医学图像信息融合技术的发展

医学图像信息融合是医学图像处理、放射医学及医学影像学领域近几年兴起的一种新技术。应用医学图像信息融合技术 ,可以把不同模态的医学图像有机地结合起来 ,为临床诊断和治疗提供更完善的图像信息 ,从而使医学图像能更好地为现代医学服务。本文对医学图像信息融合技术及其研究现状作了详细的介绍和综述 ,并对其研究前景作了预测。     

医院小型PACS系统的设计与实现

介绍了PACS的概念、分类及其在设计和实现中的一些关键技术，如图像数据的采集方法、图像存储设备及介质的选取，图像传输的网络等，并分析了各种类型PACS的优缺点，最后结合实际给出金卫工程试点医院小型PACS设计方案及网络结构图。     

医学图像融合技术的研究

利用图像融合技术,将不同模态的医学图像有机地结合在一起,可以充分利用各种医学图像的优点,为临床诊断和治疗提供帮助.本文主要介绍了医学图像融合技术的基本概念、发展情况、常用方法及面临的困难等,并对医学图像的研究前景作了预测.     

磁共振图像的分割

近年来，磁共振图像在临床上的应用越来越广泛和深入，但是，制约磁共振图像在临床上广泛应用和研究的一个瓶颈问题是图像分割。自从８０年代末磁共振图像应用于临床检查以来，人们提出了众多的磁共振图像的分方法。这些方法中有经典的方法，如阈值法、基于边界的方法和基于区域的方法；有现代的方法，如概率统计的方法、基于知识的方法、模糊方法和人工神经网络的方法等。本文对这些方法进行了综述和讨论。     

医学图像处理中有关图像分割的新技术

图像分割是图像处理中最基本和最主要的技术.本文简要介绍了医学图像分割的常用分割方法,主要包括阈值分割、神经网络分割、模糊分割、遗传算法、统计方法和基于特定模型等方法的图像分割.并对其近年来的进展和应用进行了综述.     

肺癌图像数据库及可视化工具的建立

肺癌是目前最常见的恶性肿瘤,也是已知的确诊后存活率最低的癌症之一。建立大规模的肺癌图像数据库是进行肺癌计算机辅助诊断(CAD)研究,开展肺癌诊断教育和训练以减轻医生负担,以及提高医疗诊断效率的基础。本文针对当前的肺癌图像数据库联盟(LIDC)在使用中存在的数据存取困难、缺乏对数据可视化和数据检索的支持等问题,提出了一个集数据模型、可视化和数据检索工具为一体的肺癌数据库平台。本文从分析LIDC的数据格式入手,引入数据库技术设计完成了肺癌数据库,以对获取的大量的肺癌图像数据进行管理和使用;针对数据可视化和检索的需要,设计了用于图像及其标注可视化的浏览器和数据查询器。研究结果表明该平台能很好地完成肺癌数据的存储、整合、可视化和检索,促进了肺癌诊断的研究。     

磁共振图像的分割

近年来,磁共振图像在临床上的应用越来越广泛和深入,但是,制约磁共振图像在临床上广泛应用和研究的一个瓶颈问题是图像分割。自从７０ 年代末磁共振图像应用于临床检查以来,人们提出了众多的磁共振图像的分割方法。这些方法中有经典的方法,如阈值法、基于边界的方法和基于区域的方法;有现代的方法,如概率统计的方法、基于知识的方法、模糊方法和人工神经网络的方法等。本文对这些方法进行了综述和讨论。     

医学图像分割技术

图像分割是制约医学图像在临床上广泛应用的关键性问题。医学图像分割则是图像分割的一个重要应用领域。本文讨论了医学图像分割的目的和意义，简述了医学图像分割技术的进展，对近年来医学图像分割技术进行了综述。     

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.     

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。     

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.     

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.     

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.     

医学CBIR系统与PACS的集成平台研究

随着在临床中应用的数字影像设备越来越多,CBIR对于PACS变得越来重要。若使强大的算法变得可能,医学CBIR系统必须被整合和集成到PACS中,才能更好地为医生所应用。考虑到医生检索习惯,本文提出了一个基于现有的标准协议把医学CBIR系统整合到PACS中的平台模型,并保证两系统的自主性,为真正实现在PACS中基于内容的检索提供了理论基础和方法指导。     

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。     

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。     

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.     

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 New Family of Distance Functions for Perceptual Similarity Retrieval of Medical Images

A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition of the AID family with L _p distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR.     

Content-based Retrieval of Mammograms Using Visual Features Related to Breast Density Patterns

This paper describes part of content-based image retrieval (CBIR) system that has been developed for mammograms. Details are presented of methods implemented to derive measures of similarity based upon structural characteristics and distributions of density of the fibroglandular tissue, as well as the anatomical size and shape of the breast region as seen on the mammogram. Well-known features related to shape, size, and texture (statistics of the gray-level histogram, Haralick’s texture features, and moment-based features) were applied, as well as less-explored features based in the Radon domain and granulometric measures. The Kohonen self-organizing map (SOM) neural network was used to perform the retrieval operation. Performance evaluation was done using precision and recall curves obtained from comparison between the query and retrieved images. The proposed methodology was tested with 1,080 mammograms, including craniocaudal and mediolateral-oblique views. Precision rates obtained are in the range from 79% to 83% considering the total image set. Considering the first 50% of the retrieved mages, the precision rates are in the range from 78% to 83%; the rates are in the range from 79% to 86% considering the first 25% of the retrieved images. Results obtained indicate the potential of the implemented methodology to serve as a part of a CBIR system for mammography.