面向临床研究主题服务的医学影像数据库研制

研制面向临床研究主题服务的医学影像数据库,建立数据库制作流程,实现医学影像按疾病分类存储和在线服务。首先进行医学影像数据库建设的需求分析和数据资源调查,围绕临床研究主题需求设计医学影像数据库的系统结构。然后,应用"数据库—样本数据—影像文件"三级信息组织模型、基于DICOM标准的医学影像数据处理、"数据库索引+Zip文件"的影像文件管理与缓存机制等技术实现该系统。系统支持DICOM、JPG等多种图像格式和组合查询模式,测试运行取得良好效果。     

医学影像数据库的索引及检索技术的研究

随着医学影像数据库数据量的剧增，迫切需要研究高效的索引技术以支持基于图像内容的检索。介绍了医学影像数据库索引技术的特点，比较分析了几种典型的空间数据索引方法（如R树、VA文件、A树、NB树和M树）及其作为医学图像数据库索引的性能。     

应用MPEG-7建立乳腺X线影像数据库

乳腺肿瘤是妇女的多发疾病。建立有效的数字化乳腺X线影像存储与检索技术，可以充分利用医疗资源，并且帮助用户进行个人健康管理。MPEG-7技术可以实现对多媒体内容低级特征和高级语义信息的注释。将MPEG-7技术引入数字化乳腺X线影像存储，详细分析对医学影像MPEG-7注释以及将MPEG-7文件与医学图像一起存储到数据库的技术实现，在Linux系统下构建包含MPEG-7信息的数字化乳腺X线影像MySQL数据库和基于网络的查询系统。结果说明使用MPEG-7对医学图像低级特征和高级语义注释是可行的，数据库中存储的注释文件有利于医学图像高级语义信息的检索。     

解剖学结构方法在基于医学图像内容检索中的应用

基于图像内容的检索content-based Image retrieval，CBIR)在医学领域的应用有着广阔的前景。针对医学图像特点，我们提出基于解剖结构的灰度及纹理特征的医学图像检索方法，CT／MRI图像数据库的检索效果较好。     

基于WEB的寄生虫虫卵特征分类检索系统的建立

目的利用数字化技术和网络技术,方便基层医务人员和研究人员获取寄生虫病防治和研究的相关数据资源,特别是辅助其进行虫卵的甄别。方法使用基于web2.0的网络技术、特征提取算法和数据库检索的相关技术对寄生虫虫卵的特征进行归纳、提取和分类识别的研究,制定其特征数据编码存储规则和虫卵检索、识别的流程。结果构建了基于网络设施基础上的病原体远程监测网站系统,可提供寄生虫虫卵特征分类检索的手段与服务。结论测试结果表明,建立的寄生虫虫卵特征分类检索方案是可行和有效的,值得推广试用。     

医学图像检索研究

介绍了基于内容的医学图像检索系统的意义、方法和关键技术,分析了国内外医学图像检索领域的应用现状,并在此基础上探讨了基于内容的图像检索技术在医学图像检索领域的应用前景和未来的发展方向.     

基于linux平台的医学影像软件系统设计与应用

背景：随着VTK和ITK两个软件开发包的研制成功,医学影像领域内的研究人员越来越重视本领域内的软件开发问题。 目的：开发一种结合VTK和ITK的医学影像软件系统。 方法：首先对可视化软件包和图像处理包整合,进而基于整合框架对体数据进行处理、同步可视化和测量分析,最后结合病历信息与医学影像分析数据建立管理系统,在linux平台上对该软件系统进行了实现,利用上气道CT体数据对系统进行了测试。 结果与结论：该系统能够结合VTK和ITK对体数据进行可视化和图像处理,基于MySQL数据库对病历信息和医学影像数据进行合理管理,体积、长度等测量精度都在1%之内。     

