Enabling search over encrypted multimedia databases

NASA Astrophysics Data System (ADS)

Lu, Wenjun; Swaminathan, Ashwin; Varna, Avinash L.; Wu, Min

2009-02-01

Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a database is stored on a server maintained by a third-party service provider. This paper addresses the problem of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying cryptographic techniques, such as order preserving encryption and randomized hash functions, with image processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security analysis of the secure indexing schemes under different attack models show that data confidentiality can be preserved while retaining very good retrieval performance. This work has promising applications in secure multimedia management.

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

New model for distributed multimedia databases and its application to networking of museums

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1998-02-01

This paper proposes a new distributed multimedia data base system where the databases storing MPEG-2 videos and/or super high definition images are connected together through the B-ISDN's, and also refers to an example of the networking of museums on the basis of the proposed database system. The proposed database system introduces a new concept of the 'retrieval manager' which functions an intelligent controller so that the user can recognize a set of image databases as one logical database. A user terminal issues a request to retrieve contents to the retrieval manager which is located in the nearest place to the user terminal on the network. Then, the retrieved contents are directly sent through the B-ISDN's to the user terminal from the server which stores the designated contents. In this case, the designated logical data base dynamically generates the best combination of such a retrieving parameter as a data transfer path referring to directly or data on the basis of the environment of the system. The generated retrieving parameter is then executed to select the most suitable data transfer path on the network. Therefore, the best combination of these parameters fits to the distributed multimedia database system.

The comparative effectiveness of conventional and digital image libraries.

PubMed

McColl, R I; Johnson, A

2001-03-01

Before introducing a hospital-wide image database to improve access, navigation and retrieval speed, a comparative study between a conventional slide library and a matching image database was undertaken to assess its relative benefits. Paired time trials and personal questionnaires revealed faster retrieval rates, higher image quality, and easier viewing for the pilot digital image database. Analysis of confidentiality, copyright and data protection exposed similar issues for both systems, thus concluding that the digital image database is a more effective library system. The authors suggest that in the future, medical images will be stored on large, professionally administered, centrally located file servers, allowing specialist image libraries to be tailored locally for individual users. The further integration of the database with web technology will enable cheap and efficient remote access for a wide range of users.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Multimedia explorer: image database, image proxy-server and search-engine.

PubMed Central

Frankewitsch, T.; Prokosch, U.

1999-01-01

Multimedia plays a major role in medicine. Databases containing images, movies or other types of multimedia objects are increasing in number, especially on the WWW. However, no good retrieval mechanism or search engine currently exists to efficiently track down such multimedia sources in the vast of information provided by the WWW. Secondly, the tools for searching databases are usually not adapted to the properties of images. HTML pages do not allow complex searches. Therefore establishing a more comfortable retrieval involves the use of a higher programming level like JAVA. With this platform independent language it is possible to create extensions to commonly used web browsers. These applets offer a graphical user interface for high level navigation. We implemented a database using JAVA objects as the primary storage container which are then stored by a JAVA controlled ORACLE8 database. Navigation depends on a structured vocabulary enhanced by a semantic network. With this approach multimedia objects can be encapsulated within a logical module for quick data retrieval. PMID:10566463

Multimedia explorer: image database, image proxy-server and search-engine.

PubMed

Frankewitsch, T; Prokosch, U

1999-01-01

Multimedia plays a major role in medicine. Databases containing images, movies or other types of multimedia objects are increasing in number, especially on the WWW. However, no good retrieval mechanism or search engine currently exists to efficiently track down such multimedia sources in the vast of information provided by the WWW. Secondly, the tools for searching databases are usually not adapted to the properties of images. HTML pages do not allow complex searches. Therefore establishing a more comfortable retrieval involves the use of a higher programming level like JAVA. With this platform independent language it is possible to create extensions to commonly used web browsers. These applets offer a graphical user interface for high level navigation. We implemented a database using JAVA objects as the primary storage container which are then stored by a JAVA controlled ORACLE8 database. Navigation depends on a structured vocabulary enhanced by a semantic network. With this approach multimedia objects can be encapsulated within a logical module for quick data retrieval.

An end to end secure CBIR over encrypted medical database.

PubMed

Bellafqira, Reda; Coatrieux, Gouenou; Bouslimi, Dalel; Quellec, Gwenole

2016-08-01

In this paper, we propose a new secure content based image retrieval (SCBIR) system adapted to the cloud framework. This solution allows a physician to retrieve images of similar content within an outsourced and encrypted image database, without decrypting them. Contrarily to actual CBIR approaches in the encrypted domain, the originality of the proposed scheme stands on the fact that the features extracted from the encrypted images are themselves encrypted. This is achieved by means of homomorphic encryption and two non-colluding servers, we however both consider as honest but curious. In that way an end to end secure CBIR process is ensured. Experimental results carried out on a diabetic retinopathy database encrypted with the Paillier cryptosystem indicate that our SCBIR achieves retrieval performance as good as if images were processed in their non-encrypted form.

Requirements for benchmarking personal image retrieval systems

NASA Astrophysics Data System (ADS)

Bouguet, Jean-Yves; Dulong, Carole; Kozintsev, Igor; Wu, Yi

2006-01-01

It is now common to have accumulated tens of thousands of personal ictures. Efficient access to that many pictures can only be done with a robust image retrieval system. This application is of high interest to Intel processor architects. It is highly compute intensive, and could motivate end users to upgrade their personal computers to the next generations of processors. A key question is how to assess the robustness of a personal image retrieval system. Personal image databases are very different from digital libraries that have been used by many Content Based Image Retrieval Systems.1 For example a personal image database has a lot of pictures of people, but a small set of different people typically family, relatives, and friends. Pictures are taken in a limited set of places like home, work, school, and vacation destination. The most frequent queries are searched for people, and for places. These attributes, and many others affect how a personal image retrieval system should be benchmarked, and benchmarks need to be different from existing ones based on art images, or medical images for examples. The attributes of the data set do not change the list of components needed for the benchmarking of such systems as specified in2: - data sets - query tasks - ground truth - evaluation measures - benchmarking events. This paper proposed a way to build these components to be representative of personal image databases, and of the corresponding usage models.

Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

NASA Astrophysics Data System (ADS)

Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

2001-01-01

Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

NASA Astrophysics Data System (ADS)

Eakins, John P.; Edwards, Jonathan D.; Riley, K. Jonathan; Rosin, Paul L.

2000-12-01

Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

Image Engine: an object-oriented multimedia database for storing, retrieving and sharing medical images and text.

PubMed Central

Lowe, H. J.

1993-01-01

This paper describes Image Engine, an object-oriented, microcomputer-based, multimedia database designed to facilitate the storage and retrieval of digitized biomedical still images, video, and text using inexpensive desktop computers. The current prototype runs on Apple Macintosh computers and allows network database access via peer to peer file sharing protocols. Image Engine supports both free text and controlled vocabulary indexing of multimedia objects. The latter is implemented using the TView thesaurus model developed by the author. The current prototype of Image Engine uses the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary (with UMLS Meta-1 extensions) as its indexing thesaurus. PMID:8130596

Content-based image retrieval from a database of fracture images

NASA Astrophysics Data System (ADS)

Müller, Henning; Do Hoang, Phuong Anh; Depeursinge, Adrien; Hoffmeyer, Pierre; Stern, Richard; Lovis, Christian; Geissbuhler, Antoine

2007-03-01

This article describes the use of a medical image retrieval system on a database of 16'000 fractures, selected from surgical routine over several years. Image retrieval has been a very active domain of research for several years. It was frequently proposed for the medical domain, but only few running systems were ever tested in clinical routine. For the planning of surgical interventions after fractures, x-ray images play an important role. The fractures are classified according to exact fracture location, plus whether and to which degree the fracture is damaging articulations to see how complicated a reparation will be. Several classification systems for fractures exist and the classification plus the experience of the surgeon lead in the end to the choice of surgical technique (screw, metal plate, ...). This choice is strongly influenced by the experience and knowledge of the surgeons with respect to a certain technique. Goal of this article is to describe a prototype that supplies similar cases to an example to help treatment planning and find the most appropriate technique for a surgical intervention. Our database contains over 16'000 fracture images before and after a surgical intervention. We use an image retrieval system (GNU Image Finding Tool, GIFT) to find cases/images similar to an example case currently under observation. Problems encountered are varying illumination of images as well as strong anatomic differences between patients. Regions of interest are usually small and the retrieval system needs to focus on this region. Results show that GIFT is capable of supplying similar cases, particularly when using relevance feedback, on such a large database. Usual image retrieval is based on a single image as search target but for this application we have to select images by case as similar cases need to be found and not images. A few false positive cases often remain in the results but they can be sorted out quickly by the surgeons. Image retrieval can well be used for the planning of operations by supplying similar cases. A variety of challenges has been identified and partly solved (varying luminosity, small region of interested, case-based instead of image-based). This article mainly presents a case study to identify potential benefits and problems. Several steps for improving the system have been identified as well and will be described at the end of the paper.

Space Images for NASA JPL Android Version

NASA Technical Reports Server (NTRS)

Nelson, Jon D.; Gutheinz, Sandy C.; Strom, Joshua R.; Arca, Jeremy M.; Perez, Martin; Boggs, Karen; Stanboli, Alice

2013-01-01

This software addresses the demand for easily accessible NASA JPL images and videos by providing a user friendly and simple graphical user interface that can be run via the Android platform from any location where Internet connection is available. This app is complementary to the iPhone version of the application. A backend infrastructure stores, tracks, and retrieves space images from the JPL Photojournal and Institutional Communications Web server, and catalogs the information into a streamlined rating infrastructure. This system consists of four distinguishing components: image repository, database, server-side logic, and Android mobile application. The image repository contains images from various JPL flight projects. The database stores the image information as well as the user rating. The server-side logic retrieves the image information from the database and categorizes each image for display. The Android mobile application is an interfacing delivery system that retrieves the image information from the server for each Android mobile device user. Also created is a reporting and tracking system for charting and monitoring usage. Unlike other Android mobile image applications, this system uses the latest emerging technologies to produce image listings based directly on user input. This allows for countless combinations of images returned. The backend infrastructure uses industry-standard coding and database methods, enabling future software improvement and technology updates. The flexibility of the system design framework permits multiple levels of display possibilities and provides integration capabilities. Unique features of the software include image/video retrieval from a selected set of categories, image Web links that can be shared among e-mail users, sharing to Facebook/Twitter, marking as user's favorites, and image metadata searchable for instant results.

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

PubMed Central

Muhammad, Khan; Baik, Sung Wook

2017-01-01

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods. PMID:28859140

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Image query and indexing for digital x rays

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Thoma, George R.

1998-12-01

The web-based medical information retrieval system (WebMIRS) allows interned access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examination Surveys (NHANES). WebMIRS allows SQL query of the text, and viewing of the returned text records and images using a standard browser. We are now working (1) to determine utility of data directly derived from the images in our databases, and (2) to investigate the feasibility of computer-assisted or automated indexing of the images to support image retrieval of images of interest to biomedical researchers in the field of osteoarthritis. To build an initial database based on image data, we are manually segmenting a subset of the vertebrae, using techniques from vertebral morphometry. From this, we will derive and add to the database vertebral features. This image-derived data will enhance the user's data access capability by enabling the creation of combined SQL/image-content queries.

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

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

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. 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. 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. 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.

Content based information retrieval in forensic image databases.

