【作者】 楚春雨；

【导师】 刘露；

【作者基本信息】 哈尔滨理工大学 ， 控制理论与控制工程， 2010， 硕士

【摘要】 医学图像处理与分析技术借助计算机图像处理与分析、计算机图形学、虚拟现实和计算机网络等技术使得医学影像诊疗水平大大提高,日益受到人们的重视,成为国内外研究和应用的热点,并形成了一门新兴的、发展迅速的交叉学科。随着医学成像技术的发展,医学图像的精度越来越高,数据量也越来越大,因此医学图像处理就需要一种存储容量大运算能力强的处理环境。而EGEE(The Enabling Grids for E-sciencE)恰好能够满足这种需求。因此,研究基于EGEE的医学图像处理技术具有重要意义。本论文主要研究了以下三方面内容：设计并开发了基于EGEE的医学图像处理平台,命名为HBB,它基于ITK、VTK和QT而开发,集成了图像滤波、分割和三维可视化等算法,实现了一个功能全面、接口丰富、可扩展性强的医学图像处理平台,可做为一个算法研发的基础开发平台。针对经典区域增长算法中生长规则确定的困难和单纯使用支持向量机分割速度慢的问题,提出了一种支持向量机与区域增长相结合的图像并行分割方法。首先,从已知分割结果的图像中选取一定数量的目标区域与非目标区域样本点做为支持向量机分类器的训练样本并训练支持向量机,然后利用训练好的支持向量机自动寻找种子点并进行区域增长,在区域增长过程中使用支持向量机分类器作为增长规则,最后,针对边缘和噪声像素点进行必要的后处理。测试实验当使用16结点时分割512×512×64的图像所用时间为132秒,获得了较好的分割效果和较快的分割速度且能实现自动分割,表明所提出的方法是有效可行的。在医学图像三维重建方面,提出了一种基于EGEE的并行三维重建策略。首先,把任务划分为若干份并分发给子进程；其次,每个子进程采用MC算法提取等值面,将三角面连接成三角带并将结果返回给主进程；最后,主进程将各子进程返回的结果进行合并并显示。实验测试当使用4结点时重建速度提高了约66%,表明该并行三维重建方法能够有效的提高三维重建速度且重建效果良好。更多还原

【Abstract】 The medical image processing and analysis has improved the level of diagnosis and treat greatly and has been paid more attention to by people with the help of technologies of computer image processing and analysis, computer graphics, virtual reality, computer network and so on. It has become a research and application hotspot at home and abroad, and former a new crossover subject with rapid development.With the development of the medical imaging technology, the accuracy of medical images is higher and higher, data volume is larger and larger at the same time. Therefore, medical image processing would require a handling environment which has a large storage capacity and a strong computing capable. The EGEE(The Enabling Grids for E-sciencE) just to meet this demand. So the study of medical image processing technology which based on EGEE has great significance.This thesis includes contents of three main aspects:Medical image processing platform was designed and developed based on ITK, VTK and QT. It integrated image filtering, segmentation and three-dimensional visualization algorithm and implements a full-featured, interface rich, scalable platform for medical image processing, Can be used as a basis for algorithm development platform for R & D.In order to solve the difficult of determine the growth rules in conventional regional growth algorithm and the slowly of support vector machine segmentation algorithm, an image segmentation method combined support vector machine and regional growth was proposed. Firstly, select a certain numbers of sample point from target area and non-target area and train the support vector machine classification, then use the trained classification search seed point and regional growing, the support vector machine classification is used as growth rules, the last, some necessary retrogressing were used for the edge and noise. The experimental results show that this algorithm is feasible and it performs better than conventional region growth segmentation algorithm and faster then conventional support vector machine segmentation algorithm.In the medical image 3D reconstruction, proposed a 3D reconstruction parallel strategy based on EGEE. First, the task is divided into a number of copies and distributed to the sub-processes; secondly, each sub-process using MC algorithm extraction isosurface and linked triangulated surfaces into Triangle and back the results to the main process; finally, the main process will be merge all results of sub-processes returned and displayed. Experimental tests show that the 3D reconstruction parallel method can effectively improve the speed of three-dimensional reconstruction and rebuilding good effect.更多还原