Mobile medical visual information retrieval

In this paper, we propose mobile access to peer-reviewed medical information based on textual search and content-based visual image retrieval. Web-based interfaces designed for limited screen space were developed to query via web services a medical information retrieval engine optimizing the amount of data to be transferred in wireless form. Visual and textual retrieval engines with state-of-the-art performance were integrated. Results obtained show a good usability of the software. Future use in clinical environments has the potential of increasing quality of patient care through bedside access to the medical literature in context.     

Medical image categorization and retrieval for PACS using the GMM-KL framework.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.     

Ten years of art imaging research

This paper describes a decade of work on digital imaging for museums. From 1989 to 1992, the visual arts system for archiving and retrieval of images (VASARI) project produced a digital-imaging system that made color-calibrated images of up to 20000×20000 pixels directly from paintings. It used seven color-separation bands in the visible region, resulting in an average color error of around 1 ΔE*_ab unit. These images have since been used to monitor the condition of paintings, document paintings during conservation treatment, including predicting appearance after cleaning, reconstruct the original appearance of paintings in which pigments have faded, and assess whether paintings have been damaged during transportation, in estimations of the surface reflectance spectra and in the printing of high-quality reproductions. We have applied similar techniques to museum infrared and X-ray imaging. To manage the images produced by the VASARI system, an image-processing package has been developed that is tailored for very large colorimetric images. This package has since been used in several other projects, including a remote image viewer designed to provide internet access to high-resolution images. The paper explores these developments and gives details of the current generation of VASARI-derived systems, set in the context of the state of the art for museum imaging     

A High-Speed Fiber Optic Data Bus for Local Data Communications

Over the past decade, the volume of data compiled in industrial, military, and scientific databases has increased to enormous proportions. This increase has led to the development of new techniques for economically transporting information locally at high data rates. Leading among these techniques is the serial asynchronous multiterminal time-division multiplexed data bus. This paper describes a 100 Mbit/s fiber optic data bus system for connecting 16 terminals separated by up to 2 km. The system was developed as part of a NASA database management system for archiving and retrieval of satellite data. The NASA application will be briefly introduced followed by discussions on the high-speed data bus architecture, bus access protocols, system implementation, and error rate performance.     

A memory learning framework for effective image retrieval.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.     

A new way for multidimensional medical data management: volume of interest (VOI)-based retrieval of medical images with visual and functional features.

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.     

Content-based image retrieval using H.264 intra coding features

Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images.     

A Service-Oriented Medical Framework for Fast and Adaptive Information Delivery in Mobile Environment

The need for fast treatment of patients in critical conditions motivates the use of mobile devices to provide prompt and consistent communication between hospitals and physicians. We propose a framework that supports ubiquitous access to medical systems using personalized mobile services and integrated medical systems. The proposed service-oriented medical framework provides dynamically composed services that are adapted to contextual variables such as the user’s role, the network bandwidth, and resources available at mobile devices while supporting task allocation in distributed servers for massive resource-consuming services. It also manages accurate patient data by integrating local medical systems using medical information standards such as Digital Imaging and Communications in Medicine and Health Level 7. We have demonstrated the effectiveness of our framework by building a prototype of context-based adaptation of computerized tomography image retrieval for acute stroke treatments, which allows images to be viewed on mobile devices with WiMax wireless network. The proposed medical framework reduces hospital delays of patients and facilitates treatments in the absence of medical specialists.      

Multiple image watermarking applied to health information management.

Information technology advances have brought forth new challenges in healthcare information management, due to the vast amount of medical data that needs to be efficiently stored, retrieved, and distributed, and the increased security threats that explicitly have to be addressed. The paper discusses the perspectives of digital watermarking in a range of medical data management and distribution issues, and proposes a complementary and/or alternative tool that simultaneously addresses medical data protection, archiving, and retrieval, as well as source and data authentication. The scheme imperceptibly embeds in medical images multiple watermarks conveying patient's personal and examination data, keywords for information retrieval, the physician's digital signature for authentication, and a reference message for data integrity control. Experimental results indicate the efficiency and transparency of the scheme, which conforms to the strict requirements that apply to regions of diagnostic significance.     

基于内容的图像检索技术

目前各行业对图像的使用越来越广泛,如何有效、快速地从大规模图像数据库中检索出需要的图像,是目前一个相当重要而又富有挑战性的研究课题.但传统的图像检索技术是基于文本的检索技术,这种方法虽然简单易行,但存在一些致命的缺点,严重影响了对图像信息的有效使用.为了克服传统方法的缺点,提出了基于内容的图像检索技术,该技术能够全面客观地提取图像内容,能有效地获取所需的视觉信息,能使图像数据库中的信息得到有效的管理.     

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.     

