基于DICOM的医学影像设备接口设计与实现

医学影像存档与通讯系统(Picture Archiving and Communication Systems,PACS)是目前医院信息化建设的热点,医学数字成像和通信标准(Digital Imaging and Communication in Medicine,DICOM)是有关医学图像及其相关信息的数据编码及通讯的国际标准,支持DICOM标准是医学影像设备并入PACS网络的必要条件。为使目前尚不符合DICOM标准的影像设备有效并入PACS系统,必须为其添加DICOM接口。我们介绍了DICOM信息模型并实现了接口的软件系统,重点介绍了应用VisualC 编程实现DICOM服务中的C-STORE和DCM文件的读写功能。     

PACS与DICOM

近几年来随着医院信息化的深入发展及数字影像时代的到来，医学图像归档和通讯系统（picture archiving and communication system，PACS）在我国已开始逐渐发展起来。不少PACS软件产品都称是依据医学数字成像和交换（digital imaging and communication in medicine，DICOM）标准^[1]开发并与DICOM兼容的。但是对PACS、DICOM的理解及PACS究竟如何遵从DICOM标准都是一个值得深入探讨的问题。     

DICOM标准的语义扩充

医学图像数字成像和通信（Digital Imaging and Communicationsin Medicine，DICOM）标准是美国电气制造商联合会（NEMA）和美国放射学会（ACR）为了医学图像的存储和传输而制定的标准，在医学上得到广泛的应用。但该标准目前没有充分定义图像语义内容，从而使得实际中不能有效的实现DICOM的基于语义的应用。本文讨论了医学图像中的语义描述层次结构，概述了DICOM标准中对图像的描述方法并指出了该标准对图像语义内容描述的不足，给出了使用DICOM标准中私有属性对DICOM图像语义内容进行扩充的方法。     

DICOM医学图像的存储与管理

随着数字化医学成像设备在医院的广泛使用,对医学图像及相关数据的存档管理以及在不同科室之间的数据共享的要求越来越迫切,这就需要建立PACS(图像存档和通讯系统),这方面国外已经发展了很多年,我国目前处于起步阶段.本文参照PACS系统的一个已经被国际认可的医学图像标准即医学成像和通讯标准DICOM(digital imaging and communication in medicine),研究了标准的各个部分,特别是兼容性、信息对象定义(IOD)、服务对象对类(SOP)、数据编码等部分,就具体实现PACS系统的一个重要方面即医学图像的存档和管理做了深入的探讨,在此基础上建立了医学图像数据库系统,为实现医学图像信息的网络共享打下了基础.     

PACS中的DICOM标准分析及应用

DICOM是PACS系统中被国际认可的医学图像标准。本文分析了DICOM标准的主要部分，特别是信息对象定义、服务类和服务对象、数据组织格式、通讯机制等部分。结合广州医学院附属第二医院的实际情况，阐述了DICOM标准如何在PACS中应用。     

医学成像和通讯系统的CORBA设计方案

针对目前医学信息系统的异构带来了彼此之间通讯和信息共享的困难 ,对象管理组 (OMG)制定了CORBAMed软件规范 ,规定了医学信息系统的软件架构 ,定义各种服务的接口。本文尝试将 CORBA应用于医学图像存档和通讯系统 ,提出 CORBA解决方案的 PACS系统模型 ,分析了软件系统的视图层次 ,最后讨论了CORBAMed的相关服务。     

PACS中的标准DICOM

医学数字图像通讯标准DICOM3.0(Digital Imaging and Communication in Medicine Ⅲ）是医学信息学领域中正在广泛使用的工业标准，DICOM3.0正在成为医院医学影像存档与通讯系统PACS（Picture Archiving and Communication System)事实上的国际标准。本文介绍了DICOM3．0的产生，组成和E－R建模基础，介绍了DICOM的国内外发展情况。     

医学图像DICOM格式转换软件的设计与实现

PACS（图像存档与通讯系统）应遵循DICOM（医学数字图像通讯）标准。目前国内存在大量不符合DICOM标准的影像设备,为了使这些设备也应用于PACS,我们使用面向对象方法设计和实现了一个具有良好扩充性的格式转换工具包,可进行DICOM格式与各种通用图像格式之间的转换,并具有视频输入与格式转换工具包,可进行DICOM格式与各种通用图像格式之间的转换,并具有视频输入与扫描仪输入的接口。该工具包提供一组DICOM API,可供Windows平台的各种编程环境使用。     