PubMed

Geradts, Zeno; Bijhold, Jurrien

2002-03-01

This paper gives an overview of the various available image databases and ways of searching these databases on image contents. The developments in research groups of searching in image databases is evaluated and compared with the forensic databases that exist. Forensic image databases of fingerprints, faces, shoeprints, handwriting, cartridge cases, drugs tablets, and tool marks are described. The developments in these fields appear to be valuable for forensic databases, especially that of the framework in MPEG-7, where the searching in image databases is standardized. In the future, the combination of the databases (also DNA-databases) and possibilities to combine these can result in stronger forensic evidence.

Automated search and retrieval of information from imaged documents using optical correlation techniques

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-10-01

Litton PRC and Litton Data Systems Division are developing a system, the Imaged Document Optical Correlation and Conversion System (IDOCCS), to provide a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provides the search and retrieval of information from imaged documents. IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited; e.g., imaged documents containing an agency's seal or logo can be singled out. In this paper, we present a description of IDOCCS as well as preliminary performance results and theoretical projections.

Using an image-extended relational database to support content-based image retrieval in a PACS.

PubMed

Traina, Caetano; Traina, Agma J M; Araújo, Myrian R B; Bueno, Josiane M; Chino, Fabio J T; Razente, Humberto; Azevedo-Marques, Paulo M

2005-12-01

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

Classroom Laboratory Report: Using an Image Database System in Engineering Education.

ERIC Educational Resources Information Center

Alam, Javed; And Others

1991-01-01

Describes an image database system assembled using separate computer components that was developed to overcome text-only computer hardware storage and retrieval limitations for a pavement design class. (JJK)

Storage and retrieval of digital images in dermatology.

PubMed

Bittorf, A; Krejci-Papa, N C; Diepgen, T L

1995-11-01

Differential diagnosis in dermatology relies on the interpretation of visual information in the form of clinical and histopathological images. Up until now, reference images have had to be retrieved from textbooks and/or appropriate journals. To overcome inherent limitations of those storage media with respect to the number of images stored, display, and search parameters available, we designed a computer-based database of digitized dermatologic images. Images were taken from the photo archive of the Dermatological Clinic of the University of Erlangen. A database was designed using the Entity-Relationship approach. It was implemented on a PC-Windows platform using MS Access* and MS Visual Basic®. As WWW-server a Sparc 10 workstation was used with the CERN Hypertext-Transfer-Protocol-Daemon (httpd) 3.0 pre 6 software running. For compressed storage on a hard drive, a quality factor of 60 allowed on-screen differential diagnosis and corresponded to a compression factor of 1:35 for clinical images and 1:40 for histopathological images. Hierarchical keys of clinical or histopathological criteria permitted multi-criteria searches. A script using the Common Gateway Interface (CGI) enabled remote search and image retrieval via the World-Wide-Web (W3). A dermatologic image database, featurig clinical and histopathological images was constructed which allows for multi-parameter searches and world-wide remote access.

Architecture for biomedical multimedia information delivery on the World Wide Web

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Goh, Gin-Hua; Neve, Leif; Thoma, George R.

1997-10-01

Research engineers at the National Library of Medicine are building a prototype system for the delivery of multimedia biomedical information on the World Wide Web. This paper discuses the architecture and design considerations for the system, which will be used initially to make images and text from the third National Health and Nutrition Examination Survey (NHANES) publicly available. We categorized our analysis as follows: (1) fundamental software tools: we analyzed trade-offs among use of conventional HTML/CGI, X Window Broadway, and Java; (2) image delivery: we examined the use of unconventional TCP transmission methods; (3) database manager and database design: we discuss the capabilities and planned use of the Informix object-relational database manager and the planned schema for the HNANES database; (4) storage requirements for our Sun server; (5) user interface considerations; (6) the compatibility of the system with other standard research and analysis tools; (7) image display: we discuss considerations for consistent image display for end users. Finally, we discuss the scalability of the system in terms of incorporating larger or more databases of similar data, and the extendibility of the system for supporting content-based retrieval of biomedical images. The system prototype is called the Web-based Medical Information Retrieval System. An early version was built as a Java applet and tested on Unix, PC, and Macintosh platforms. This prototype used the MiniSQL database manager to do text queries on a small database of records of participants in the second NHANES survey. The full records and associated x-ray images were retrievable and displayable on a standard Web browser. A second version has now been built, also a Java applet, using the MySQL database manager.

Partitioning medical image databases for content-based queries on a Grid.

PubMed

Montagnat, J; Breton, V; E Magnin, I

2005-01-01

In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Grids are promising for content-based image retrieval in medical databases.

Interactive radiographic image retrieval system.

PubMed

Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

2017-02-01

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A normative database and determinants of lexical retrieval for 186 Arabic nouns: effects of psycholinguistic and morpho-syntactic variables on naming latency.

PubMed

Khwaileh, Tariq; Body, Richard; Herbert, Ruth

2014-12-01

Research into lexical retrieval requires pictorial stimuli standardised for key psycholinguistic variables. Such databases exist in a number of languages but not in Arabic. In addition there are few studies of the effects of psycholinguistic and morpho-syntactic variables on Arabic lexical retrieval. The current study identified a set of culturally and linguistically appropriate concept labels, and corresponding photographic representations for Levantine Arabic. The set included masculine and feminine nouns, nouns from both types of plural formation (sound and broken), and both rational and irrational nouns. Levantine Arabic speakers provided norms for visual complexity, imageability, age of acquisition, naming latency and name agreement. This delivered a normative database for a set of 186 Arabic nouns. The effects of the morpho-syntactic and the psycholinguistic variables on lexical retrieval were explored using the database. Imageability and age of acquisition were the only significant determinants of successful lexical retrieval in Arabic. None of the other variables, including all the linguistic variables, had any effect on production time. The normative database is available for the use of clinicians and researchers in the Arab world in the domains of speech and language pathology, neurolinguistics and psycholinguistics. The database and the photographic representations will be soon available for free download from the first author's personal webpage or via email.

Estimating Missing Features to Improve Multimedia Information Retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bagherjeiran, A; Love, N S; Kamath, C

Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features.more » In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query.« less

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

Brain CT image similarity retrieval method based on uncertain location graph.

PubMed

Pan, Haiwei; Li, Pengyuan; Li, Qing; Han, Qilong; Feng, Xiaoning; Gao, Linlin

2014-03-01

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Managing biomedical image metadata for search and retrieval of similar images.

PubMed

Korenblum, Daniel; Rubin, Daniel; Napel, Sandy; Rodriguez, Cesar; Beaulieu, Chris

2011-08-01

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Image-Based Airborne LiDAR Point Cloud Encoding for 3d Building Model Retrieval

NASA Astrophysics Data System (ADS)

Chen, Yi-Chen; Lin, Chao-Hung

2016-06-01

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design.

ERIC Educational Resources Information Center

Stephenson, Christie

1999-01-01

Examines the Museum Educational Site Licensing (MESL) Project--a cooperative project between seven cultural heritage repositories and seven universities--as well as other developments of cultural heritage image databases for academic use. Reviews recent literature on image indexing and retrieval, interface design, and tool development, urging a…

EROS Main Image File: A Picture Perfect Database for Landsat Imagery and Aerial Photography.

ERIC Educational Resources Information Center

Jack, Robert F.

1984-01-01

Describes Earth Resources Observation System online database, which provides access to computerized images of Earth obtained via satellite. Highlights include retrieval system and commands, types of images, search strategies, other online functions, and interpretation of accessions. Satellite information, sources and samples of accessions, and…

An intelligent framework for medical image retrieval using MDCT and multi SVM.

PubMed

Balan, J A Alex Rajju; Rajan, S Edward

2014-01-01

Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

Li, Wenxin; Zhang, David; Xu, Z.; You, J.

2000-12-01

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

Li, Wenxin; Zhang, David; Xu, Z.; You, J.

2001-01-01

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of furniture images selection based on neural network

NASA Astrophysics Data System (ADS)

Wang, Yong; Gao, Wenwen; Wang, Ying

2018-05-01

In the construction of 2 million furniture image databases, aiming at the problem of low quality of database, a combination of CNN and Metric learning algorithm is proposed, which makes it possible to quickly and accurately remove duplicate and irrelevant samples in the furniture image database. Solve problems that images screening method is complex, the accuracy is not high, time-consuming is long. Deep learning algorithm achieve excellent image matching ability in actual furniture retrieval applications after improving data quality.

EROS main image file - A picture perfect database for Landsat imagery and aerial photography

NASA Technical Reports Server (NTRS)

Jack, R. F.

1984-01-01

The Earth Resources Observation System (EROS) Program was established by the U.S. Department of the Interior in 1966 under the administration of the Geological Survey. It is primarily concerned with the application of remote sensing techniques for the management of natural resources. The retrieval system employed to search the EROS database is called INORAC (Inquiry, Ordering, and Accounting). A description is given of the types of images identified in EROS, taking into account Landsat imagery, Skylab images, Gemini/Apollo photography, and NASA aerial photography. Attention is given to retrieval commands, geographic coordinate searching, refinement techniques, various online functions, and questions regarding the access to the EROS Main Image File.

Method for the reduction of image content redundancy in large image databases

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

A method of increasing information content for content-based image retrieval (CBIR) systems includes the steps of providing a CBIR database, the database having an index for a plurality of stored digital images using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the images. A visual similarity parameter value is calculated based on a degree of visual similarity between features vectors of an incoming image being considered for entry into the database and feature vectors associated with a most similar of the stored images. Based on said visual similarity parameter value it is determined whether to store or how long to store the feature vectors associated with the incoming image in the database.

An improved real time image detection system for elephant intrusion along the forest border areas.

PubMed

Sugumar, S J; Jayaparvathy, R

2014-01-01

Human-elephant conflict is a major problem leading to crop damage, human death and injuries caused by elephants, and elephants being killed by humans. In this paper, we propose an automated unsupervised elephant image detection system (EIDS) as a solution to human-elephant conflict in the context of elephant conservation. The elephant's image is captured in the forest border areas and is sent to a base station via an RF network. The received image is decomposed using Haar wavelet to obtain multilevel wavelet coefficients, with which we perform image feature extraction and similarity match between the elephant query image and the database image using image vision algorithms. A GSM message is sent to the forest officials indicating that an elephant has been detected in the forest border and is approaching human habitat. We propose an optimized distance metric to improve the image retrieval time from the database. We compare the optimized distance metric with the popular Euclidean and Manhattan distance methods. The proposed optimized distance metric retrieves more images with lesser retrieval time than the other distance metrics which makes the optimized distance method more efficient and reliable.

Unsupervised symmetrical trademark image retrieval in soccer telecast using wavelet energy and quadtree decomposition

NASA Astrophysics Data System (ADS)

Ong, Swee Khai; Lim, Wee Keong; Soo, Wooi King

2013-04-01

Trademark, a distinctive symbol, is used to distinguish products or services provided by a particular person, group or organization from other similar entries. As trademark represents the reputation and credit standing of the owner, it is important to differentiate one trademark from another. Many methods have been proposed to identify, classify and retrieve trademarks. However, most methods required features database and sample sets for training prior to recognition and retrieval process. In this paper, a new feature on wavelet coefficients, the localized wavelet energy, is introduced to extract features of trademarks. With this, unsupervised content-based symmetrical trademark image retrieval is proposed without the database and prior training set. The feature analysis is done by an integration of the proposed localized wavelet energy and quadtree decomposed regional symmetrical vector. The proposed framework eradicates the dependence on query database and human participation during the retrieval process. In this paper, trademarks for soccer games sponsors are the intended trademark category. Video frames from soccer telecast are extracted and processed for this study. Reasonably good localization and retrieval results on certain categories of trademarks are achieved. A distinctive symbol is used to distinguish products or services provided by a particular person, group or organization from other similar entries.

