MIARS: A Medical Image Retrieval System

The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.     

ProQuest Medical Library全文数据库及检索系统功能

介绍ProQuest Medical Library 全文数据库及检索系统功能。     

面向医学学科领域的检索词库构建

分析专业数据库和搜索引擎在进行文献检索时存在的问题及构建医学学科领域检索词库的原因,以教育部科技查新站2014年以来的科技查新报告为数据源,建立经过科技查新和学科专家共同审验通过的检索词库。该词库可应用于科技查新、文献检索教学等领域,还可推广到其他领域。     

构建医学影像信息统一Web调阅系统

随着医院信息化的建设和发展,使医学影像信息的数字化保存处理成为可能,并提供了大量有价值的信息数据。如何将这些信息统一展现在同一个平台,省去临床医生或疾病防控中心调阅不同系统的麻烦,更快捷地查询病人的各方面疾病信息成为研究重点。     

Evaluation of a Content-Based Retrieval System for Blood Cell Images with Automated Methods

Content-based image retrieval techniques have been extensively studied for the past few years. With the growth of digital medical image databases, the demand for content-based analysis and retrieval tools has been increasing remarkably. Blood cell image is a key diagnostic tool for hematologists. An automated system that can retrieved relevant blood cell images correctly and efficiently would save the effort and time of hematologists. The purpose of this work is to develop such a content-based image retrieval system. Global color histogram and wavelet-based methods are used in the prototype. The system allows users to search by providing a query image and select one of four implemented methods. The obtained results demonstrate the proposed extended query refinement has the potential to capture a user’s high level query and perception subjectivity by dynamically giving better query combinations. Color-based methods performed better than wavelet-based methods with regard to precision, recall rate and retrieval time. Shape and density of blood cells are suggested as measurements for future improvement. The system developed is useful for undergraduate education.     

医学图像检索技术的应用研究

医学图像的检索一直是人们所关注的焦点,本文主要介绍了基于内容的医学图像检索的意义、工作原理和方法.本研究将COM技术引入到医学图像检索中,使用MATLAB制作COM组件供VB调用、实现图像处理.实验表明,在VB开发环境中调用COM组件,能大大加快软件开发的进度、缩短源代码,提高代码质量及软件的稳定性.     

互联网医学影像资源的特点与利用

医学教学、科研需要大量的医学影像资源,本文介绍了互联网七的两个医学影像资源数据库并对其特点进行了分析和总结.     

中文医学主题词表检索系统

在简要介绍“中文医学主题词表检索系统”开发研制的基础上,重点讨论了该系统的功能和特点,描述了数据结构及内容,并提出了进一步发展和改进的建议。     

Linear Models of Cumulative Distribution Function for Content-based Medical Image Retrieval

We propose an Image matching technique based on Cumulative Distribution Function, which provides a considerable reduction in the retrieval time. The two novel approaches called bit plane histogram and hierarchical bit plane histogram are discussed. Next, the image matching technique based on Cumulative Distribution Function is explained and a comparison of the various techniques is brought out. The CDF of the query and the images in the database are approximated by piecewise linear models with two parameters, slope and intercept at various grayscale intervals. The contiguous set of lines approximating the CDFs enables us to compare the query image and the images in the database with corresponding estimated slopes and intercepts. As the dynamic range of CDF is from 0 to 1, images of different sizes can be compared. Approximation of CDFs with lines further reduces the dimension of the image features and thus improves the speed of matching.     

基于梯度方向直方图特征的医学图像分类方法

医学图像分类是医学图像处理中的研究热点,可应用在医疗信息化、辅助诊断和远程医学等方面。采用梯度方向的直方图特征来描述图片的边缘特征,利用支持向量机对多类别的图片进行分类,并比较了几种不同的核函数对分类效果的影响。     

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree

The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area – in mammography, in addition to the creation of the list of similar images – cases. The created list is used for assessing the nature of the finding – whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.     

基于DT-CWT变换统计模型的医学图像纹理检索

针对离散小波变换具有平移变化性和弱方向性的特性,本文提出了一种基于双树复小波变换(DT-CWT)统计模型的医学图像纹理检索方法 .该方法 首先利用双树复小波变换系数的平移不变和多方向选择特性,建立广义高斯分布的统计模型,然后基于该模型提取图像的特征矢量,最后利用改进的Log-likelih60d(ILL)相似性测度算法进行纹理图像检索.实验结果 表明,该方法 的检索查准率达到了82.8%,相比于传统的Gabor算法和小波算法都有了较大的提高,对今后的纹理图像检索具有重要的理论与实际意义.     