DICOM医学图像数据接口的Java实现

DICOM3.0标准作为目前通用的医学图像标准 ,最重要的特性之一在于其面向对象性。本研究依据这一重要特性 ,描述了如何使用面向对象的Java语言实现该标准的接口软件。从而解决了DICOM硬件设备与后继PACS处理软件的接口问题。同时 ,为了克服现有PACS系统的硬件瓶颈 ,对数据读入方式采取了优化 ,大大降低了CPU运行时间和内存占用空间 ,提高了系统的运行质量和性能。     

标准医学DICOM图像的转换实现

针对我国目前存在的非DICOM与DICOM设备并存所造成的医疗通讯问题，通过分析BMP与DICOM图像文件的特点，基于VC＋＋6．0编程环境实现了BMP医学图像向DICOM标准图像的转换，为不同设备之间的互联以及PACS系统的发展提供了有利条件。     

The ImageCLEFmed Medical Image Retrieval Task Test Collection

A growing number of clinicians, educators, researchers, and others use digital images in their work and search for them via image retrieval systems. Yet, this area of information retrieval is much less understood and developed than searching for text-based content, such as biomedical literature and its derivations. The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) is to improve understanding and system capability in search for medical images. In this paper, we describe the development and use of a medical image test collection designed to facilitate research with image retrieval systems and their users. We also provide baseline results with the new collection and describe them in the context of past research with portions of the collection.     

Selective automated indexing of findings and diagnoses in radiology reports

The recent improvements in capabilities of desktop computers and communications networks give impetus for the development of clinical image repositories that can be used for patient care and medical education. A challenge in the use of these systems is the accurate indexing of images for retrieval performance acceptable to users. This paper describes a series of experiments aiming to adapt the SAPHIRE system, which matches text to concepts in the UMLS Metathesaurus, for the automated indexing of image reports. A series of enhancements to the baseline system resulted in a recall of 63% but a precision of only 30% in detecting concepts. At this level of performance, such a system might be problematic for users in a purely automated indexing environment. However, if the ability to retrieve images in repositories based on content in their reports is desired by clinical users, and no other current systems offer this functionality, then follow-up research questions include whether these imperfect results would be useful in a completely or partially automated indexing environment and/or whether other approaches can improve upon them.     

Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data

Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.     

基于图像分割的Tamura纹理特征算法的研究与实现***☆

背景：ITK主要提供医学图像处理、分割与配准算法,但其缺少可视化的功能,缺乏灵活实用的用户界面,VTK提供了丰富的医学影像处理与分析工具,具有强大的图形处理和可视化功能。 目的：利用以前的确诊病例和医生的诊断经验以及患者的相关病史,对确诊的医学影像资源进行管理,归档,并检索,以减少人工干预,提高图像的查全率和查准率。 方法：以视觉感知机制为基础,在ITK平台上进行图像平滑去噪和分割的预处理过程,利用Tamura算法完成纹理特征提取,最后通过实验采集、计算数据,完成对比分析。 结果与结论：基于图像分割的Tamura纹理特征算法在基于图像纹理检索应用上便于相似性度量,进而可提高检索的准确率。     

Developing a Medical Image Content Repository for E-Learning

The integration of medical informatics and e-learning systems could provide many advanced applications including training, knowledge management, telemedicine, etc. Currently, both the domains of e-learning and medical image have sophisticated specifications and standards. It is a great challenge to bring about integration. In this paper, we describe the development of a Web interface for searching and viewing medical images that are stored in standard medical image servers. With the creation of a Web solution, we have reduced the overheads of integration. We have packaged Digital Imaging and Communications in Medicine (DICOM) network services as a component that can be used via a Web server. The Web server constitutes a content repository for searching, editing, and storing Web-based medical image content. This is a simple method by which the use of Picture Archiving and Communication System (PACS) can be extended. We show that the content repository can easily interact and integrate with a learning system. With the integration, the user can easily generate and assign medical image content for e-learning. A Web solution might be the simplest way for system integration. The demonstration in this paper should be useful as a method of expanding the usage of medical information. The construction of a Web-based repository and integrated with a learning system may be also applicable to other domains.     

A Multimodal Search Engine for Medical Imaging Studies

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.     

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.     

A Mammography Database and View System for African-American Patients

We have digitized mammography films of African-American patients treated in the Howard University Hospital Radiology Department and have developed a database using these images. Two hundred and sixty cases totaling more than 5,000 images have been scanned with a high resolution Kodak LS85 laser scanner. The database system and web-based search engine were developed using MySQL and PHP. The database has been evaluated by medical professionals, and the experimental results obtained so far are promising with high image quality and fast access time. We have also developed an image viewing system, D-Viewer, to display these digitized mammograms. This viewer is coded in Microsoft Visual C# and is intended to help medical professionals view and retrieve large data sets in near real time. Finally, we are currently developing an image content-based retrieval function for the database system to provide improved search capability for the medical professionals.