Optimal Embedding for Shape Indexing in Medical Image Databases

PubMed Central

Qian, Xiaoning; Tagare, Hemant D.; Fulbright, Robert K.; Long, Rodney; Antani, Sameer

2010-01-01

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. PMID:20163981

Optimal embedding for shape indexing in medical image databases.

PubMed

Qian, Xiaoning; Tagare, Hemant D; Fulbright, Robert K; Long, Rodney; Antani, Sameer

2010-06-01

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. Copyright (c) 2010 Elsevier B.V. All rights reserved.

A new method for the automatic retrieval of medical cases based on the RadLex ontology.

PubMed

Spanier, A B; Cohen, D; Joskowicz, L

2017-03-01

The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

Informatics in radiology: use of CouchDB for document-based storage of DICOM objects.

PubMed

Rascovsky, Simón J; Delgado, Jorge A; Sanz, Alexander; Calvo, Víctor D; Castrillón, Gabriel

2012-01-01

Picture archiving and communication systems traditionally have depended on schema-based Structured Query Language (SQL) databases for imaging data management. To optimize database size and performance, many such systems store a reduced set of Digital Imaging and Communications in Medicine (DICOM) metadata, discarding informational content that might be needed in the future. As an alternative to traditional database systems, document-based key-value stores recently have gained popularity. These systems store documents containing key-value pairs that facilitate data searches without predefined schemas. Document-based key-value stores are especially suited to archive DICOM objects because DICOM metadata are highly heterogeneous collections of tag-value pairs conveying specific information about imaging modalities, acquisition protocols, and vendor-supported postprocessing options. The authors used an open-source document-based database management system (Apache CouchDB) to create and test two such databases; CouchDB was selected for its overall ease of use, capability for managing attachments, and reliance on HTTP and Representational State Transfer standards for accessing and retrieving data. A large database was created first in which the DICOM metadata from 5880 anonymized magnetic resonance imaging studies (1,949,753 images) were loaded by using a Ruby script. To provide the usual DICOM query functionality, several predefined "views" (standard queries) were created by using JavaScript. For performance comparison, the same queries were executed in both the CouchDB database and a SQL-based DICOM archive. The capabilities of CouchDB for attachment management and database replication were separately assessed in tests of a similar, smaller database. Results showed that CouchDB allowed efficient storage and interrogation of all DICOM objects; with the use of information retrieval algorithms such as map-reduce, all the DICOM metadata stored in the large database were searchable with only a minimal increase in retrieval time over that with the traditional database management system. Results also indicated possible uses for document-based databases in data mining applications such as dose monitoring, quality assurance, and protocol optimization. RSNA, 2012

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

An ECG storage and retrieval system embedded in client server HIS utilizing object-oriented DB.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-02-01

In the University of Tokyo Hospital, the improved client server HIS has been applied to clinical practice and physicians can order prescription, laboratory examination, ECG examination and radiographic examination, etc. directly by themselves and read results of these examinations, except medical signal waves, schema and image, on UNIX workstations. Recently, we designed and developed an ECG storage and retrieval system embedded in the client server HIS utilizing object-oriented database to take the first step in dealing with digitized signal, schema and image data and show waves, graphics, and images directly to physicians by the client server HIS. The system was developed based on object-oriented analysis and design, and implemented with object-oriented database management system (OODMS) and C++ programming language. In this paper, we describe the ECG data model, functions of the storage and retrieval system, features of user interface and the result of its implementation in the HIS.

Intelligent web image retrieval system

NASA Astrophysics Data System (ADS)

Hong, Sungyong; Lee, Chungwoo; Nah, Yunmook

2001-07-01

Recently, the web sites such as e-business sites and shopping mall sites deal with lots of image information. To find a specific image from these image sources, we usually use web search engines or image database engines which rely on keyword only retrievals or color based retrievals with limited search capabilities. This paper presents an intelligent web image retrieval system. We propose the system architecture, the texture and color based image classification and indexing techniques, and representation schemes of user usage patterns. The query can be given by providing keywords, by selecting one or more sample texture patterns, by assigning color values within positional color blocks, or by combining some or all of these factors. The system keeps track of user's preferences by generating user query logs and automatically add more search information to subsequent user queries. To show the usefulness of the proposed system, some experimental results showing recall and precision are also explained.

Image retrieval for identifying house plants

NASA Astrophysics Data System (ADS)

Kebapci, Hanife; Yanikoglu, Berrin; Unal, Gozde

2010-02-01

We present a content-based image retrieval system for plant identification which is intended for providing users with a simple method to locate information about their house plants. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, texture and shape features for this problem, as well as introducing some new ones. The features are extracted from the general plant region that is segmented from the background using the max-flow min-cut technique. Results on a database of 132 different plant images show promise (in about 72% of the queries, the correct plant image is retrieved among the top-15 results).

A novel method for efficient archiving and retrieval of biomedical images using MPEG-7

NASA Astrophysics Data System (ADS)

Meyer, Joerg; Pahwa, Ash

2004-10-01

Digital archiving and efficient retrieval of radiological scans have become critical steps in contemporary medical diagnostics. Since more and more images and image sequences (single scans or video) from various modalities (CT/MRI/PET/digital X-ray) are now available in digital formats (e.g., DICOM-3), hospitals and radiology clinics need to implement efficient protocols capable of managing the enormous amounts of data generated daily in a typical clinical routine. We present a method that appears to be a viable way to eliminate the tedious step of manually annotating image and video material for database indexing. MPEG-7 is a new framework that standardizes the way images are characterized in terms of color, shape, and other abstract, content-related criteria. A set of standardized descriptors that are automatically generated from an image is used to compare an image to other images in a database, and to compute the distance between two images for a given application domain. Text-based database queries can be replaced with image-based queries using MPEG-7. Consequently, image queries can be conducted without any prior knowledge of the keys that were used as indices in the database. Since the decoding and matching steps are not part of the MPEG-7 standard, this method also enables searches that were not planned by the time the keys were generated.

Imaged Document Optical Correlation and Conversion System (IDOCCS)

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-03-01

Today, the paper document is fast becoming a thing of the past. With the rapid development of fast, inexpensive computing and storage devices, many government and private organizations are archiving their documents in electronic form (e.g., personnel records, medical records, patents, etc.). In addition, many organizations are converting their paper archives to electronic images, which are stored in a computer database. Because of this, there is a need to efficiently organize this data into comprehensive and accessible information resources. The Imaged Document Optical Correlation and Conversion System (IDOCCS) provides a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provides the search and retrieval capability of document images. The IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives and can even determine the types of languages contained within a document. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited, e.g., imaged documents containing an agency's seal or logo, or documents with a particular individual's signature block, can be singled out. With this dual capability, IDOCCS outperforms systems that rely on optical character recognition as a basis for indexing and storing only the textual content of documents for later retrieval.

View subspaces for indexing and retrieval of 3D models

NASA Astrophysics Data System (ADS)

Dutagaci, Helin; Godil, Afzal; Sankur, Bülent; Yemez, Yücel

2010-02-01

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu

2017-07-01

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

NASA Astrophysics Data System (ADS)

Zhang, Hai; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Huang, Jingfeng; Superczynski, Stephen; Ciren, Pubu

2016-09-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S-NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of -0.008, and standard deviation (STD) of error of 0.139 when compared against the ground-based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root-mean-square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

NASA Astrophysics Data System (ADS)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

Image databases: Problems and perspectives

NASA Technical Reports Server (NTRS)

Gudivada, V. Naidu

1989-01-01

With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined.

A database system to support image algorithm evaluation

NASA Technical Reports Server (NTRS)

Lien, Y. E.

1977-01-01

The design is given of an interactive image database system IMDB, which allows the user to create, retrieve, store, display, and manipulate images through the facility of a high-level, interactive image query (IQ) language. The query language IQ permits the user to define false color functions, pixel value transformations, overlay functions, zoom functions, and windows. The user manipulates the images through generic functions. The user can direct images to display devices for visual and qualitative analysis. Image histograms and pixel value distributions can also be computed to obtain a quantitative analysis of images.

Developing a comprehensive system for content-based retrieval of image and text data from a national survey

NASA Astrophysics Data System (ADS)

Antani, Sameer K.; Natarajan, Mukil; Long, Jonathan L.; Long, L. Rodney; Thoma, George R.

2005-04-01

The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES II). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data; flexibility to incorporate new research work; provides a means to control access to system components under development; and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.

A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

NASA Astrophysics Data System (ADS)

Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

2015-03-01

This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

A statistical retrieval of cloud parameters for the millimeter wave Ice Cloud Imager on board MetOp-SG

NASA Astrophysics Data System (ADS)

Prigent, Catherine; Wang, Die; Aires, Filipe; Jimenez, Carlos

2017-04-01

The meteorological observations from satellites in the microwave domain are currently limited to below 190 GHz. However, the next generation of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System-Second Generation-EPS-SG will carry an instrument, the Ice Cloud Imager (ICI), with frequencies up to 664 GHz, to improve the characterization of the cloud frozen phase. In this paper, a statistical retrieval of cloud parameters for ICI is developed, trained on a synthetic database derived from the coupling of a mesoscale cloud model and radiative transfer calculations. The hydrometeor profiles simulated with the Weather Research and Forecasting model (WRF) for twelve diverse European mid-latitude situations are used to simulate the brightness temperatures with the Atmospheric Radiative Transfer Simulator (ARTS) to prepare the retrieval database. The WRF+ARTS simulations have been compared to the Special Sensor Microwave Imager/Sounder (SSMIS) observations up to 190 GHz: this successful evaluation gives us confidence in the simulations at the ICI channels from 183 to 664 GHz. Statistical analyses have been performed on this simulated retrieval database, showing that it is not only physically realistic but also statistically satisfactory for retrieval purposes. A first Neural Network (NN) classifier is used to detect the cloud presence. A second NN is developed to retrieve the liquid and ice integrated cloud quantities over sea and land separately. The detection and retrieval of the hydrometeor quantities (i.e., ice, snow, graupel, rain, and liquid cloud) are performed with ICI-only, and with ICI combined with observations from the MicroWave Imager (MWI, with frequencies from 19 to 190 GHz, also on board MetOp-SG). The ICI channels have been optimized for the detection and quantification of the cloud frozen phases: adding the MWI channels improves the performance of the vertically integrated hydrometeor contents, especially for the cloud liquid phases. The relative error for the retrieved integrated frozen water content (FWP, i.e., ice+snow+graupel) is below 40% for 0.1kg/m2 < FWP < 0.5kg/m2 and below 20% for FWP > 0.5 kg/m2.

Feature hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

PubMed Central

Sparks, Rachel; Madabhushi, Anant

2016-01-01

Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

Psychophysical studies of the performance of an image database retrieval system

NASA Astrophysics Data System (ADS)

Papathomas, Thomas V.; Conway, Tiffany E.; Cox, Ingemar J.; Ghosn, Joumana; Miller, Matt L.; Minka, Thomas P.; Yianilos, Peter N.

1998-07-01

We describe psychophysical experiments conducted to study PicHunter, a content-based image retrieval (CBIR) system. Experiment 1 studies the importance of using (a) semantic information, (2) memory of earlier input and (3) relative, rather than absolute, judgements of image similarity. The target testing paradigm is used in which a user must search for an image identical to a target. We find that the best performance comes from a version of PicHunter that uses only semantic cues, with memory and relative similarity judgements. Second best is use of both pictorial and semantic cues, with memory and relative similarity judgements. Most reports of CBIR systems provide only qualitative measures of performance based on how similar retrieved images are to a target. Experiment 2 puts PicHunter into this context with a more rigorous test. We first establish a baseline for our database by measuring the time required to find an image that is similar to a target when the images are presented in random order. Although PicHunter's performance is measurably better than this, the test is weak because even random presentation of images yields reasonably short search times. This casts doubt on the strength of results given in other reports where no baseline is established.

