【作者】 宫延新；

【导师】 孙丰荣；

【作者基本信息】 山东大学 ， 通信与信息系统， 2007， 硕士

【摘要】 体绘制技术被证实是揭示三维数据结构信息的有效工具。此前的研究主要是围绕如何提高体绘制质量和体绘制速度的算法，直到最近研究重心才放在转换函数的构造上。传统的一维转换函数依靠数据的标量值来分类各组织，但是它对于用户使用不直观，效率低，并且当两个组织标量值(灰度值)接近的时候，不能很好的分类。近年来，许多研究使用二维和三维的转换函数，同时考虑一阶和二阶导数。附加的导数信息允许精确的分类，可以很好的显示用户感兴趣的区域。然而，其他的属性，比如纹理和位置信息，没有考虑进来。在体绘制中寻找一个高效直观的转换函数来进行数据理解，仍然是一个研究的难点。转换函数的维数越高，结果就越好。因为高维的转换函数考虑了更多体素的不同属性。在临床的医学实践中，对于三维数据中的感兴趣区域，不同的场合有不一样的需要。因此在设计转换函数的时候加入用户的交互信息就非常重要。通过用户的交互，使得设计的转换函数满足临床的实际需要，显示医生感兴趣的区域。因此在医学体绘制的转换函数设计中，需要把多维转换函数和用户交互结合起来。本文就设计了一个用户交互的接口，用于设计多维转换函数。首先用户先确定需要显示的组织结构的类别，然后在体数据中随机选取几个切片，用户在切片上选择相应结构类别的采样点数据，最后用机器学习的方法根据采样点数据进行多维转换函数的自动训练。计算机辅助检测／诊断技术(CAD)在临床医学中的应用是近年来研究的热点，CAD对临床医师起着“第二双眼”的作用，它在医生读片后执行二次辅助诊断，同时可以三维分析检查，自动检测可疑区域，减少医生的肉眼观察疏漏。CAD在肺癌诊断中的应用因为其巨大的理论价值和使用价值吸引了大量的研究力量。最近，国外已经出现了一些商用的肺癌CAD系统。但是很多问题还没有解决，需要进一步研究。文中就尝试把设计的转换函数用于肺癌的检测中，并取得了较好的效果。本文使用BP神经网络设计高维的转换函数，通过用户的交互，以及神经网络训练，达到自动分类识别显示的效果，使显示的结果是用户感兴趣的区域，并去掉不相关区域。同时，把设计的转换函数应用于肺癌CAD中，较好地区分了肺部各组织的关系，减轻了临床医生的工作负担，提高读片的效率，对于下一步肺结节的提取具有重要的指导作用。更多还原

【Abstract】 Nowadays direct volume rendering has been proved that it was an effective method to discover the detailed information of three-dimensional objects. The former researcher, however, committed themselves to generate the algorithms that enhancing the quality of rendering and accelerating the process of rendering, until these days the focus was drawn into the research of constructing transfer functions.The traditional one-dimensional transfer function for volume rendering only considers a voxel’s scalar value; that is, there is a direct mapping of transparency to scalar value. Designing this type of transfer function is often not intuitive for the users and less effective. Recently, there has been research into the use of 2D and 3D transfer functions which take more information into account such as first- and second-order gradient. The additional derivative information allows more refined classification, and works well when a user wants to visualize the region of interest. However, other properties such as textures and position are not taken into account.The implement of the CAD in medicine practice spend a lot of research effort. CAD can help doctors to do the second aided-diagnosis, evaluate the inspection results in 3D visualization and detect the suspicious region to reduce the doctor’s mistakes. Recently, a few commercial lung cancer CAD systems are present in abroad. Although the value of theory and application in these CAD systems is promising, there are many issues to address.Finding a method for specifying these transfer function in an intuitive and efficient manner remains an important challenge in making volume visualization a more attractive tool for understanding data. Higher-dimensional transfer function can lead to better results since it uses more properties for each voxel. In medical routine, the aim of visualization is highly dependent on the application. Thus, in order to achieve a successful volume classification in a reasonable period of time, part of the knowledge of an expert user must be introduced in the transfer function design process. So it’s the best to combine the high-dimensional transfer function with a user interface.We present a new approach to the transfer function problem, using a BP network to abstract high-dimensional transfer function from the user. The user’s interaction consists of specifying regions of interest by sampling on a number of slices from the volume data. And then we apply the transfer function to the lung cancer CAD to get a better result of classification of chest volume data, alleviating the jobs in the clinical inspection. It is instructive to the detecting of the nodules in the future work.更多还原