基于图割与粗糙集的MRI脑部肿瘤图像检索方法

背景:基于内容的医学图像检索是一门涉及多领域的学科,由于各种医学图像的成像原理不同,产生的图像在颜色、纹理和形状等视觉特征方面存在差别,使得此方法的实现还存在许多需要解决的问题.目的:针对基于内容的医学图像检索中存在特征提取困难、检索时间长的问题,提出一种基于图割与粗糙集结合的相似图像检索方法.方法:为克服图割仅适用于较少象素的图像和倾向于小割集的缺陷,首先对图像进行聚类,然后构建图像的Gomory-Hu割树,按割值大小依次去掉值较小的边,提取出图像的特征子图并构建特征库.为实现快速检索,借助粗糙集对特征库中的特征进行约简,有效减少参与相似性比较的特征数量.并将此方法应用到MRI脑部肿瘤图像的检索.结果与结论:实验结果表明该方法能快速有效地检索出MRI脑部图像库中的肿瘤图像,检索的平均查准率为78.4%,平均查全率为62.9%.     

A deep community based approach for large scale content based X-ray image retrieval

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.     

基于可扩展标记语言技术构建中国人大脑MR影像库

医学影像数据库及信息系统是医学影像数据有效检索与分析的重要手段,是医学临床、教育与研究的基础。本文提出一种基于可扩展标记语言(XML)的医学影像数据库及信息系统的构建方法,实现中国正常人脑MR影像库及信息系统,该系统支持医学影像数据的有效组织、存储、检索和挖掘。临床实际应用证明该系统具有可扩展性强、结构稳定、功能独立、存取效率高等特点,满足人脑MR影像数据存储、检索和处理分析的需求,具有良好的应用价值。     

Use of a database for clinical assignments and student evaluation

Nurse educators must maintain and have access to large amounts of information about student clinical performance. A computerized database is proposed as an efficient and effective clinical record-keeping system. This system will save the nurse educator time and energy with record-keeping and retrieval of student information. Implementation of the database also facilitates the clinical assignment and student evaluation process.     

Scalable histopathological image analysis via supervised hashing with multiple features

Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods.     

The development of a digital image database of clinical photographs

At the end of December 1998, the traditional clinical slide library at the Media Resources Centre University Hospital of Wales and University of Wales College of Medicine was closed to new acquisitions. This paper describes how a digital image database (Image AXS) has been customized to provide a source of rapidly accessible and easily searchable images, suitable for teaching and reference purposes. The basic functions of the program are explained and areas such as data integrity, image capture, storage, archiving and the impact that this technology has had on departmental workflow are discussed.     

Online medical information services

The author provides a comprehensive overview of online medical information services that can be accessed by personal computer users over telephone lines. The article encompasses the reasons for online database access, equipment considerations, and structure and sources of database information. Tips on search strategy and resources for increasing database search proficiency are also included.     

Personal digital assistants change management more often than paper texts and foster patient confidence.

INTRODUCTION: Rapid retrieval of information, including drug treatment options, is critical to emergency department practice. OBJECTIVES: To assess feasibility and patient acceptance of personal digital assistants and to determine the scope of management changes. METHODS: Emergency medicine residents (EMRs, n = 18) and emergency medicine attending (EMAs, n = 12) used personal digital assistants with drug database and clinical references. Text versions were also available in the emergency department. We did a prospective, random, cross-over time-motion study, recording retrieval time, source, and changes to patient care for 16 and 8 h for EMRs and EMAs, respectively. We surveyed patients for confidence in EMRs and EMAs with personal digital assistants, and perceived efficiency. RESULTS: EMRs accessed paper (n = 131) or personal digital assistant (n = 181) information on 92.3% of patients (n = 17, both). They accessed personal digital assistant on 61.4% of patients vs. 44.5% with texts (odds ratio 1.99, 95% confidence interval 1.4-2.80). Mean access times were 9.3 and 9.4 s, respectively, +1.4 for both. Personal digital assistant access was 75%/25% between pharmacopeia and clinical resource. Personal digital assistants changed drug choice in 39/181 patients (21.5%), and other management (diagnosis, treatment or disposition) in 15/181 patients (8.3%). Odds ratio for change in management for personal digital assistant vs. paper was 2.00 (95% confidence interval 1.11-3.60). We surveyed patient perception for 198 of 295 patients (67.1%). Fifty percent reported more confidence in their EMRs and EMAs with a personal digital assistant, while 5% reported less confidence. Sixty percent agreed strongly that there is too much medical information to remember. CONCLUSIONS: Personal digital assistants are feasible in an academic emergency department and change management more often than texts. EMRs accessed personal digital assistants more often than paper texts. Patient perceptions of physicians who use personal digital assistants are neutral or favorable.     

Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752 ms. The source code is available at https://github.com/zhengyushan/lagenet.     

Microcomputer image processing for burn patients

A computer-based system was developed to handle information and images in a clinical setting. The system is based on an 80286 AT computer, the topology of which includes a 16-bit color image capture/display graphics adapter and a 256 gray scale 8-bit digital image analysis processor. Using its color capabilities, we have created a picture database of patients' injuries, radiographs, and other relevant clinical data. The computer-based imaging system allows instant access to this information and minimizes subjective evaluation, making comparison of and follow-up of treatments more objective. The image analysis components permit digital characterization of the burn wound, enhancing our ability to quantitatively evaluate wound size, contracture, graft take, and re-epithelialization. This is a cost-effective method of handling information and images in the clinical setting as well as an effective research and teaching tool that facilitates management and follow-up care of the patient with burns.     

Towards an object-based multi-scale soil moisture product using coupled Sentinel-1 and Sentinel-2 data

With the advent of the new generation of microwave and optical sensors with unique characteristics, the multi-sensor fusion for the soil moisture retrieval has given rise to a considerable research interest. The Sentinel satellites enable operational soil moisture mapping with moderate accuracy while providing regular temporal coverage and high spatial resolution at no cost to the users. In the meantime, working at the object level enables us to take advantage of some of the unique capabilities of this approach. This letter outlines an object-based approach for the multi-scale soil moisture retrieval by coupling the single polarization C-band synthetic aperture radar (SAR) and optical data. To fulfil this goal, a broad range of optical and texture features was extracted from the optical and single polarization mode SAR data. Subsequently, the relevant features were properly selected using the Random Forest-Recursive Feature Elimination (RF-RFE) algorithm. Then, the selected features were imported to the segmentation process to create the image objects. Afterwards, the support vector regression (SVR) technique was used to estimate the soil moisture value of the image objects and to conduct the multi-scale soil moisture retrieval with a small training database. It is observed that the proposed approach can provide multi-scale soil moisture maps with a reasonable accuracy in different applications requiring different scale requirements.     

全国高职护理专业教学资源库管理机制研究

目的探讨全国高职护理专业教学资源库的管理机制。方法从构建过程和运行过程两方面探讨资源库管理机制,构建过程管理包括资源库建设人员组织及管理和资源库建设架构管理;运行过程管理包括教学资源维护管理、资源库运行管理和用户权限管理。通过现场访谈和问卷调查的方法评价效果。结果成功构建护理专业教学资源库,用户对资源库总体满意度现场访谈为91.81%,调查问卷为88.28%。每周使用护理专业教学资源库的用户占52.30%。31.67%现场用户和44.99%问卷用户提出需要提高网页浏览速度。结论通过资源库构建过程和运行过程两个环节对构建并运行全国高职护理专业教学资源库进行了有益的尝试。     

