基于Demon算法的乳房核磁共振图像配准

为了提高乳房动态核磁共振成像中不同时间点序列图像的配准速度,提出了将快速的Demon非刚性配准算法与图像强度校正相结合的乳房图像配准模型。Demon算法本身基于图像强度变化计算变形参数,因此不适于强度动态增强的图像配准。首先根据乳房组织的信号增强模型应用多项式理论校正待配准图像的强度,再应用Demon算法进行几何变形配准。实验表明该算法在精度上远优于直接使用Demon算法,在与自由形变配准算法具有几乎相同配准精度的前提下,明显提高了配准速度。     

星载InSAR非相干图像对精确配准

为了解决星载InSAR非相干图像配准中不同区域对应不同形变参数的问题,将几种传统的SAR图像配准和光学图像配准的方法有机结合,提出了一种基于概略配准、Harris角点检测、相关系数匹配、Delaunay三角网剖分插值映射的InSAR复图像精确配准方法。通过仿真验证,该方法能够适用相干和非相干的InSAR复图像。配准精度达到0.1像素级。     

基于空间加权互信息的非刚性医学图像配准

互信息作为相似性测度标准在医学图像配准领域应用广泛,尤其是在刚性配准研究中,能实现较高配准精度和非常稳健的配准效果。然而由于非线性形变的复杂性,在非刚性配准领域,对基于互信息的配准方法进行深入研究势在必行。提出的方案采用基于B样条的自由变换模型模拟医学图像中解剖结构的非线性形变,同时根据变换模型,考虑空间信息对配准效果的影响,以空间加权的方式对互信息方法进行改进。使用对大规模参数优化效果较好的LBFGS方法对配准参数进行优化,编程实现程序,并通过实验对效果进行验证和分析。实验结果显示,该方案配准精度明显优于传统的互信息方法。     

梯度与曲率变形力相结合的图像配准方法——大形变非刚性图像配准

Active Demons算法是Demons算法的改进形式,其将形变配准视作扩散问题,利用牛顿作用力与反作用力思想,仅依靠梯度信息确定浮动图像的位移,在处理大形变配准问题时存在配准精度不高的弊病。将等照度线曲率作为一个控制形变的驱动力因素引入Active Demons扩散方程,建立了一个具有梯度与曲率双重驱动力相结合的非线性扩散模型（Active G&C model）,并在Active G&C模型应用于大形变图像配准的算法实现过程中加入多分辨率策略,以提高大形变图像的配准精度。实验结果表明,这一模型较经典的Active Demons算法具有更好的配准性能。     

基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用

计划CT图像与锥形束CT (Cone beam CT, CBCT)图像的配准是基于CBCT图像引导放射治疗(Image guided radiation therapy, IGRT)系统中实现自适应放疗(Adaptive radiation therapy, ART)的关键部分.边缘保护多尺度空间基于非线性扩散模型,可以为基于互信息的配准提供丰富的空间位置信息.为了提高系统中配准算法性能,本文提出了一种基于边缘保护尺度空间与自由形变模型(Free form deformation, FFD)相结合的多尺度形变配准方法.我们采用了在不同的尺度上根据精细程度选择相应的自由形变控制点数,由粗及精地恢复形变.同时, 提出了自动获取非线性扩散模型中平滑参数λ的方法来实现全自动配准. 实验结果表明,本文提出的方法用于基于CBCT的图像引导放射系统时,可实现日常放疗时的CBCT图像和计划CT图像准确且快速的配准.通过获得的形变域,可实现CBCT图像肿瘤靶区、危及器官(Organ at risk, OR)和等剂量线的自动勾画,从而实现剂量体积直方图(Dose volume histograms, DVH)分析.最终实现了放疗计划从CT到CBCT的自适应转移.     