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Medical Image Databases

PubMed Central

Tagare, Hemant D.; Jaffe, C. Carl; Duncan, James

1997-01-01

Abstract Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema. PMID:9147338

Toward privacy-preserving JPEG image retrieval

NASA Astrophysics Data System (ADS)

Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

2017-07-01

This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

A digital library for medical imaging activities

NASA Astrophysics Data System (ADS)

dos Santos, Marcelo; Furuie, Sérgio S.

2007-03-01

This work presents the development of an electronic infrastructure to make available a free, online, multipurpose and multimodality medical image database. The proposed infrastructure implements a distributed architecture for medical image database, authoring tools, and a repository for multimedia documents. Also it includes a peer-reviewed model that assures quality of dataset. This public repository provides a single point of access for medical images and related information to facilitate retrieval tasks. The proposed approach has been used as an electronic teaching system in Radiology as well.

Measurement of tag confidence in user generated contents retrieval

NASA Astrophysics Data System (ADS)

Lee, Sihyoung; Min, Hyun-Seok; Lee, Young Bok; Ro, Yong Man

2009-01-01

As online image sharing services are becoming popular, the importance of correctly annotated tags is being emphasized for precise search and retrieval. Tags created by user along with user-generated contents (UGC) are often ambiguous due to the fact that some tags are highly subjective and visually unrelated to the image. They cause unwanted results to users when image search engines rely on tags. In this paper, we propose a method of measuring tag confidence so that one can differentiate confidence tags from noisy tags. The proposed tag confidence is measured from visual semantics of the image. To verify the usefulness of the proposed method, experiments were performed with UGC database from social network sites. Experimental results showed that the image retrieval performance with confidence tags was increased.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

An Intelligent Pictorial Information System

NASA Astrophysics Data System (ADS)

Lee, Edward T.; Chang, B.

1987-05-01

In examining the history of computer application, we discover that early computer systems were developed primarily for applications related to scientific computation, as in weather prediction, aerospace applications, and nuclear physics applications. At this stage, the computer system served as a big calculator to perform, in the main, manipulation of numbers. Then it was found that computer systems could also be used for business applications, information storage and retrieval, word processing, and report generation. The history of computer application is summarized in Table I. The complexity of pictures makes picture processing much more difficult than number and alphanumerical processing. Therefore, new techniques, new algorithms, and above all, new pictorial knowledge, [1] are needed to overcome the limitatins of existing computer systems. New frontiers in designing computer systems are the ways to handle the representation,[2,3] classification, manipulation, processing, storage, and retrieval of pictures. Especially, the ways to deal with similarity measures and the meaning of the word "approximate" and the phrase "approximate reasoning" are an important and an indispensable part of an intelligent pictorial information system. [4,5] The main objective of this paper is to investigate the mathematical foundation for the effective organization and efficient retrieval of pictures in similarity-directed pictorial databases, [6] based on similarity retrieval techniques [7] and fuzzy languages [8]. The main advantage of this approach is that similar pictures are stored logically close to each other by using quantitative similarity measures. Thus, for answering queries, the amount of picture data needed to be searched can be reduced and the retrieval time can be improved. In addition, in a pictorial database, very often it is desired to find pictures (or feature vectors, histograms, etc.) that are most similar to or most dissimilar [9] to a test picture (or feature vector). Using similarity measures, one can not only store similar pictures logically or physically close to each other in order to improve retrieval or updating efficiency, one can also use such similarity measures to answer fuzzy queries involving nonexact retrieval conditions. In this paper, similarity directed pictorial databases involving geometric figures, chromosome images, [10] leukocyte images, cardiomyopathy images, and satellite images [11] are presented as illustrative examples.

Image/text automatic indexing and retrieval system using context vector approach

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Caid, William R.; Ren, Clara Z.; McCabe, Patrick

1995-11-01

Thousands of documents and images are generated daily both on and off line on the information superhighway and other media. Storage technology has improved rapidly to handle these data but indexing this information is becoming very costly. HNC Software Inc. has developed a technology for automatic indexing and retrieval of free text and images. This technique is demonstrated and is based on the concept of `context vectors' which encode a succinct representation of the associated text and features of sub-image. In this paper, we will describe the Automated Librarian System which was designed for free text indexing and the Image Content Addressable Retrieval System (ICARS) which extends the technique from the text domain into the image domain. Both systems have the ability to automatically assign indices for a new document and/or image based on the content similarities in the database. ICARS also has the capability to retrieve images based on similarity of content using index terms, text description, and user-generated images as a query without performing segmentation or object recognition.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

A cloud and radiation model-based algorithm for rainfall retrieval from SSM/I multispectral microwave measurements

NASA Technical Reports Server (NTRS)

Xiang, Xuwu; Smith, Eric A.; Tripoli, Gregory J.

1992-01-01

A hybrid statistical-physical retrieval scheme is explored which combines a statistical approach with an approach based on the development of cloud-radiation models designed to simulate precipitating atmospheres. The algorithm employs the detailed microphysical information from a cloud model as input to a radiative transfer model which generates a cloud-radiation model database. Statistical procedures are then invoked to objectively generate an initial guess composite profile data set from the database. The retrieval algorithm has been tested for a tropical typhoon case using Special Sensor Microwave/Imager (SSM/I) data and has shown satisfactory results.

Incorporating the APS Catalog of the POSS I and Image Archive in ADS

NASA Technical Reports Server (NTRS)

Humphreys, Roberta M.

1998-01-01

The primary purpose of this contract was to develop the software to both create and access an on-line database of images from digital scans of the Palomar Sky Survey. This required modifying our DBMS (called Star Base) to create an image database from the actual raw pixel data from the scans. The digitized images are processed into a set of coordinate-reference index and pixel files that are stored in run-length files, thus achieving an efficient lossless compression. For efficiency and ease of referencing, each digitized POSS I plate is then divided into 900 subplates. Our custom DBMS maps each query into the corresponding POSS plate(s) and subplate(s). All images from the appropriate subplates are retrieved from disk with byte-offsets taken from the index files. These are assembled on-the-fly into a GIF image file for browser display, and a FITS format image file for retrieval. The FITS images have a pixel size of 0.33 arcseconds. The FITS header contains astrometric and photometric information. This method keeps the disk requirements manageable while allowing for future improvements. When complete, the APS Image Database will contain over 130 Gb of data. A set of web pages query forms are available on-line, as well as an on-line tutorial and documentation. The database is distributed to the Internet by a high-speed SGI server and a high-bandwidth disk system. URL is http://aps.umn.edu/IDB/. The image database software is written in perl and C and has been compiled on SGI computers with MIX5.3. A copy of the written documentation is included and the software is on the accompanying exabyte tape.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

A novel 3D shape descriptor for automatic retrieval of anatomical structures from medical images

NASA Astrophysics Data System (ADS)

Nunes, Fátima L. S.; Bergamasco, Leila C. C.; Delmondes, Pedro H.; Valverde, Miguel A. G.; Jackowski, Marcel P.

2017-03-01

Content-based image retrieval (CBIR) aims at retrieving from a database objects that are similar to an object provided by a query, by taking into consideration a set of extracted features. While CBIR has been widely applied in the two-dimensional image domain, the retrieval of3D objects from medical image datasets using CBIR remains to be explored. In this context, the development of descriptors that can capture information specific to organs or structures is desirable. In this work, we focus on the retrieval of two anatomical structures commonly imaged by Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques, the left ventricle of the heart and blood vessels. Towards this aim, we developed the Area-Distance Local Descriptor (ADLD), a novel 3D local shape descriptor that employs mesh geometry information, namely facet area and distance from centroid to surface, to identify shape changes. Because ADLD only considers surface meshes extracted from volumetric medical images, it substantially diminishes the amount of data to be analyzed. A 90% precision rate was obtained when retrieving both convex (left ventricle) and non-convex structures (blood vessels), allowing for detection of abnormalities associated with changes in shape. Thus, ADLD has the potential to aid in the diagnosis of a wide range of vascular and cardiac diseases.

Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library.

PubMed

Chen, Yang; Ren, Xiaofeng; Zhang, Guo-Qiang; Xu, Rong

2013-01-01

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

DICOM-compliant PACS with CD-based image archival

NASA Astrophysics Data System (ADS)

Cox, Robert D.; Henri, Christopher J.; Rubin, Richard K.; Bret, Patrice M.

1998-07-01

This paper describes the design and implementation of a low- cost PACS conforming to the DICOM 3.0 standard. The goal was to provide an efficient image archival and management solution on a heterogeneous hospital network as a basis for filmless radiology. The system follows a distributed, client/server model and was implemented at a fraction of the cost of a commercial PACS. It provides reliable archiving on recordable CD and allows access to digital images throughout the hospital and on the Internet. Dedicated servers have been designed for short-term storage, CD-based archival, data retrieval and remote data access or teleradiology. The short-term storage devices provide DICOM storage and query/retrieve services to scanners and workstations and approximately twelve weeks of 'on-line' image data. The CD-based archival and data retrieval processes are fully automated with the exception of CD loading and unloading. The system employs lossless compression on both short- and long-term storage devices. All servers communicate via the DICOM protocol in conjunction with both local and 'master' SQL-patient databases. Records are transferred from the local to the master database independently, ensuring that storage devices will still function if the master database server cannot be reached. The system features rules-based work-flow management and WWW servers to provide multi-platform remote data access. The WWW server system is distributed on the storage, retrieval and teleradiology servers allowing viewing of locally stored image data directly in a WWW browser without the need for data transfer to a central WWW server. An independent system monitors disk usage, processes, network and CPU load on each server and reports errors to the image management team via email. The PACS was implemented using a combination of off-the-shelf hardware, freely available software and applications developed in-house. The system has enabled filmless operation in CT, MR and ultrasound within the radiology department and throughout the hospital. The use of WWW technology has enabled the development of an intuitive we- based teleradiology and image management solution that provides complete access to image data.

Multi-clues image retrieval based on improved color invariants

NASA Astrophysics Data System (ADS)

Liu, Liu; Li, Jian-Xun

2012-05-01

At present, image retrieval has a great progress in indexing efficiency and memory usage, which mainly benefits from the utilization of the text retrieval technology, such as the bag-of-features (BOF) model and the inverted-file structure. Meanwhile, because the robust local feature invariants are selected to establish BOF, the retrieval precision of BOF is enhanced, especially when it is applied to a large-scale database. However, these local feature invariants mainly consider the geometric variance of the objects in the images, and thus the color information of the objects fails to be made use of. Because of the development of the information technology and Internet, the majority of our retrieval objects is color images. Therefore, retrieval performance can be further improved through proper utilization of the color information. We propose an improved method through analyzing the flaw of shadow-shading quasi-invariant. The response and performance of shadow-shading quasi-invariant for the object edge with the variance of lighting are enhanced. The color descriptors of the invariant regions are extracted and integrated into BOF based on the local feature. The robustness of the algorithm and the improvement of the performance are verified in the final experiments.

