体绘制多维传递函数.doc
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体绘制多维传递函数,摘要目前，体绘制已成为三维数据场可视化的重要技术手段之一，在科学计算和工程领域受到人们的普遍重视和广泛应用。体绘制的传递函数将三维体数据的体素值映射成光学成像参数，直接决定了三维重建图像的质量。但长期以来，体绘制的传递函数的设计问题一直没有得到很好的解决，成为制约体绘制技术发展和应用的瓶颈，也是近年来体绘制研究的关键技...
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摘 要
目前，体绘制已成为三维数据场可视化的重要技术手段之一，在科学计算和工程领域受到人们的普遍重视和广泛应用。体绘制的传递函数将三维体数据的体素值映射成光学成像参数，直接决定了三维重建图像的质量。但长期以来，体绘制的传递函数的设计问题一直没有得到很好的解决，成为制约体绘制技术发展和应用的瓶颈，也是近年来体绘制研究的关键技术和热点问题。本论文在研究分析当前体绘制传递函数设计方法的基础上，利用聚类算法和极端学习机来指导和优化体绘制传递函数的设计过程，以实现设计过程的智能化和自动化。本论文的主要工作如下：
(1)提出了一种基于K均值聚类算法的体绘制多维传递函数设计方法，在利用灰度-梯度直方图分析体数据内部结构信息的基础上，应用K均值聚类算法对整个体数据进行聚类分类，对属于不同聚类中的体素值和不透明度进行伪彩色映射，实现体数据与彩色编码的转换关系。实验表明，该方法所设计的体绘制传递函数能够揭示体数据的内部结构关系，具有算法简洁、计算效率高、操作方便、重建的三维图像逼真、质量高等优点。
(2)在深入分析神经网络应用于体绘制传递函数设计的可行性和有效性的基础上，提出了一种基于极端学习机的体绘制传递函数设计方法，将极端学习机应用于传递函数的设计，并通过一个可供用户交互操作的界面，利用极端学习机对体数据进行分类，并对不同的类赋予不同的颜色值和不透明度，达到按类进行体绘制的效果。实验表明，该方法设计的传递函数能够有效的分辨不同的物质，绘制的图像清晰，学习效率与BP神经网络相比有大幅提高。
关键词 体绘制；传递函数；灰度-梯度直方图；K均值聚类；极端学习机
Abstract
Currently, volume rendering has become one of the important technical methods for three-dimensional data visualization.And it has been widely valued and used in scientific computing and engineering. Transfer function of volume rendering maps voxel value of volume data to the optical imaging parameters, which directly determines the three-dimensional reconstructed images’ quality. But for a long time, the design problem of transfer function of volume rendering had never been satisfactorily resolved, which became the bottleneck of the development and application of volume rendering and has been the key technology and hot issue of volume rendering in recent years. Based on researching and analysising the current method of transfer function design of volume rendering, this thesis used clustering algorithm and extreme learning machine to guide and optimize the the process of transfer function design of volume rendering, which can make the design process intelligent and automatic. The main work of this paper is as follows:
(1) This paper proposed a novel method of multi-dimensional transfer function design of volume rendering. Based on anglicizing the internal structure of volume data by the scalar and the gradient magnitude histogram, all the volume data was classified using K-means clustering algorithm. Then, the volume data belonging to different clustering was pseudo-color mapped for the transformation between volume data and color coding. The experimental results show that transfer function designed by the proposed method can reveal the internal structures of volume data. And our method has the advantages of simple algorithm,high computational efficiency and convenient operation.The reconstructed three-dimensional images are more fidelity and have higher quality.
(2) Based on the depth analysis of the feasibility and effectiveness of using neural network in the field of transfer function of volume rendering, the paper proposed a new method of transfer function design of volume rendering based on extreme learning machine. The method applied extreme learning machine to transfer function design. First all the volume data was classified using extreme learning machine through a interaction user interface. Then, the classified volume data was mapped to different color and opacity.The experimental results show that transfer function designed by the proposed method can effectively separate the different substances,and the reconstructed three-dimensional images are relatively clear. Compared to BP neural network, the learning rate of our method has great increse.
Key words: volume rendering; transfer function; scalar and gradient magnitude histogram; K means clustering; extreme learning machine
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 研究的目的和意义 1
1.2 国内外的研究现状和发展趋势 3
1.3 论文的主要内容和组织结构 4
第2章 体绘制的传递函数 6
2.1 概述 6
2.2 体绘制的传递函数 12
2.2.1 传递函数的定义 12
2.2.2 传递函数的分类 13
2.3 体绘制传递函数的主要设计方法 16
2.3.1 手动调节法 17
2.3.2 图像中心法 17
2.3.3 数据中心法 18
2.3.4 对象中心法 18
2.4 本章小结 19
第3章 基于K均值聚类算法的多维传递函数设计 20
3.1 概述 20
3.2 灰度-梯度直方图 20
3.2.1 梯度与物质边界模型 20
3.1.2 灰度-梯度直方图 21
3.3 K均值聚类算法 23
3.3.1 K均值聚类算法原理 24
3.3.2 K均值聚类算法步骤 25
3.4 基于K均值聚类算法的传递函数设计 26
3.5 实验结果与分析 28
3.6 本章小结 30
第4章 基于极端学习机的多维传递函数设计 31
4.1 概述 31
4.2人工神经..