Fast segmentation of porcelain images based on texture features

This study aims to segment objects within images of porcelain artifacts to help users retrieve the images in an efficient and convenient manner. Through digital archiving, a tremendous number of porcelain images have been created. To avoid interference due to the image’s background during the retrieval process, it is necessary to segment objects in advance to accommodate high-precision image retrieval. In the proposed segmentation process, four texture features, including coarseness, contrast, directionality, and gradient, are first obtained. The morphological processing, which involves PCA (principal component analysis), Otsu’s method, and object filter for opening and closing operation, is applied. Finally, regarding the objects selected by object filter, boundary extraction and watershed segmentation are performed to segment the porcelain objects from the background. In our image segmentation experiment using images of Chinese porcelain from various dynasties, featuring various shapes and colors, complete and accurate segmentation results are produced. The results can be used as a reference for future identification of the era to which the artifacts belong, and also to lay a foundation for future development of porcelain image retrieval techniques as a benefit to academic research.     

编码衍射图样的相位检索

经典的迭代相位检索方法具有抗噪性能差、不 能收敛到全局最优解等缺点;而基于相位割(PC)的最优化相位检索技术通过精确的分离振幅 和相位变量, 将相位检索问题转化 为二次约束的凸规划问题,理论上可以保证在相差全局相位因子条件下收敛到全局最优解。 本文提出用更结构 化和更“很少随机的”的三元与八元掩膜编码衍射图样(CDP),求解PC以获得丢失的 相位。模拟实验首先测试 一维复信号,分别比较不同掩膜重构的成功率。其次,比较它们对二维分子图像的重构结果 。实验结果表 明,本文方法可以获得比传统的基于二元掩膜的方法更好的重构结果。将恢复的相位分 布加载到所搭 建的基于Si基液晶(LCOS)的光学重构系统中,产生的CDP证明了所提方法的正确性。     

基于角色等级树的光网络信息多域云访问控制研究

为了提高光网络信息多域云访问和调度能力,提出基于角色等级树的光网络信息多域云访问控制方法.构建光网络信息多域云访的分布式存储结构模型,结合特征重建重组方法进行光网络信息多域云数据的结构重组,在高维特征空间中提取光网络信息多域云数据的关联规则性特征量,采用模糊相关性融合方法进行光网络信息多域云数据信息融合和特征调度,结合...     

图像搜索引擎检索模式分析与研究

文中研究如何从HTML文档中提取图片相关信息,保证高效和准确的实现图片检索。在对图像搜索引擎检索模式分析的基础上,提出了若干关键技术,设计并实现了一个基于文本的Web图片搜索引擎,给出了系统的总体结构图．并对获取网页、提取信息、图片抓取、建立索引和提供查询进行了详细的描述,分析了图像搜索引擎的检索模式。     

Content-based retrieval of dynamic PET functional images

The recent information explosion has led to a massively increased demand for multimedia data storage in integrated database systems. Content-based retrieval is an important alternative and complement to traditional keyword-based searching for multimedia data and can greatly enhance information management. However, current content-based image retrieval techniques have some deficiencies when applied in the biomedical functional imaging domain. In this paper, we presented a prototype design for a content-based functional image retrieval database system for dynamic positron emission tomography (PET). The system supports efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. This design makes it possible to maintain a large number of patient data sets online and to rapidly retrieve dynamic functional image sequences for the interpretation and generation of physiological parametric images, and offers potential advantages in medical image data management and telemedicine, as well as providing possible opportunities in the statistical and comparative analysis of functional image data     

基于边缘方向直方图的形状检索

近年来,随着多媒体技术和数字设备的出现,如何有效地管理和访问图像信息已成为人们亟待解决的问题.因此,一种新的图像检索技术——基于内容的图像检索技术被提出来.其中,由于图像的形状特征更符合人们的视觉感知,因此基于形状的图像检索越来越受到研究者的关注.旨在研究基于形状轮廓特征的图像检索,提出了基于边缘方向的直方图形状检索算法.通过对常用边缘检测算子的分析和比较,给出了边缘方向直方图特征提取的具体实现技术,对采用的特征匹配方法做了描述,最后通过实验的结果与分析验证了算法的性能.     

Optical microscopic image processing of dendritic spines morphology

This paper describes an integrated neuroinformatics system, called NeuronIQ (neuronal image quantitator), for processing, archiving, and analyzing 3D neuronal images obtained from high resolution optical microscopy. While this paper focuses on single channel images, multichannel images can also be acquired for neurons, glial cells, blood vessels, and other neurological structures by using different staining agents.     

基于重组DCT系数子带能量直方图的图像检索

现在许多图像采用JPEG格式存储,检索这些图像通常要先解压缩,然后提取基于像素域的特征矢量进行图像检索。己有文献提出直接在DCT域进行图像检索的方法,这样可以降低检索的时间复杂度。本文提出对JPEG图像的DCT系数利用多分辨率小波变换的形式进行重组,对整个数据库中所有图像的DCT系数重组得到的若干子带,分别建立子带能量直方图,而后采用Morton顺序建立每幅图像的索引,并采用变形B树结构组织图像数据库用于图像检索。     

基于互信息方法的医学图像检索

提出了一种适用于医学图像检索的新方法,即最大互信息方法,使用该方法对超声图像库进行了检索实验,验证了基于最大互信息方法具有良好的检索性能;该方法既能满足位置不变性,旋转不变性和比例不变性,又能避免进行图像分割,从而避免了因图像分割引起的复杂计算,该方法还具有检索速度不受数据库中图像数目影响等优点,也适用于对一般图像进行相似性检索。