatively and to seek out various parameters. We can segment the different components of metal metallographic image to extract and apply them through image segmentation techniques, so study of metal image segmentation is a very important and basic research. Metal image segmentation as an important branch of metal image processing is developing toward high quality, high efficiency, high reliability and automation.
This thesis has conducted systematic, comprehensive and in-depth research from the aspects of image segmentation theory, methods, techniques and applications. This thesis makes a deep analysis and discussion according to the characteristics of the metal image and the shortage of the technology and methods and the difficulties that they have ever met. And also makes a series of models and improved methods which achieved good results.
This thesis studies on the theory of Markov random field image model and segmentation method. This thesis uses Statistical dec ision-making and parameter estimation method of combination and according to the maximum a posteriori (MAP) optimization criterion determines the objective function. Finally creates a Markov random field image segmentation model. Compared with other segmentation algorithms, we can see that the general image segmentation methods only consider image gray level information, however, based segmentation of Markov random field model not only consider the gray-scale information but also takes into account the spatial information. The metal image segmentation experiments show that Markov random field image segmentation method has better segmentation results.
This thesis has conducted in-depth research and analysis of fuzzy C-means clustering image segmentation algorithm. This thesis studies the selection of initial parameters and also analyes the problems existing in the fuzzy C-means clustering image segmentation algorithm. This thesis focuses on the influence of and initial cluster centers the relevant parameters such as clustering category number, fuzzy weighted index and iterative cut-off error on image segmentation results.
Fuzzy C-means clustering image segmentation algorithm is more sensitive to noise. To solve this problem, we propose a new fuzzy C-means clustering algorithm.
Through analysis of the image gray information and spatial information, we take advantage of image space neighborhood relations to expand the one-dimensional histogram to the two-dimensional histogram with spatial neighborhood relations. Then we design a simple distance measurement method. We proposed to set up the new cluster objective function which includes neighborhood information, through the distance measurement between the pixel value of clustering center and the value of neighborhood pixels. Eventually we can get fuzzy C-means clustering image segmentation algorithm with neighborhood spatial information. The segmentation and analysis results of metal images and simulated images show that the fuzzy C-means clustering image segmentation algorithm with neighborhood spatial information has a strong noise immunity ability and good segmentation results.
According to the problem that a lot of relevant information is wasted because of not being utilized fully in common methods of image segmentation, this thesis has a deep analysis of the D-S theory, D-S theory has the characteristics of integration of multi-source information. It can fusion between all kinds of prior information and image information, also it can fusion between two images and even more. So It can make full use of image and related information. This thesis presents a new method of image fusion segmentation based on D-S theory, Markov random field and fuzzy theory, and then analyzes and discusses the basic probability assignment method of D-S theory.
Although the image information each are not identical which Markov random field segmentation and two-dimensional histogram fuzzy cluster segmentation uses, they are not full use of information and some information is not accurate and some information lose. We put the difference between Markov random field segmentation results and fuzzy clustering two-dimensional histogram segmentation results as a redundant image. Then we use D-S theory to make up for defects caused by incompleteness, inaccuracy and uncertainty of information.
The results of fusion and segmentation experiments of metallographic images, CT images, MR images and simulated images and show that the method of image fusion segmentation which based on D-S theory, Markov random field and fuzzy theory can improve the accuracy of segmentation and quality. And the method has a strong anti-noise capability. It's a automatic, stable method so it has extensive research and application value.
In a word, the development of image segmentation technology will promote the research and the application of materials science, it also promotes the further development of materials science. And it get more extensive application in various fields.
引文
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