Ocean Optical Database

DTIC Science & Technology

1992-05-01

ocean color for retrieving ocean k(490) values are examined. The validation of the optical database from the satellite is accessed through comparison...for sharing results of this validation study. We wish to thank J. Mueller for helpful discussions in optics and satellite processing and for sharing his...of these data products are displayable as 512 x 512 8-bit image maps compatible with the PC-SeaPak image format. Valid data ranges are from 1 to 255

Fast large-scale object retrieval with binary quantization

NASA Astrophysics Data System (ADS)

Zhou, Shifu; Zeng, Dan; Shen, Wei; Zhang, Zhijiang; Tian, Qi

2015-11-01

The objective of large-scale object retrieval systems is to search for images that contain the target object in an image database. Where state-of-the-art approaches rely on global image representations to conduct searches, we consider many boxes per image as candidates to search locally in a picture. In this paper, a feature quantization algorithm called binary quantization is proposed. In binary quantization, a scale-invariant feature transform (SIFT) feature is quantized into a descriptive and discriminative bit-vector, which allows itself to adapt to the classic inverted file structure for box indexing. The inverted file, which stores the bit-vector and box ID where the SIFT feature is located inside, is compact and can be loaded into the main memory for efficient box indexing. We evaluate our approach on available object retrieval datasets. Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality. Therefore, the proposed approach is an improvement over state-of-the-art approaches for object retrieval.

Content-Based Management of Image Databases in the Internet Age

ERIC Educational Resources Information Center

Kleban, James Theodore

2010-01-01

The Internet Age has seen the emergence of richly annotated image data collections numbering in the billions of items. This work makes contributions in three primary areas which aid the management of this data: image representation, efficient retrieval, and annotation based on content and metadata. The contributions are as follows. First,…

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

PubMed

Tourassi, Georgia D; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y; Floyd, Carey E

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

New Software for Ensemble Creation in the Spitzer-Space-Telescope Operations Database

NASA Technical Reports Server (NTRS)

Laher, Russ; Rector, John

2004-01-01

Some of the computer pipelines used to process digital astronomical images from NASA's Spitzer Space Telescope require multiple input images, in order to generate high-level science and calibration products. The images are grouped into ensembles according to well documented ensemble-creation rules by making explicit associations in the operations Informix database at the Spitzer Science Center (SSC). The advantage of this approach is that a simple database query can retrieve the required ensemble of pipeline input images. New and improved software for ensemble creation has been developed. The new software is much faster than the existing software because it uses pre-compiled database stored-procedures written in Informix SPL (SQL programming language). The new software is also more flexible because the ensemble creation rules are now stored in and read from newly defined database tables. This table-driven approach was implemented so that ensemble rules can be inserted, updated, or deleted without modifying software.

Multimedia human brain database system for surgical candidacy determination in temporal lobe epilepsy with content-based image retrieval

NASA Astrophysics Data System (ADS)

Siadat, Mohammad-Reza; Soltanian-Zadeh, Hamid; Fotouhi, Farshad A.; Elisevich, Kost

2003-01-01

This paper presents the development of a human brain multimedia database for surgical candidacy determination in temporal lobe epilepsy. The focus of the paper is on content-based image management, navigation and retrieval. Several medical image-processing methods including our newly developed segmentation method are utilized for information extraction/correlation and indexing. The input data includes T1-, T2-Weighted MRI and FLAIR MRI and ictal and interictal SPECT modalities with associated clinical data and EEG data analysis. The database can answer queries regarding issues such as the correlation between the attribute X of the entity Y and the outcome of a temporal lobe epilepsy surgery. The entity Y can be a brain anatomical structure such as the hippocampus. The attribute X can be either a functionality feature of the anatomical structure Y, calculated with SPECT modalities, such as signal average, or a volumetric/morphological feature of the entity Y such as volume or average curvature. The outcome of the surgery can be any surgery assessment such as memory quotient. A determination is made regarding surgical candidacy by analysis of both textual and image data. The current database system suggests a surgical determination for the cases with relatively small hippocampus and high signal intensity average on FLAIR images within the hippocampus. This indication pretty much fits with the surgeons" expectations/observations. Moreover, as the database gets more populated with patient profiles and individual surgical outcomes, using data mining methods one may discover partially invisible correlations between the contents of different modalities of data and the outcome of the surgery.

Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm.

PubMed

Sivakamasundari, J; Natarajan, V

2015-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.

Multimedia Database at National Museum of Ethnology

NASA Astrophysics Data System (ADS)

Sugita, Shigeharu

This paper describes the information management system at National Museum of Ethnology, Osaka, Japan. This museum is a kind of research center for cultural anthropology, and has many computer systems such as IBM 3090, VAX11/780, Fujitu M340R, etc. With these computers, distributed multimedia databases are constructed in which not only bibliographic data but also artifact image, slide image, book page image, etc. are stored. The number of data is now about 1.3 million items. These data can be retrieved and displayed on the multimedia workstation which has several displays.

Attention-based image similarity measure with application to content-based information retrieval

NASA Astrophysics Data System (ADS)

Stentiford, Fred W. M.

2003-01-01

Whilst storage and capture technologies are able to cope with huge numbers of images, image retrieval is in danger of rendering many repositories valueless because of the difficulty of access. This paper proposes a similarity measure that imposes only very weak assumptions on the nature of the features used in the recognition process. This approach does not make use of a pre-defined set of feature measurements which are extracted from a query image and used to match those from database images, but instead generates features on a trial and error basis during the calculation of the similarity measure. This has the significant advantage that features that determine similarity can match whatever image property is important in a particular region whether it be a shape, a texture, a colour or a combination of all three. It means that effort is expended searching for the best feature for the region rather than expecting that a fixed feature set will perform optimally over the whole area of an image and over every image in a database. The similarity measure is evaluated on a problem of distinguishing similar shapes in sets of black and white symbols.

Content-based retrieval of historical Ottoman documents stored as textual images.

PubMed

Saykol, Ediz; Sinop, Ali Kemal; Güdükbay, Ugur; Ulusoy, Ozgür; Cetin, A Enis

2004-03-01

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network

PubMed Central

Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin. PMID:27258404

Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

NASA Astrophysics Data System (ADS)

Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

2017-10-01

Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

Sentence-Based Metadata: An Approach and Tool for Viewing Database Designs.

ERIC Educational Resources Information Center

Boyle, John M.; Gunge, Jakob; Bryden, John; Librowski, Kaz; Hanna, Hsin-Yi

2002-01-01

Describes MARS (Museum Archive Retrieval System), a research tool which enables organizations to exchange digital images and documents by means of a common thesaurus structure, and merge the descriptive data and metadata of their collections. Highlights include theoretical basis; searching the MARS database; and examples in European museums.…

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

PubMed

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-28

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

PubMed Central

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-01

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. PMID:26828496

Content-based unconstrained color logo and trademark retrieval with color edge gradient co-occurrence histograms

NASA Astrophysics Data System (ADS)

Phan, Raymond; Androutsos, Dimitrios

2008-01-01

In this paper, we present a logo and trademark retrieval system for unconstrained color image databases that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, in comparison to the simple color pixel difference classification of edges as seen in the CECH. Our proposed method is thus reliant on edge gradient information, and as such, we call this the Color Edge Gradient Co-occurrence Histogram (CEGCH). We use this as the main mechanism for our unconstrained color logo and trademark retrieval scheme. Results illustrate that the proposed retrieval system retrieves logos and trademarks with good accuracy, and outperforms the CECH object detection scheme with higher precision and recall.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

NASA Astrophysics Data System (ADS)

Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

2018-01-01

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

A content-based retrieval of mammographic masses using the curvelet descriptor

NASA Astrophysics Data System (ADS)

Narváez, Fabian; Díaz, Gloria; Gómez, Francisco; Romero, Eduardo

2012-03-01

Computer-aided diagnosis (CAD) that uses content based image retrieval (CBIR) strategies has became an important research area. This paper presents a retrieval strategy that automatically recovers mammography masses from a virtual repository of mammographies. Unlike other approaches, we do not attempt to segment masses but instead we characterize the regions previously selected by an expert. These regions are firstly curvelet transformed and further characterized by approximating the marginal curvelet subband distribution with a generalized gaussian density (GGD). The content based retrieval strategy searches similar regions in a database using the Kullback-Leibler divergence as the similarity measure between distributions. The effectiveness of the proposed descriptor was assessed by comparing the automatically assigned label with a ground truth available in the DDSM database.1 A total of 380 masses with different shapes, sizes and margins were used for evaluation, resulting in a mean average precision rate of 89.3% and recall rate of 75.2% for the retrieval task.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

PubMed Central

Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. PMID:24632078

Image Retrieval using Integrated Features of Binary Wavelet Transform

NASA Astrophysics Data System (ADS)

Agarwal, Megha; Maheshwari, R. P.

2011-12-01

In this paper a new approach for image retrieval is proposed with the application of binary wavelet transform. This new approach facilitates the feature calculation with the integration of histogram and correlogram features extracted from binary wavelet subbands. Experiments are performed to evaluate and compare the performance of proposed method with the published literature. It is verified that average precision and average recall of proposed method (69.19%, 41.78%) is significantly improved compared to optimal quantized wavelet correlogram (OQWC) [6] (64.3%, 38.00%) and Gabor wavelet correlogram (GWC) [10] (64.1%, 40.6%). All the experiments are performed on Corel 1000 natural image database [20].

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

NASA Astrophysics Data System (ADS)

Kawata, Yoshiki; Niki, Noboru; Ohmatsu, Hironobu; Kusumoto, Masahiro; Kakinuma, Ryutaro; Mori, Kiyoshi; Yamada, Kozo; Nishiyama, Hiroyuki; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki

2004-05-01

The purpose of this study is to develop an image-guided decision support system that assists decision-making in clinical differential diagnosis of pulmonary nodules. This approach retrieves and displays nodules that exhibit morphological and internal profiles consistent to the nodule in question. It uses a three-dimensional (3-D) CT image database of pulmonary nodules for which diagnosis is known. In order to build the system, there are following issues that should be solved: 1) to categorize the nodule database with respect to morphological and internal features, 2) to quickly search nodule images similar to an indeterminate nodule from a large database, and 3) to reveal malignancy likelihood computed by using similar nodule images. Especially, the first problem influences the design of other issues. The successful categorization of nodule pattern might lead physicians to find important cues that characterize benign and malignant nodules. This paper focuses on an approach to categorize the nodule database with respect to nodule shape and CT density patterns inside nodule.

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

PubMed

Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

2014-11-01

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

The state of the art of medical imaging technology: from creation to archive and back.

PubMed

Gao, Xiaohong W; Qian, Yu; Hui, Rui

2011-01-01

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations.

The State of the Art of Medical Imaging Technology: from Creation to Archive and Back

PubMed Central

Gao, Xiaohong W; Qian, Yu; Hui, Rui

2011-01-01

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations. PMID:21915232

Imaged document information location and extraction using an optical correlator

NASA Astrophysics Data System (ADS)

Stalcup, Bruce W.; Dennis, Phillip W.; Dydyk, Robert B.

1999-12-01

Today, the paper document is fast becoming a thing of the past. With the rapid development of fast, inexpensive computing and storage devices, many government and private organizations are archiving their documents in electronic form (e.g., personnel records, medical records, patents, etc.). Many of these organizations are converting their paper archives to electronic images, which are then stored in a computer database. Because of this, there is a need to efficiently organize this data into comprehensive and accessible information resources and provide for rapid access to the information contained within these imaged documents. To meet this need, Litton PRC and Litton Data Systems Division are developing a system, the Imaged Document Optical Correlation and Conversion System (IDOCCS), to provide a total solution to the problem of managing and retrieving textual and graphic information from imaged document archives. At the heart of IDOCCS, optical correlation technology provide a means for the search and retrieval of information from imaged documents. IDOCCS can be used to rapidly search for key words or phrases within the imaged document archives and has the potential to determine the types of languages contained within a document. In addition, IDOCCS can automatically compare an input document with the archived database to determine if it is a duplicate, thereby reducing the overall resources required to maintain and access the document database. Embedded graphics on imaged pages can also be exploited, e.g., imaged documents containing an agency's seal or logo can be singled out. In this paper, we present a description of IDOCCS as well as preliminary performance results and theoretical projections.