目前，体绘制已成为三维数据场可视化的重要技术手段之一，在科学计算和工程领域受到人们的普遍重视和广泛应用。体绘制的传递函数将三维体数据的体素值映射成光学成像参数，直接决定了三维重建图像的质量。但长期以来，体绘制的传递函数的设计问题一直没有得到很好的解决，成为制约体绘制技术发展和应用的瓶颈，也是近年来体绘制研究的关键技术和热点问题。本论文在研究分析当前体绘制传递函数设计方法的基础上，利用聚类算法和极端学习机来指导和优化体绘制传递函数的设计过程，以实现设计过程的智能化和自动化。本论文的主要工作如下：
(1)提出了一种基于K均值聚类算法的体绘制多维传递函数设计方法，在利用灰度-梯度直方图分析体数据内部结构信息的基础上，应用K均值聚类算法对整个体数据进行聚类分类，对属于不同聚类中的体素值和不透明度进行伪彩色映射，实现体数据与彩色编码的转换关系。实验表明，该方法所设计的体绘制传递函数能够揭示体数据的内部结构关系，具有算法简洁、计算效率高、操作方便、重建的三维图像逼真、质量高等优点。
(2)在深入分析神经网络应用于体绘制传递函数设计的可行性和有效性的基础上，提出了一种基于极端学习机的体绘制传递函数设计方法，将极端学习机应用于传递函数的设计，并通过一个可供用户交互操作的界面，利用极端学习机对体数据进行分类，并对不同的类赋予不同的颜色值和不透明度，达到按类进行体绘制的效果。实验表明，该方法设计的传递函数能够有效的分辨不同的物质，绘制的图像清晰，学习效率与BP神经网络相比有大幅提高。
关键词 体绘制；传递函数；灰度-梯度直方图；K均值聚类；极端学习机
Abstract
Currently, volume rendering has become one of the important technical methods for three-dimensional data visualization.And it has been widely valued and used in scientific computing and engineering. Transfer function of volume rendering maps voxel value of volume data to the optical imaging parameters, which directly determines the three-dimensional reconstructed images’ quality. But for a long time, the design problem of transfer function of volume rendering had never been satisfactorily resolved, which became the bottleneck of the development and application of volume rendering and has been the key technology and hot issue of volume rendering in recent years. Based on researching and analysising the current method of transfer function design of volume rendering, this thesis used clustering algorithm and extreme learning machine to guide and optimize the the process of transfer function design of volume rendering, which can make the design process intelligent and automatic. The main work of this paper is as follows:
(1) This paper proposed a novel method of multi-dimensional transfer function design of volume rendering. Based on anglicizing the internal structure of volume data by the scalar and the gradient magnitude histogram, all the volume data was classified using K-means clustering algorithm. Then, the volume data belonging to different clustering was pseudo-color mapped for the transformation between volume data and color coding. The experimental results show that transfer function designed by the proposed method can reveal the internal structures of volume data. And our method has the advantages of simple algorithm,high computational efficiency and convenient operation.The reconstructed three-dimensional images are more fidelity and have higher quality.
(2) Based on the depth analysis of the feasibility and effectiveness of using neural network in the field of transfer function of volume rendering, the paper proposed a new method of transfer function design of volume rendering based on extreme learning machine. The method applied extreme learning machine to transfer function design. First all the volume data was classified using extreme learning machine through a interaction user interface. Then, the classified volume data was mapped to different color and opacity.The experimental results show that transfer function designed by the proposed method can effectively separate the different substances,and the reconstructed three-dimensional images are relatively clear. Compared to BP neural network, the learning rate of our method has great increse.
Key words: volume rendering; transfer function; scalar and gradient magnitude histogram; K means clustering; extreme learning machine
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 研究的目的和意义 1
1.2 国内外的研究现状和发展趋势 3
1.3 论文的主要内容和组织结构 4
第2章 体绘制的传递函数 6
2.1 概述 6
2.2 体绘制的传递函数 12
2.2.1 传递函数的定义 12
2.2.2 传递函数的分类 13
2.3 体绘制传递函数的主要设计方法 16
2.3.1 手动调节法 17
2.3.2 图像中心法 17
2.3.3 数据中心法 18
2.3.4 对象中心法 18
2.4 本章小结 19
第3章 基于K均值聚类算法的多维传递函数设计 20
3.1 概述 20
3.2 灰度-梯度直方图 20
3.2.1 梯度与物质边界模型 20
3.1.2 灰度-梯度直方图 21
3.3 K均值聚类算法 23
3.3.1 K均值聚类算法原理 24
3.3.2 K均值聚类算法步骤 25
3.4 基于K均值聚类算法的传递函数设计 26
3.5 实验结果与分析 28
3.6 本章小结 30
第4章 基于极端学习机的多维传递函数设计 31
4.1 概述 31
4.2人工神经..
