医学影像处理与分析开发包MITK的设计与实现

随着VTK(visualization ToolKit)和ITK(insight segmentation and registration ToolKit)两个软件开发包的成功,医学影像领域内的研究人员越来越重视本领域内的软件包的开发问题.介绍了所开发的集成化的三维医学影像处理与分析开发包MITK(medical imaging ToolKit),其目的主要是提供一个一致的框架,整合医学图像分割、配准、可视化等功能.给出了MITK的设计目标、整体框架和关键技术的实现,以及一些应用实例以展示MITK的功能.希望MITK能成为本领域内研究开发人员的另外一个可选择的开发包.     

Efficient segmentation of leaves in semi-controlled conditions

We present a study on segmentation of leaf images restricted to semi-controlled conditions, in which leaves are photographed against a solid light-colored background. Such images can be used in practice for plant species identification, by analyzing the distinctive shapes of the leaves. We restrict our attention to segmentation in this semi-controlled condition, providing us with a more well-defined problem, which at the same time presents several challenges. The most important of these are: the variety of leaf shapes, inevitable presence of shadows and specularities, and the time constraints required by interactive species identification applications. We evaluate several popular segmentation algorithms on this task. Different datasets of leaf images are used, with manually segmented images serving as ground truth for quantitative comparisons. We observe that many of the methods are not immediately applicable: they are either too slow or would require that important modifications be introduced. We thus present extensions to our previously published segmentation method, which are able to improve its performance. The previous approach was based on pixel clustering in color space. Our extensions introduce a graph cut step and the use of a training set of manual segmentations in order to adjust important parameters of the method. The new method is fast enough for an interactive application, while producing state-of-the-art results.     

Graph-cut based interactive segmentation of 3D materials-science images

Segmenting materials’ images is a laborious and time-consuming process, and automatic image segmentation algorithms usually contain imperfections and errors. Interactive segmentation is a growing topic in the areas of image processing and computer vision, which seeks to find a balance between fully automatic methods and fully-manual segmentation processes. By allowing minimal and simplistic interaction from the user in an otherwise automatic algorithm, interactive segmentation is able to simultaneously reduce the time taken to segment an image while achieving better segmentation results. Given the specialized structure of materials’ images and level of segmentation quality required, we show an interactive segmentation framework for materials’ images that has three key contributions: (1) a multi-labeling approach that can handle a large number of structures while still quickly and conveniently allowing manual addition and removal of segments in real-time, (2) multiple extensions to the interactive tools which increase the simplicity of the interaction, and (3) a web interface for using the interactive tools in a client/server architecture. We show a full formulation of each of these contributions and example results from their application.     

移动混合开发框架

针对现有混合开发框架在开发便利性和应用性能方面的不足,研究了以HTML5开发为基础的移动混合开发框架的问题.结合HTML5跨平台特性、打包机制、多窗口管理机制、原生插件开发技术等,实现了一套自有的混合开发框架.本混合开发框架为开发带来了便利,提升了开发效率,同时,混合引擎中独特实现的Widget应用管理以及窗口管理模式也改善了混合应用的性能,提升了用户体验.我公司实现的企业级移动解决方案表明该框架性能较好,易于使用,具有较好的商业价值.     

Comparison of four freely available frameworks for image processing and visualization that use ITK

Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VoIView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VoIView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application     

Efficient Impostor Manipulation for Real-Time Visualization of Urban Scenery

Urban environments present unique challenges to interactive visualization systems, because of the huge complexity of the geometrical data and the widely varying visibility conditions. This paper introduces a new framework for real-time visualisation of such urban scenes. The central concept is that of a dynamic segmentation of the dataset, into a local three-dimensional model and a set of impostors used to represent distant scenery. A segmentation model is presented, based on inherent urban structure. A new impostor structure is introduced, derived from the level-of-detail approach. Impostors combine three-dimensional geometry to correctly model large depth discontinuities and parallax, and textures to rapidly display visual detail. We present the algorithms necessary for the creation of accurate and efficient three-dimensional impostors. The implementation of our algorithms allows interactive navigation in complex urban databases, as required by many applications.     