The BioImage Database Project: organizing multidimensional biological images in an object-relational database.

PubMed

Carazo, J M; Stelzer, E H

1999-01-01

The BioImage Database Project collects and structures multidimensional data sets recorded by various microscopic techniques relevant to modern life sciences. It provides, as precisely as possible, the circumstances in which the sample was prepared and the data were recorded. It grants access to the actual data and maintains links between related data sets. In order to promote the interdisciplinary approach of modern science, it offers a large set of key words, which covers essentially all aspects of microscopy. Nonspecialists can, therefore, access and retrieve significant information recorded and submitted by specialists in other areas. A key issue of the undertaking is to exploit the available technology and to provide a well-defined yet flexible structure for dealing with data. Its pivotal element is, therefore, a modern object relational database that structures the metadata and ameliorates the provision of a complete service. The BioImage database can be accessed through the Internet. Copyright 1999 Academic Press.

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

Web-based Hyper Suprime-Cam Data Providing System

NASA Astrophysics Data System (ADS)

Koike, M.; Furusawa, H.; Takata, T.; Price, P.; Okura, Y.; Yamada, Y.; Yamanoi, H.; Yasuda, N.; Bickerton, S.; Katayama, N.; Mineo, S.; Lupton, R.; Bosch, J.; Loomis, C.

2014-05-01

We describe a web-based user interface to retrieve Hyper Suprime-Cam data products, including images and. Users can access data directly from a graphical user interface or by writing a database SQL query. The system provides raw images, reduced images and stacked images (from multiple individual exposures), with previews available. Catalog queries can be executed in preview or queue mode, allowing for both exploratory and comprehensive investigations.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

PubMed

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

BIRAM: a content-based image retrieval framework for medical images

NASA Astrophysics Data System (ADS)

Moreno, Ramon A.; Furuie, Sergio S.

2006-03-01

In the medical field, digital images are becoming more and more important for diagnostics and therapy of the patients. At the same time, the development of new technologies has increased the amount of image data produced in a hospital. This creates a demand for access methods that offer more than text-based queries for retrieval of the information. In this paper is proposed a framework for the retrieval of medical images that allows the use of different algorithms for the search of medical images by similarity. The framework also enables the search for textual information from an associated medical report and DICOM header information. The proposed system can be used for support of clinical decision making and is intended to be integrated with an open source picture, archiving and communication systems (PACS). The BIRAM has the following advantages: (i) Can receive several types of algorithms for image similarity search; (ii) Allows the codification of the report according to a medical dictionary, improving the indexing of the information and retrieval; (iii) The algorithms can be selectively applied to images with the appropriated characteristics, for instance, only in magnetic resonance images. The framework was implemented in Java language using a MS Access 97 database. The proposed framework can still be improved, by the use of regions of interest (ROI), indexing with slim-trees and integration with a PACS Server.

SKL algorithm based fabric image matching and retrieval

NASA Astrophysics Data System (ADS)

Cao, Yichen; Zhang, Xueqin; Ma, Guojian; Sun, Rongqing; Dong, Deping

2017-07-01

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

Digitizing Olin Eggen's Card Database

NASA Astrophysics Data System (ADS)

Crast, J.; Silvis, G.

2017-06-01

The goal of the Eggen Card Database Project is to recover as many of the photometric observations from Olin Eggen's Card Database as possible and preserve these observations, in digital forms that are accessible by anyone. Any observations of interest to the AAVSO will be added to the AAVSO International Database (AID). Given to the AAVSO on long-term loan by the Cerro Tololo Inter-American Observatory, the database is a collection of over 78,000 index cards holding all Eggen's observations made between 1960 and 1990. The cards were electronically scanned and the resulting 108,000 card images have been published as a series of 2,216 PDF files, which are available from the AAVSO web site. The same images are also stored in an AAVSO online database where they are indexed by star name and card content. These images can be viewed using the eggen card portal online tool. Eggen made observations using filter bands from five different photometric systems. He documented these observations using 15 different data recording formats. Each format represents a combination of filter magnitudes and color indexes. These observations are being transcribed onto spreadsheets, from which observations of value to the AAVSO are added to the AID. A total of 506 U, B, V, R, and I observations were added to the AID for the variable stars S Car and l Car. We would like the reader to search through the card database using the eggen card portal for stars of particular interest. If such stars are found and retrieval of the observations is desired, e-mail the authors, and we will be happy to help retrieve those data for the reader.

A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis

PubMed Central

Rahman, M. M.; Antani, S. K.; Thoma, G. R.

2011-01-01

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. PMID:21822350

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

PubMed Central

Green, Jason M.; Harnsomburana, Jaturon; Schaeffer, Mary L.; Lawrence, Carolyn J.; Shyu, Chi-Ren

2011-01-01

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. Database URL: http:www.PhenomicsWorld.org/QBTA.php PMID:21558151

The design and implementation of image query system based on color feature

NASA Astrophysics Data System (ADS)

Yao, Xu-Dong; Jia, Da-Chun; Li, Lin

2013-07-01

ASP.NET technology was used to construct the B/S mode image query system. The theory and technology of database design, color feature extraction from image, index and retrieval in the construction of the image repository were researched. The campus LAN and WAN environment were used to test the system. From the test results, the needs of user queries about related resources were achieved by system architecture design.

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

PubMed

Rahman, Md Mahmudur; Bhattacharya, Prabir; Desai, Bipin C

2007-01-01

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

Toward a standard reference database for computer-aided mammography

NASA Astrophysics Data System (ADS)

Oliveira, Júlia E. E.; Gueld, Mark O.; de A. Araújo, Arnaldo; Ott, Bastian; Deserno, Thomas M.

2008-03-01

Because of the lack of mammography databases with a large amount of codified images and identified characteristics like pathology, type of breast tissue, and abnormality, there is a problem for the development of robust systems for computer-aided diagnosis. Integrated to the Image Retrieval in Medical Applications (IRMA) project, we present an available mammography database developed from the union of: The Mammographic Image Analysis Society Digital Mammogram Database (MIAS), The Digital Database for Screening Mammography (DDSM), the Lawrence Livermore National Laboratory (LLNL), and routine images from the Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen. Using the IRMA code, standardized coding of tissue type, tumor staging, and lesion description was developed according to the American College of Radiology (ACR) tissue codes and the ACR breast imaging reporting and data system (BI-RADS). The import was done automatically using scripts for image download, file format conversion, file name, web page and information file browsing. Disregarding the resolution, this resulted in a total of 10,509 reference images, and 6,767 images are associated with an IRMA contour information feature file. In accordance to the respective license agreements, the database will be made freely available for research purposes, and may be used for image based evaluation campaigns such as the Cross Language Evaluation Forum (CLEF). We have also shown that it can be extended easily with further cases imported from a picture archiving and communication system (PACS).

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Building structural similarity database for metric learning

NASA Astrophysics Data System (ADS)

Jin, Guoxin; Pappas, Thrasyvoulos N.

2015-03-01

We propose a new approach for constructing databases for training and testing similarity metrics for structurally lossless image compression. Our focus is on structural texture similarity (STSIM) metrics and the matched-texture compression (MTC) approach. We first discuss the metric requirements for structurally lossless compression, which differ from those of other applications such as image retrieval, classification, and understanding. We identify "interchangeability" as the key requirement for metric performance, and partition the domain of "identical" textures into three regions, of "highest," "high," and "good" similarity. We design two subjective tests for data collection, the first relies on ViSiProG to build a database of "identical" clusters, and the second builds a database of image pairs with the "highest," "high," "good," and "bad" similarity labels. The data for the subjective tests is generated during the MTC encoding process, and consist of pairs of candidate and target image blocks. The context of the surrounding image is critical for training the metrics to detect lighting discontinuities, spatial misalignments, and other border artifacts that have a noticeable effect on perceptual quality. The identical texture clusters are then used for training and testing two STSIM metrics. The labelled image pair database will be used in future research.

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrievalmore » precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.« less

Case-based fracture image retrieval.

PubMed

Zhou, Xin; Stern, Richard; Müller, Henning

2012-05-01

Case-based fracture image retrieval can assist surgeons in decisions regarding new cases by supplying visually similar past cases. This tool may guide fracture fixation and management through comparison of long-term outcomes in similar cases. A fracture image database collected over 10 years at the orthopedic service of the University Hospitals of Geneva was used. This database contains 2,690 fracture cases associated with 43 classes (based on the AO/OTA classification). A case-based retrieval engine was developed and evaluated using retrieval precision as a performance metric. Only cases in the same class as the query case are considered as relevant. The scale-invariant feature transform (SIFT) is used for image analysis. Performance evaluation was computed in terms of mean average precision (MAP) and early precision (P10, P30). Retrieval results produced with the GNU image finding tool (GIFT) were used as a baseline. Two sampling strategies were evaluated. One used a dense 40 × 40 pixel grid sampling, and the second one used the standard SIFT features. Based on dense pixel grid sampling, three unsupervised feature selection strategies were introduced to further improve retrieval performance. With dense pixel grid sampling, the image is divided into 1,600 (40 × 40) square blocks. The goal is to emphasize the salient regions (blocks) and ignore irrelevant regions. Regions are considered as important when a high variance of the visual features is found. The first strategy is to calculate the variance of all descriptors on the global database. The second strategy is to calculate the variance of all descriptors for each case. A third strategy is to perform a thumbnail image clustering in a first step and then to calculate the variance for each cluster. Finally, a fusion between a SIFT-based system and GIFT is performed. A first comparison on the selection of sampling strategies using SIFT features shows that dense sampling using a pixel grid (MAP = 0.18) outperformed the SIFT detector-based sampling approach (MAP = 0.10). In a second step, three unsupervised feature selection strategies were evaluated. A grid parameter search is applied to optimize parameters for feature selection and clustering. Results show that using half of the regions (700 or 800) obtains the best performance for all three strategies. Increasing the number of clusters in clustering can also improve the retrieval performance. The SIFT descriptor variance in each case gave the best indication of saliency for the regions (MAP = 0.23), better than the other two strategies (MAP = 0.20 and 0.21). Combining GIFT (MAP = 0.23) and the best SIFT strategy (MAP = 0.23) produced significantly better results (MAP = 0.27) than each system alone. A case-based fracture retrieval engine was developed and is available for online demonstration. SIFT is used to extract local features, and three feature selection strategies were introduced and evaluated. A baseline using the GIFT system was used to evaluate the salient point-based approaches. Without supervised learning, SIFT-based systems with optimized parameters slightly outperformed the GIFT system. A fusion of the two approaches shows that the information contained in the two approaches is complementary. Supervised learning on the feature space is foreseen as the next step of this study.

QBIC project: querying images by content, using color, texture, and shape

NASA Astrophysics Data System (ADS)

Niblack, Carlton W.; Barber, Ron; Equitz, Will; Flickner, Myron D.; Glasman, Eduardo H.; Petkovic, Dragutin; Yanker, Peter; Faloutsos, Christos; Taubin, Gabriel

1993-04-01

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.