口腔数字成像系统在阻生牙拔除中的应用评价

目的:评价口腔数字成像系统在阻生牙拔除手术中的作用。方法:应用ODIS-I型口腔数字成像系统对138例拟行阻生牙拔除的患者拍片,并对成像质量进行判断。应用该系统的图像处理功能进行阻生牙拔除的术前手术设计,配合应用系统的内窥镜与患者交待病情和拔除方法。结果:ODIS-I型口腔数字成像系统拍摄的阻生牙图像可以基本满足临床的使用需求;应用该系统的图像处理功能可方便地进行阻生牙拔除的手术设计,提高了手术的准确率;配合利用系统的内窥镜可直观地与患者交流。利用数据库管理功能可以完成图像及病历资料的管理和检索及调阅,对患者的复诊和治疗都十分有益;数字化摄像采用的传感器质硬、不易随意摆放、放入患者口内有不适感是其不足。结论:应用口腔数字成像系统可提高阻生牙的临床治疗和科研水平;尚须改进传感器性能以提高拍片质量。     

Transition to an All-Digital Echocardiography Laboratory: A Large, Multi-site Private Cardiology Practice Experience

Acquisition, interpretation, and storage of digital echocardiographic images has many advantages over the standard videotape-based method. Archival, transmission, and comparative interpretation are all optimized with digital echocardiography. A study performed at one site can be immediately available for viewing and analysis at another site by means of standard data transfer technology. Echocardiograms can be interpreted in the context of prior studies, which are readily available for side-by-side comparison. The transition to an all-digital laboratory involves the commitment of persons at multiple levels in the cardiology practice, including administrators, information technology specialists, sonographers, and physicians. Quality of patient care, use of physicians' and sonographers' time, and long-term financial benefit are all areas where improvement may be realized with the use of digital echocardiography. We present our experience in the development of an all-digital echocardiography laboratory, and we conclude that digital echo-cardiography is practical and can be implemented readily in a clinical setting. We performed several correlative analyses during this transition to validate the consistency and accuracy of digital interpretation compared with those of analog methods. The transition process from analog (videotape) to digital, including full wide area network exchange, took approximately 8 months. As technology advances, issues surrounding storage, comparison, and acquisition formats will continue to develop. We hope that our experience will help others make the transition to the digital environment and benefit from the ease of image access, the ability to comparatively interpret echocardiograms, and the superior image quality afforded by this advancement.     

Optimal embedding for shape indexing in medical image databases

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative.We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance.The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity.Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding.     

Wavelet optimization for content-based image retrieval in medical databases

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system.     

护士对文献检索相关知识与培训的需求与现状调查

目的调查护士对文献检索相关知识与培训的需求与现状,并探讨护士在文献检索方面存在的问题及解决办法。方法采用随机抽样法,于2015年8月随机抽取我院15个科室护士共130名护士,以自行编制的护士对文献检索需求问卷调查表对其进行问卷调查,并进行结果分析。结果 121名护士中,25.6%的护士具备文献检索知识,16.5%的护士曾参加过文献检索相关培训,21.5%的护士参加过2次以上培训;89.3%被调查者希望加强文献检索培训,教学演示视频是最受欢迎的培训形式;52.1%希望每年培训1~2次;在培训师资上,57.8%希望是专职教师或图书情报工作人员。护士对文献检索培训的各项内容的需求均分均在3.98分以上,其中最高为对中文期刊全文数据库进行培训的需求;不同护龄护士在文献检索内容需求上存在统计学差异(P0.01)。结论护士对文献检索相关知识培训的需求比较迫切,在培训形式和方法上,传播性强的方式受到更多人偏好,同时应丰富文献检索培训内容,重点对中文全文数据库进行培训。     

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations

Purpose

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases.

Methods

We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features’ relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan.

Results

Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively.

Conclusions

Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

     

KID, a Kinase Inhibitor Database project.

The Kinase Inhibitor Database is a small specialized database dedicated to the gathering of information on protein kinase inhibitors. The database is accessible through the World Wide Web system and gives access to structural and bibliographic information on protein kinase inhibitors. The data in the database will be collected and submitted by researchers working in the kinase inhibitor field. The submitted data will be checked by the curator of the database before entry.