Semiautomatic segmentation with compact shape prior

In recent years, interactive methods for segmentation are increasing in popularity due to their success in different domains such as medical image processing, photo editing, etc. We present an interactive segmentation algorithm that can segment an object of interest from its background with minimum guidance from the user, who just has to select a single seed pixel inside the object of interest. Due to minimal requirements from the user, we call our algorithm semiautomatic. To obtain a reliable and robust segmentation with such low user guidance, we have to make several assumptions. Our main assumption is that the object to be segmented is of compact shape, or can be approximated by several connected roughly collinear compact pieces. We base our work on the powerful graph cut segmentation algorithm of Boykov and Jolly, which allows straightforward incorporation of the compact shape constraint. In order to make the graph cut approach suitable for our semiautomatic framework, we address several well-known issues of graph cut segmentation technique. In particular, we counteract the bias towards shorter segmentation boundaries and develop a method for automatic selection of parameters. We demonstrate the effectiveness of our approach on the challenging industrial application of transistor gate segmentation in images of integrated chips. Our approach produces highly accurate results in real-time.     

Graph-based tools for microscopic cellular image segmentation

We propose a framework of graph-based tools for the segmentation of microscopic cellular images. This framework is based on an object oriented analysis of imaging problems in pathology. Our graph tools rely on a general formulation of discrete functional regularization on weighted graphs of arbitrary topology. It leads to a set of useful tools which can be combined together to address various image segmentation problems in pathology. To provide fast image segmentation algorithms, we also propose an image simplification based on graphs as a pre processing step. The abilities of this set of image processing discrete tools are illustrated through automatic and interactive segmentation schemes for color cytological and histological images segmentation problems.     

Flow-inspector: a framework for visualizing network flow data using current web technologies

New web technologies led to the development of browser applications for data analysis. Modern browser engines allow for building interactive real-time visualization applications that enable efficient ways to understand complex data. We present Flow-Inspector, a highly interactive open-source web framework for visualizing network flow data using latest web technologies. Flow-Inspector includes a backend for processing and storing large-scale network flow data, as well as a JavaScript-based web application capable to display and manipulate traffic information in real-time. This work provides operators with a toolkit to analyze their networks and enables the scientific community to create new and innovative visualizations of traffic data with an extensible framework. We demonstrate the applicability of our approach by implementing several different visualization components that help to identify topological characteristics in network flows.     

A unified paradigm for scalable multi-projector displays

We present a general framework for the modeling and optimization of scalable multi-projector displays. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors without manual adjustment. When the projectors are tiled, we show that our framework automatically produces blending maps that outperform state-of-the-art projector blending methods. When all the projectors are superimposed, the framework can produce high-resolution images beyond the Nyquist resolution limits of component projectors. When a combination of tiled and superimposed projectors are deployed, the same framework harnesses the best features of both tiled and superimposed multi-projector projection paradigms. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution video at real-time interactive frame rates achieved on commodity graphics platforms. This work allows for inexpensive, compelling, flexible, and robust large scale visualization systems to be built and deployed very efficiently.     

Partition-distance methods for assessing spatial segmentations of images and videos

The primary goal of the research on image segmentation is to produce better segmentation algorithms. In spite of almost 50 years of research and development in this field, the general problem of splitting an image into meaningful regions remains unsolved. New and emerging techniques are constantly being applied with reduced success. The design of each of these new segmentation algorithms requires spending careful attention judging the effectiveness of the technique.This paper demonstrates how the proposed methodology is well suited to perform a quantitative comparison between image segmentation algorithms using a ground-truth segmentation. It consists of a general framework already partially proposed in the literature, but dispersed over several works. The framework is based on the principle of eliminating the minimum number of elements such that a specified condition is met. This rule translates directly into a global optimization procedure and the intersection-graph between two partitions emerges as the natural tool to solve it. The objective of this paper is to summarize, aggregate and extend the dispersed work. The principle is clarified, presented striped of unnecessary supports and extended to sequences of images. Our study shows that the proposed framework for segmentation performance evaluation is simple, general and mathematically sound.     