Visualization and manipulating the image of a formal data structure (FDS)-based database

NASA Astrophysics Data System (ADS)

Verdiesen, Franc; de Hoop, Sylvia; Molenaar, Martien

1994-08-01

A vector map is a terrain representation with a vector-structured geometry. Molenaar formulated an object-oriented formal data structure for 3D single valued vector maps. This FDS is implemented in a database (Oracle). In this study we describe a methodology for visualizing a FDS-based database and manipulating the image. A data set retrieved by querying the database is converted into an import file for a drawing application. An objective of this study is that an end-user can alter and add terrain objects in the image. The drawing application creates an export file, that is compared with the import file. Differences between these files result in updating the database which involves checks on consistency. In this study Autocad is used for visualizing and manipulating the image of the data set. A computer program has been written for the data exchange and conversion between Oracle and Autocad. The data structure of the FDS is compared to the data structure of Autocad and the data of the FDS is converted into the structure of Autocad equal to the FDS.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

PubMed

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan

2017-02-01

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

Conjunctive patches subspace learning with side information for collaborative image retrieval.

PubMed

Zhang, Lining; Wang, Lipo; Lin, Weisi

2012-08-01

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

Towards building high performance medical image management system for clinical trials

NASA Astrophysics Data System (ADS)

Wang, Fusheng; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel

2011-03-01

Medical image based biomarkers are being established for therapeutic cancer clinical trials, where image assessment is among the essential tasks. Large scale image assessment is often performed by a large group of experts by retrieving images from a centralized image repository to workstations to markup and annotate images. In such environment, it is critical to provide a high performance image management system that supports efficient concurrent image retrievals in a distributed environment. There are several major challenges: high throughput of large scale image data over the Internet from the server for multiple concurrent client users, efficient communication protocols for transporting data, and effective management of versioning of data for audit trails. We study the major bottlenecks for such a system, propose and evaluate a solution by using a hybrid image storage with solid state drives and hard disk drives, RESTfulWeb Services based protocols for exchanging image data, and a database based versioning scheme for efficient archive of image revision history. Our experiments show promising results of our methods, and our work provides a guideline for building enterprise level high performance medical image management systems.

Storage and retrieval of medical images from data warehouses

NASA Astrophysics Data System (ADS)

Tikekar, Rahul V.; Fotouhi, Farshad A.; Ragan, Don P.

1995-11-01

As our applications continue to become more sophisticated, the demand for more storage continues to rise. Hence many businesses are looking toward data warehousing technology to satisfy their storage needs. A warehouse is different from a conventional database and hence deserves a different approach while storing data that might be retrieved at a later point in time. In this paper we look at the problem of storing and retrieving medical image data from a warehouse. We regard the warehouse as a pyramid with fast storage devices at the top and slower storage devices at the bottom. Our approach is to store the most needed information abstract at the top of the pyramid and more detailed and storage consuming data toward the end of the pyramid. This information is linked for browsing purposes. In a similar fashion, during the retrieval of data, the user is given a sample representation with browse option of the detailed data and, as required, more and more details are made available.

Targeted Silver Nanoparticles for Dual-Energy Breast X-Ray Imaging

DTIC Science & Technology

2013-03-01

imaging parameters. In addition, Ag performs better than I when imaging at the optimal conditions for I. For example, using a rhodium filter, the...Laboratory. XCOM: Photon Cross Sections Database. Retrieved December 10, 2011 2. Boone J.M. , Fewell, T.R., Jennings, R.J. Molybdenum, rhodium , and tungsten...and a 27 kVp low-energy beam with rhodium filtration, at a dose distribution of 50:50. This low-energy technique is a classic example of an

Object-oriented analysis and design of an ECG storage and retrieval system integrated with an HIS.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-03-01

For a hospital information system, object-oriented methodology plays an increasingly important role, especially for the management of digitized data, e.g., the electrocardiogram, electroencephalogram, electromyogram, spirogram, X-ray, CT and histopathological images, which are not yet computerized in most hospitals. As a first step in an object-oriented approach to hospital information management and storing medical data in an object-oriented database, we connected electrocardiographs to a hospital network and established the integration of ECG storage and retrieval systems with a hospital information system. In this paper, the object-oriented analysis and design of the ECG storage and retrieval systems is reported.

BDVC (Bimodal Database of Violent Content): A database of violent audio and video

NASA Astrophysics Data System (ADS)

Rivera Martínez, Jose Luis; Mijes Cruz, Mario Humberto; Rodríguez Vázqu, Manuel Antonio; Rodríguez Espejo, Luis; Montoya Obeso, Abraham; García Vázquez, Mireya Saraí; Ramírez Acosta, Alejandro Álvaro

2017-09-01

Nowadays there is a trend towards the use of unimodal databases for multimedia content description, organization and retrieval applications of a single type of content like text, voice and images, instead bimodal databases allow to associate semantically two different types of content like audio-video, image-text, among others. The generation of a bimodal database of audio-video implies the creation of a connection between the multimedia content through the semantic relation that associates the actions of both types of information. This paper describes in detail the used characteristics and methodology for the creation of the bimodal database of violent content; the semantic relationship is stablished by the proposed concepts that describe the audiovisual information. The use of bimodal databases in applications related to the audiovisual content processing allows an increase in the semantic performance only and only if these applications process both type of content. This bimodal database counts with 580 audiovisual annotated segments, with a duration of 28 minutes, divided in 41 classes. Bimodal databases are a tool in the generation of applications for the semantic web.

The new Cloud Dynamics and Radiation Database algorithms for AMSR2 and GMI: exploitation of the GPM observational database for operational applications

NASA Astrophysics Data System (ADS)

Cinzia Marra, Anna; Casella, Daniele; Martins Costa do Amaral, Lia; Sanò, Paolo; Dietrich, Stefano; Panegrossi, Giulia

2017-04-01

Two new precipitation retrieval algorithms for the Advanced Microwave Scanning Radiometer 2 (AMSR2) and for the GPM Microwave Imager (GMI) are presented. The algorithms are based on the Cloud Dynamics and Radiation Database (CDRD) Bayesian approach and represent an evolution of the previous version applied to Special Sensor Microwave Imager/Sounder (SSMIS) observations, and used operationally within the EUMETSAT Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF). These new products present as main innovation the use of an extended database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) (Dual-frequency Precipitation Radar-DPR and GMI). The other new aspects are: 1) a new rain-no-rain screening approach; 2) the use of Empirical Orthogonal Functions (EOF) and Canonical Correlation Analysis (CCA) both in the screening approach, and in the Bayesian algorithm; 2) the use of new meteorological and environmental ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; 3) the development and implementations of specific modules for computational time minimization. The CDRD algorithms for AMSR2 and GMI are able to handle an extremely large observational database available from GPM-CO and provide the rainfall estimate with minimum latency, making them suitable for near-real time hydrological and operational applications. As far as CDRD for AMSR2, a verification study over Italy using ground-based radar data and over the MSG full disk area using coincident GPM-CO/AMSR2 observations has been carried out. Results show remarkable AMSR2 capabilities for rainfall rate (RR) retrieval over ocean (for RR > 0.25 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. Similarly, preliminary results of the application of CDRD for GMI are also shown and discussed, highlighting the advantage of the availability of high frequency channels (> 90 GHz) for precipitation retrieval over land and coastal areas.

Common hyperspectral image database design

NASA Astrophysics Data System (ADS)

Tian, Lixun; Liao, Ningfang; Chai, Ali

2009-11-01

This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method (CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet, visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor, Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search, query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in C#; Finally an application case is presented in agricultural disease detecting area.

Establishment of a database of fetal congenital heart malformations and preliminary investigation of its clinical application.

PubMed

Gao, Jun-Xue; Pei, Qiu-Yan; Li, Yun-Tao; Yang, Zhen-Juan

2015-06-01

The aim of this study was to create a database of anatomical ultrathin cross-sectional images of fetal hearts with different congenital heart diseases (CHDs) and preliminarily to investigate its clinical application. Forty Chinese fetal heart samples from induced labor due to different CHDs were cut transversely at 60-μm thickness. All thoracic organs were removed from the thoracic cavity after formalin fixation, embedded in optimum cutting temperature compound, and then frozen at -25°C for 2 hours. Subsequently, macro shots of the frozen serial sections were obtained using a digital camera in order to build a database of anatomical ultrathin cross-sectional images. Images in the database clearly displayed the fetal heart structures. After importing the images into three-dimensional software, the following functions could be realized: (1) based on the original database of transverse sections, databases of sagittal and coronal sections could be constructed; and (2) the original and constructed databases could be displayed continuously and dynamically, and rotated in arbitrary angles. They could also be displayed synchronically. The aforementioned functions of the database allowed for the retrieval of images and three-dimensional anatomy characteristics of the different fetal CHDs, and virtualization of fetal echocardiography findings. A database of 40 different cross-sectional fetal CHDs was established. An extensive database library of fetal CHDs, from which sonographers and students can study the anatomical features of fetal CHDs and virtualize fetal echocardiography findings via either centralized training or distance education, can be established in the future by accumulating further cases. Copyright © 2015. Published by Elsevier B.V.

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

USDA-ARS?s Scientific Manuscript database

In the midst of this genomics era, major plant genome databases are collecting massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc., as well as textual descriptions of many of these entities. While basic browsing and sear...

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Development of a database for the verification of trans-ionospheric remote sensing systems

NASA Astrophysics Data System (ADS)

Leitinger, R.

2005-08-01

Remote sensing systems need verification by means of in-situ data or by means of model data. In the case of ionospheric occultation inversion, ionosphere tomography and other imaging methods on the basis of satellite-to-ground or satellite-to-satellite electron content, the availability of in-situ data with adequate spatial and temporal co-location is a very rare case, indeed. Therefore the method of choice for verification is to produce artificial electron content data with realistic properties, subject these data to the inversion/retrieval method, compare the results with model data and apply a suitable type of “goodness of fit” classification. Inter-comparison of inversion/retrieval methods should be done with sets of artificial electron contents in a “blind” (or even “double blind”) way. The set up of a relevant database for the COST 271 Action is described. One part of the database will be made available to everyone interested in testing of inversion/retrieval methods. The artificial electron content data are calculated by means of large-scale models that are “modulated” in a realistic way to include smaller scale and dynamic structures, like troughs and traveling ionospheric disturbances.

Quantifying the sensitivity of aerosol optical depths retrieved from MSG SEVIRI to a priori data

NASA Astrophysics Data System (ADS)

Bulgin, C. E.; Palmer, P. I.; Merchant, C. J.; Siddans, R.; Poulsen, C.; Grainger, R. G.; Thomas, G.; Carboni, E.; McConnell, C.; Highwood, E.

2009-12-01

Radiative forcing contributions from aerosol direct and indirect effects remain one of the most uncertain components of the climate system. Satellite observations of aerosol optical properties offer important constraints on atmospheric aerosols but their sensitivity to prior assumptions must be better characterized before they are used effectively to reduce uncertainty in aerosol radiative forcing. We assess the sensitivity of the Oxford-RAL Aerosol and Cloud (ORAC) optimal estimation retrieval of aerosol optical depth (AOD) from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) to a priori aerosol data. SEVIRI is a geostationary satellite instrument centred over Africa and the neighbouring Atlantic Ocean, routinely sampling desert dust and biomass burning outflow from Africa. We quantify the uncertainty in SEVIRI AOD retrievals in the presence of desert dust by comparing retrievals that use prior information from the Optical Properties of Aerosol and Cloud (OPAC) database, with those that use measured aerosol properties during the Dust Outflow and Deposition to the Ocean (DODO) aircraft campaign (August, 2006). We also assess the sensitivity of retrieved AODs to changes in solar zenith angle, and the vertical profile of aerosol effective radius and extinction coefficient input into the retrieval forward model. Currently the ORAC retrieval scheme retrieves AODs for five aerosol types (desert dust, biomass burning, maritime, urban and continental) and chooses the most appropriate AOD based on the cost functions. We generate an improved prior aerosol speciation database for SEVIRI based on a statistical analysis of a Saharan Dust Index (SDI) determined using variances of different brightness temperatures, and organic and black carbon tracers from the GEOS-Chem chemistry transport model. This database is described as a function of season and time of day. We quantify the difference in AODs between those chosen based on prior information from the SDI and GEOS-Chem and those chosen based on the smallest cost function.