面向医学信息互操作的SOA架构设计

随着IT技术在医疗行业的应用,医院内每天产生的信息呈爆发性增长。如何充分利用这些信息以提高医疗服务质量。已成为备受关注且亟需解决的问题。在区域医疗网络的整体框架下,结合面向服务的架构（SOA）以及Web2．0理念,提出一种医学信息互操作系统的设计方案,该系统可实现不同医疗机构之间医学信息的在线调阅及动态播放。加上SOA技术的使用,使该系统层次分明,具有良好的通用性和可维护性。     

循证医学决策系统

当前，循证医学受到了很大的尊崇，但它的普遍推广却相对滞后，原因在于其实践流程较长、应用成本较高。通过使用基于知识库的智能决策技术实现循证医学的功能，建立具有人工智能特征循证医学决策系统，使两者相得益彰，扩大两者的推广应用面，促进医学信息化的进展。     

A Novel Reversible Multiple Medical Image Watermarking for Health Information System

The exponential augmentation of information and communication technologies has appreciably shifted panorama of Health Information System (HIS), drawing keen interests of the researchers to address the challenges in protecting the medical data. Digital Watermarking therefore gained wide popularity as an entrusted measure to ensure the security. Literature reports wide assortment of imaging approaches for digital watermarking; however its perspectives in the HIS have received meager attention, especially in the reversible watermarking context. This paper presents wavelet based novel reversible watermarking approach which imperceptibly embeds multiple watermarks like patient diagnosis detail, Electrocardiogram (ECG) signal, doctor’s digital signature and diagnostic code for image retrieval. The proposed wavelet based approach exploits the quantization function to embed multiple watermarks and uses a novel tracking key to recover the original medical image. The algorithm has been analyzed on various medical imaging modalities (MRI, CT, MRA, US images of size 512 X 512), considering various image processing attacks. Experimental results signify highest hiding capacity of 91,136 bits with PSNR of 44.52 dB. The proposed algorithm adheres to strict necessities concerning the tolerable modifications in the medical images. The technique addresses the issues related with HIS namely data integrity, authentication, confidentiality protection, efficient data management, retrieval of image and additionally fulfill tamper detection on the entire image for ensuring the integrity of the medical image.     

以用户为中心的医学检索系统设计——万方医学网

介绍用户体验要素模型,以万方医学网为例进行分析,阐述用户体验要素模型5层结构在万方医学网设计中的运用和体现.重点对万方医学网检索系统的设计进行剖析,包括系统结构、检索内核的功能与特色以及基于检索内核的扩展应用,最后对系统的下一步设计计划进行简要说明.     

A Multiagent Architecture for Developing Medical Information Retrieval Agents

Information that is available on the world wide web (WWW) is already more vast than can be comprehensibly studied by individuals and this quantity is increasing at a staggering pace. The quality of service delivered by physicians is dependent on the availability of current information. The agent paradigm offers a means for enabling physicians to filter information and retrieve only information that is relevant to current patient treatments. As with many specialized domains, agent-based information retrieval in medical domains must satisfy several domain-dependent constraints. A multiple agent architecture is developed and described in detail to efficiently provide agent-based information retrieval from the WWW and other explicit information resources. A simulation of the proposed multiple agent architecture shows a 97% decrease in information overload and an 85% increase in information relevancy over existing meta-search tools (with even larger gains over standard search engines).     

研究生医学信息检索教学体系的创新与实践

医学信息检索教学是培养研究生的情报意识，使他们掌握获取最新文献信息的渠道和方法的重要手段。现从教学内容、实习环节、考核方法等方面阐述山西医科大学在研究生医学信息检索教学中的方法和体会，并且对本教学体系的创新性、先进性、科学性、合理性及实践意义进行论述。     

Semantic-Enhanced Query Expansion System for Retrieving Medical Image Notes

Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model. Our new method has an interactive interface that allows users to provide relevance feedback and construct new queries more efficiently. Sixteen medical professionals evaluated the query expansion module, and each evaluator had prior experience searching for medical images. When using the initial query as the baseline standard, expanded queries achieved a performance boost of 22.6% in terms of the relevance score on first ten results (P-value<0.05). When using Google as another baseline, our system performed 24.6% better in terms of relevance score on the first ten results (P-value<0.05). Overall, 75% of the evaluators said the semantic-enhanced query expansion workflow is logical, easy to follow, and comfortable to use. In addition, 62% of the evaluators preferred using our system instead of Google. Evaluators who were positive about our system found the knowledge map-based visualization of candidate medical search terms helpful in refining cases from the initial search results.