非介入式手术导航中医学图像配准算法

提出一种用于非介入手术导航中基于自由变形模型的多模态医学图像非刚性配准方法,对术前MRI/CT和术中超声图像中都可见的血管结构进行配准.当图像对准时,一种图像中的血管中心点对应着另一种图像下灰度脊点;对于全局变换采用刚性变换、局部形变采用基于函数控制B样条的自由变形模型来描述;采用遗传算法和共轭梯度法相结合达到最小化目标函数.将文中算法应用于体模和临床数据,在配准精度和收敛速度上都取得了良好的效果.     

基于深度卷积特征光流的形变医学图像配准算法

光流法是一种基于光流场模型的重要而有效的形变配准算法。针对现有光流法所用特征质量不高使得配准结果不够准确的问题，将深度卷积神经网络特征和光流法相结合，提出了基于深度卷积特征光流（DCFOF）的形变医学图像配准算法。首先利用深度卷积神经网络稠密地提取图像中每个像素所在图像块的深度卷积特征，然后基于固定图像和浮动图像间的深度卷积特征差异求解光流场。通过提取图像的更为精确和鲁棒的深度学习特征，使求得的光流场更接近真实形变场，提升了配准精度。实验结果表明，所提算法能够更有效地解决形变医学图像配准问题，其配准精度优于Demons算法、尺度不变特征变换(SIFT) Flow算法以及医学图像专业配准软件Elastix。     

基于多空间信息提取的级联分块配准模型

在基于深度学习的医学图像配准中, 当医学图像中包含多种组织类型时, 不同组织之间结构的不同可能会导致网络配准的精度下降, 特别是在复杂形变区域, 如组织的交界处和病变区域, 精准的配准变得更加困难, 现有的配准算法对复杂形变区域的关注度不高, 导致配准精度较低. 同时现有的配准网络无法同时对图像的局部和全局空间信息进行捕获, 导致网络的鲁棒性不够, 在迁移到其他器官的配准工作中时配准准确率低. 为了解决上述的问题, 本文提出一种基于多空间信息提取的级联分块配准模型, 本模型可以有效利用输入图像的局部和空间信息, 并通过分块融合的技术, 将医学图像进行分块并依次对每个图像进行精细配准生成相应的形变场块, 在模型的最后阶段将生成的形变场块进行融合还原, 以增强网络对局部复杂形变区域的配准强度. 实验结果表明, 所提方法不仅在脑部配准上有所提升, 并且在其他人体部位的配准中也有较好的表现, 提高了医学图像配准的准确性和可靠性, 为临床医生提供更好的诊断和治疗支持.     

基于全参数的分层搜索图像配准

在图像的超分辨率复原过程中,往往需要处理数量较多的模糊形变图像,这就需要进行图像配准,而目前常见的图像配准算法在配准精度和速度上常常不能达到令人满意的程度.为了二者都能达到实际需求,本文通过对仿射变换模型和配准算法的研究,提出快速、精确的全参数分层搜索算法,并用实验进行了仿真.     

基于深度学习的肝脏CT-MR图像无监督配准

多模态配准是医学图像分析中的关键环节,在肝癌辅助诊断、图像引导的手术治疗中具有重要作用。针对传统的迭代式肝脏多模态配准计算量大、耗时长、配准精度低等问题,提出一种基于多尺度形变融合和双输入空间注意力的无监督深度学习配准算法。利用多尺度形变融合框架提取不同分辨率的图像特征,实现肝脏的逐阶配准,在提高配准精度的同时避免网络陷入局部最优。采用双输入空间注意力模块在编解码阶段融合不同水平的空间和文本信息提取图像间的差异特征,增强特征表达。引入基于邻域描述符的结构信息损失项进行网络迭代优化,不需要任何先验信息即可实现精确的无监督配准。在临床肝脏CT-MR数据集上的实验结果表明,与传统的Affine、Elastix、VoxelMorph等算法相比,该算法达到最优的DSC值和TRE值,分别为0.926 1±0.018 6和6.39±3.03 mm,其平均配准时间为0.35±0.018 s,相比Elastix算法提升了近380倍,能准确地提取特征及估计规则的形变场,具有较高的配准精度和较快的配准速度。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.