SciFinder Scholar与CA on CD比较研究

美国化学文摘(Chemical Abstracts,简称CA)创刊于1907年,是目前世界上应用最广泛的化学、化工、医药及相关学科的检索工具,由美国化学协会化学文摘社(Chemical Abstracts Service)编辑出版.CA报道的内容广泛.包括无机化学、有机化学、分析化学、物理化学、高分子化学、药物学、毒物学和生物学等学科,有印刷版、缩微版、磁带版、光盘版、联机版和网络版等出版形式,检索方法实现了从手工到计算机检索,从书目信息检索到全文检索,从单个数据库检索到跨库检索.这些出版形式又包含不同的版本或内容不同的数据库[1].本人通过比较研究目前应用较为广泛的SciFinder Scholar和CA on CD两个数据库,对其进行综合比较、分析评价,提出合理的使用建议,来反应其在医药学领域的影响.     

一种基于多特征的MRI脑部图像检索方法5000

针对目前基于内容的医学图像检索技术中存在的问题,提出一种基于多特征的MRI脑部图像检索方法.根据MRI脑部图像特点,设计多特征向量求解算法,提取脑部图像的纹理、边缘和灰度特征并组合为多特征向量.为加快检索速度,对多特征向量进行降维,初步实验结果表明提出的检索方法是可行的.     

常用医学全文数据库的比较介绍

由于全文检索及数据库技术的快速发展,一种独立的电子资源-全文数据库逐渐受到广大用户的普遍关注.全文数据库是将文献全文以机读形式存储,用自然语言表达检索课题,借助于截词、逻辑等匹配方法,直接对文献正文进行查找,以检出所需文献的一种方式.由于具有检索直接、使用方便、专指性好等优点,给用户查找和获取全文带来了极大方便.我馆以建立镜像系统及内部用户等形式引进几种外文全文数据库,针对用户在选择数据库和使用上的问题,本文主要介绍几种常用的中外文医学全文数据库及检索方法,对用户迅速准确获取原始文献提供指导和帮助.     

Ontology of Gaps in Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the “semantic gap.” The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of “gaps” in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.     

An Interactive System for Computer-Aided Diagnosis of Breast Masses

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists “a visual aid” in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting “abnormalities” similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.     

POSTGRESQL-IE: An Image-handling Extension for PostgreSQL

The last decade witnessed a growing interest in research on content-based image retrieval (CBIR) and related areas. Several systems for managing and retrieving images have been proposed, each one tailored to a specific application. Functionalities commonly available in CBIR systems include: storage and management of complex data, development of feature extractors to support similarity queries, development of index structures to speed up image retrieval, and design and implementation of an intuitive graphical user interface tailored to each application. To facilitate the development of new CBIR systems, we propose an image-handling extension to the relational database management system (RDBMS) PostgreSQL. This extension, called PostgreSQL-IE, is independent of the application and provides the advantage of being open source and portable. The proposed system extends the functionalities of the structured query language SQL with new functions that are able to create new feature extraction procedures, new feature vectors as combinations of previously defined features, and new access methods, as well as to compose similarity queries. PostgreSQL-IE makes available a new image data type, which permits the association of various images with a given unique image attribute. This resource makes it possible to combine visual features of different images in the same feature vector. To validate the concepts and resources available in the proposed extended RDBMS, we propose a CBIR system applied to the analysis of mammograms using PostgreSQL-IE.     

Extended Query Refinement for Medical Image Retrieval

The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man–machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.     

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.     

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.     

几何矩不变量在基于内容医学图像检索中的应用

基于图像内容的检索 (Content basedimageretrieval,CBIR) ,是当前比较热门也是比较难的研究课题。针对基于内容的医学图象检索 ,我们提出几个对于图像旋转、伸缩、位移不变的几何矩不变量集 ,对于图像数据库的初检效果较好。     

基于模糊聚类的医学图像检索方法

如何构建有效的图像库结构，提高图像检索速度是基于内容的图像检索所需要解决的关键问题之一。论文采用了一种基于改进的模糊C均值算法来聚类图像。实验表明该方法应用于图像检索，在准确性和实时性方面均能达到较好的效果。另外，系统利用基于分阶段显示和评价反馈的权重调整方法进一步提高检索性能。     

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.