Leveraging Java applets: toward collaboration transparency in Java

Widespread use of the Internet is giving rise to the need for collaborative applications that link users at remote sites. Many toolkits support the development of collaboration-aware applications, those developed specifically for cooperative work by multiple users. Another approach is collaboration transparency, the collaborative use of applications originally developed for a single user. When the runtime environment supports collaboration transparency, an application programmer need not write new code to make an application collaborative. Thus, collaboration transparency leverages the existing base of single user applications by extending them to collaborative use. We review options for providing collaboration transparency. We also discuss how collaboration transparency can be incorporated into Sun Microsystems' Java, the most widely used vehicle for developing interactive World Wide Web applications     

判别割(Dcut)的图像分割及其快速分割算法

谱聚类算法在模式识别和图像分割中得到了广泛应用。谱聚类算法能在任意形状的样本空间上聚类且收敛于全局最优解。采用一个新的谱聚类算法Dcut进行图像分割。Dcut完全满足聚类算法的一般准则:类内样本间的相似度大,类间样本的相似度小,因此Dcut在图像分割方面比Ncut具有更好的分组性能。为了克服Dcut分割速度慢,提出基于子空间的Dcut(SDcut)和基于分块的SDcut(BSDcut)两种快速算法。SDcut和BSDcut这两种快速算法具有Dcut的分组性能的同时,降低了分割图像的计算复杂度。通过对纹理图像和真实图像的分割,验证了新算法的有效性。     

Fast High‐Dimensional Filtering Using the Permutohedral Lattice

Many useful algorithms for processing images and geometry fall under the general framework of high‐dimensional Gaussian filtering. This family of algorithms includes bilateral filtering and non‐local means. We propose a new way to perform such filters using the permutohedral lattice, which tessellates high‐dimensional space with uniform simplices. Our algorithm is the first implementation of a high‐dimensional Gaussian filter that is both linear in input size and polynomial in dimensionality. Furthermore it is parameter‐free, apart from the filter size, and achieves a consistently high accuracy relative to ground truth (> 45 dB). We use this to demonstrate a number of interactive‐rate applications of filters in as high as eight dimensions.     

局部颜色模型的交互式Graph-Cut分割算法

由于目前大多数交互式Graph-Cut分割算法很难达到精确分割且实时交互的效果.对此,提出一种基于局部颜色模型的改进算法.该算法利用Mean-Shift预分割,建立基于局部颜色模型的交互式分割框架,并将像素级的Graph-Cut算法转化为基于区域的算法进行快速求解.预分割之后的区域保持了原有图像的结构,不仅提高了采用局部颜色模型估计分布的准确性,而且基于区域Graph-Cut的算法明显降低了计算的复杂度.实验结果表明,改进后的算法不仅保证了分割的精确性,而且还达到了实时交互.     

基于MATLAB GUI图像处理系统的设计与实现

MATLAB作为算法开发及数据可视化的交互式工具,在数字图像处理方面应用广泛,并集成了具有多种算法的图像处理工具箱。本文基于MATLAB图像用户界面(GUI)和图像处理工具箱设计实现了一款图像处理系统,该系统可实现图像的运算、变换、分割、改善、形态学处理、压缩编码等内容,并通过综合实例阐述了本系统在图像识别中的应用,使图像处理可视化更加直观,便捷。多次实践表明,本系统具有良好的实用性、交互性、移植性,易于后期模式识别和机器视觉相关应用的扩展研究。     

Toolkit design for interactive structured graphics

Here, we analyze toolkit designs for building graphical applications with rich user interfaces, comparing polylithic and monolithic toolkit-based solutions. Polylithic toolkits encourage extension by composition and follow a design philosophy similar to 3D scene graphs supported by toolkits including JavaSD and Openlnventor. Monolithic toolkits, on the other hand, encourage extension by inheritance, and are more akin to 2D graphical user interface toolkits such as Swing or MFC. We describe Jazz (a polylithic toolkit) and Piccolo (a monolithic toolkit), each of which we built to support interactive 2D structured graphics applications in general, and zoomable user interface applications in particular. We examine the trade offs of each approach in terms of performance, memory requirements, and programmability. We conclude that a polylithic approach is most suitable for toolkit builders, visual design software where code is automatically generated, and application builders where there is much customization of the toolkit. Correspondingly, we find that monolithic approaches appear to be best for application builders where there is not much customization of the toolkit.     

Robust interactive image segmentation using structure-aware labeling

Interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art.     

非监督SAR图像变化检测研究最新进展

SAR图像变化检测已经成为国内外研究热点,在多方面取得广泛应用。本文综述了SAR图像非监督变化检测技术研究的最新进展,从变化检测的基本流程出发,介绍了图像预处理、差异图提取、差异图分割的各类算法。本文的归纳总结,将进一步加深研究人员对SAR图像变化检测技术的认识,促进其在实际工作中的更好运用。