Content-based video indexing and searching with wavelet transformation

NASA Astrophysics Data System (ADS)

Stumpf, Florian; Al-Jawad, Naseer; Du, Hongbo; Jassim, Sabah

2006-05-01

Biometric databases form an essential tool in the fight against international terrorism, organised crime and fraud. Various government and law enforcement agencies have their own biometric databases consisting of combination of fingerprints, Iris codes, face images/videos and speech records for an increasing number of persons. In many cases personal data linked to biometric records are incomplete and/or inaccurate. Besides, biometric data in different databases for the same individual may be recorded with different personal details. Following the recent terrorist atrocities, law enforcing agencies collaborate more than before and have greater reliance on database sharing. In such an environment, reliable biometric-based identification must not only determine who you are but also who else you are. In this paper we propose a compact content-based video signature and indexing scheme that can facilitate retrieval of multiple records in face biometric databases that belong to the same person even if their associated personal data are inconsistent. We shall assess the performance of our system using a benchmark audio visual face biometric database that has multiple videos for each subject but with different identity claims. We shall demonstrate that retrieval of relatively small number of videos that are nearest, in terms of the proposed index, to any video in the database results in significant proportion of that individual biometric data.

Inter-comparison of the EUMETSAT H-SAF and NASA PPS precipitation products over Western Europe.

NASA Astrophysics Data System (ADS)

Kidd, Chris; Panegrossi, Giulia; Ringerud, Sarah; Stocker, Erich

2017-04-01

The development of precipitation retrieval techniques utilising passive microwave satellite observations has achieved a good degree of maturity through the use of physically-based schemes. The DMSP Special Sensor Microwave Imager/Sounder (SSMIS) has been the mainstay of passive microwave observations over the last 13 years forming the basis of many satellite precipitation products, including NASA's Precipitation Processing System (PPS) and EUMETSAT's Hydrological Satellite Application Facility (H-SAF). The NASA PPS product utilises the Goddard Profiling (GPROF; currently 2014v2-0) retrieval scheme that provides a physically consistent retrieval scheme through the use of coincident active/passive microwave retrievals from the Global Precipitation Measurement (GPM) mission core satellite. The GPM combined algorithm retrieves hydrometeor profiles optimized for consistency with both Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI); these profiles form the basis of the GPROF database which can be utilized for any constellation radiometer within the framework a Bayesian retrieval scheme. The H-SAF product (PR-OBS-1 v1.7) is based on a physically-based Bayesian technique where the a priori information is provided by a Cloud Dynamic Radiation Database (CDRD). Meteorological parameter constraints, derived from synthetic dynamical-thermodynamical-hydrological meteorological profile variables, are used in conjunction with multi-hydrometeor microphysical profiles and multispectral PMW brightness temperature vectors into a specialized a priori knowledge database underpinning and guiding the algorithm's Bayesian retrieval solver. This paper will present the results of an inter-comparison of the NASA PPS GPROF and EUMETSAT H-SAF PR-OBS-1 products over Western Europe for the period from 1 January 2015 through 31 December 2016. Surface radar is derived from the UKMO-derived Nimrod European radar product, available at 15 minute/5 km resolution. Initial results show that overall the correlations between the two satellite precipitation products and surface radar precipitation estimates are similar, particularly for cases where there is extensive precipitation; however, the H-SAF tends to have poorer correlations in situations where rain is light or limited in extent. Similarly, RMSEs for the GPROF scheme tend to a smaller than those of the H-SAF retrievals. The difference in the performance can be traced to the identification of precipitation; the GPROF2014v2-0 scheme overestimates the occurrence and extent of the precipitation, generating a significant amount of light precipitation. The H-SAF scheme has a lower precipitation threshold of about 0.25 mmh-1 while overestimating moderate and higher precipitation intensities.

Coherent Image Layout using an Adaptive Visual Vocabulary

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dillard, Scott E.; Henry, Michael J.; Bohn, Shawn J.

When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we aremore » able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.« less

Information Retrieval in Telemedicine: a Comparative Study on Bibliographic Databases

PubMed Central

Ahmadi, Maryam; Sarabi, Roghayeh Ershad; Orak, Roohangiz Jamshidi; Bahaadinbeigy, Kambiz

2015-01-01

Background and Aims: The first step in each systematic review is selection of the most valid database that can provide the highest number of relevant references. This study was carried out to determine the most suitable database for information retrieval in telemedicine field. Methods: Cinhal, PubMed, Web of Science and Scopus databases were searched for telemedicine matched with Education, cost benefit and patient satisfaction. After analysis of the obtained results, the accuracy coefficient, sensitivity, uniqueness and overlap of databases were calculated. Results: The studied databases differed in the number of retrieved articles. PubMed was identified as the most suitable database for retrieving information on the selected topics with the accuracy and sensitivity ratios of 50.7% and 61.4% respectively. The uniqueness percent of retrieved articles ranged from 38% for Pubmed to 3.0% for Cinhal. The highest overlap rate (18.6%) was found between PubMed and Web of Science. Less than 1% of articles have been indexed in all searched databases. Conclusion: PubMed is suggested as the most suitable database for starting search in telemedicine and after PubMed, Scopus and Web of Science can retrieve about 90% of the relevant articles. PMID:26236086

Information Retrieval in Telemedicine: a Comparative Study on Bibliographic Databases.

PubMed

Ahmadi, Maryam; Sarabi, Roghayeh Ershad; Orak, Roohangiz Jamshidi; Bahaadinbeigy, Kambiz

2015-06-01

The first step in each systematic review is selection of the most valid database that can provide the highest number of relevant references. This study was carried out to determine the most suitable database for information retrieval in telemedicine field. Cinhal, PubMed, Web of Science and Scopus databases were searched for telemedicine matched with Education, cost benefit and patient satisfaction. After analysis of the obtained results, the accuracy coefficient, sensitivity, uniqueness and overlap of databases were calculated. The studied databases differed in the number of retrieved articles. PubMed was identified as the most suitable database for retrieving information on the selected topics with the accuracy and sensitivity ratios of 50.7% and 61.4% respectively. The uniqueness percent of retrieved articles ranged from 38% for Pubmed to 3.0% for Cinhal. The highest overlap rate (18.6%) was found between PubMed and Web of Science. Less than 1% of articles have been indexed in all searched databases. PubMed is suggested as the most suitable database for starting search in telemedicine and after PubMed, Scopus and Web of Science can retrieve about 90% of the relevant articles.

Enhancing navigation in biomedical databases by community voting and database-driven text classification

PubMed Central

Duchrow, Timo; Shtatland, Timur; Guettler, Daniel; Pivovarov, Misha; Kramer, Stefan; Weissleder, Ralph

2009-01-01

Background The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Results Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Conclusion Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at . PMID:19799796

Multimedia systems for art and culture: a case study of Brihadisvara Temple

NASA Astrophysics Data System (ADS)

Jain, Anil K.; Goel, Sanjay; Agarwal, Sachin; Mittal, Vipin; Sharma, Hariom; Mahindru, Ranjeev

1997-01-01

In India a temple is not only a structure of religious significance and celebration, but it also plays an important role in the social, administrative and cultural life of the locality. Temples have served as centers for learning Indian scriptures. Music and dance were fostered and performed in the precincts of the temples. Built at the end of the 10th century, the Brihadisvara temple signified new design methodologies. We have access to a large number of images, audio and video recordings, architectural drawings and scholarly publications of this temple. A multimedia system for this temple is being designed which is intended to be used for the following purposes: (1) to inform and enrich the general public, and (2) to assist the scholars in their research. Such a system will also preserve and archive old historical documents and images. The large database consists primarily of images which can be retrieved using keywords, but the emphasis here is largely on techniques which will allow access using image content. Besides classifying images as either long shots or close-ups, deformable template matching is used for shape-based query by image content, and digital video retrieval. Further, to exploit the non-linear accessibility of video sequences, key frames are determined to aid the domain experts in getting a quick preview of the video. Our database also has images of several old, and rare manuscripts many of which are noisy and difficult to read. We have enhanced them to make them more legible. We are also investigating the optimal trade-off between image quality and compression ratios.

Optimality of the basic colour categories for classification

PubMed Central

Griffin, Lewis D

2005-01-01

Categorization of colour has been widely studied as a window into human language and cognition, and quite separately has been used pragmatically in image-database retrieval systems. This suggests the hypothesis that the best category system for pragmatic purposes coincides with human categories (i.e. the basic colours). We have tested this hypothesis by assessing the performance of different category systems in a machine-vision task. The task was the identification of the odd-one-out from triples of images obtained using a web-based image-search service. In each triple, two of the images had been retrieved using the same search term, the other a different term. The terms were simple concrete nouns. The results were as follows: (i) the odd-one-out task can be performed better than chance using colour alone; (ii) basic colour categorization performs better than random systems of categories; (iii) a category system that performs better than the basic colours could not be found; and (iv) it is not just the general layout of the basic colours that is important, but also the detail. We conclude that (i) the results support the plausibility of an explanation for the basic colours as a result of a pressure-to-optimality and (ii) the basic colours are good categories for machine vision image-retrieval systems. PMID:16849219

Web tools for effective retrieval, visualization, and evaluation of cardiology medical images and records

NASA Astrophysics Data System (ADS)

Masseroli, Marco; Pinciroli, Francesco

2000-12-01

To provide easy retrieval, integration and evaluation of multimodal cardiology images and data in a web browser environment, distributed application technologies and java programming were used to implement a client-server architecture based on software agents. The server side manages secure connections and queries to heterogeneous remote databases and file systems containing patient personal and clinical data. The client side is a Java applet running in a web browser and providing a friendly medical user interface to perform queries on patient and medical test dat and integrate and visualize properly the various query results. A set of tools based on Java Advanced Imaging API enables to process and analyze the retrieved cardiology images, and quantify their features in different regions of interest. The platform-independence Java technology makes the developed prototype easy to be managed in a centralized form and provided in each site where an intranet or internet connection can be located. Giving the healthcare providers effective tools for querying, visualizing and evaluating comprehensively cardiology medical images and records in all locations where they can need them- i.e. emergency, operating theaters, ward, or even outpatient clinics- the developed prototype represents an important aid in providing more efficient diagnoses and medical treatments.

MRNIDX - Marine Data Index: Database Description, Operation, Retrieval, and Display

USGS Publications Warehouse

Paskevich, Valerie F.

1982-01-01

A database referencing the location and content of data stored on magnetic medium was designed to assist in the indexing of time-series and spatially dependent marine geophysical data collected or processed by the U. S. Geological Survey. The database was designed and created for input to the Geologic Retrieval and Synopsis Program (GRASP) to allow selective retrievals of information pertaining to location of data, data format, cruise, geographical bounds and collection dates of data. This information is then used to locate the stored data for administrative purposes or further processing. Database utilization is divided into three distinct operations. The first is the inventorying of the data and the updating of the database, the second is the retrieval of information from the database, and the third is the graphic display of the geographical boundaries to which the retrieved information pertains.

[Development and evaluation of the medical imaging distribution system with dynamic web application and clustering technology].

PubMed

Yokohama, Noriya; Tsuchimoto, Tadashi; Oishi, Masamichi; Itou, Katsuya

2007-01-20

It has been noted that the downtime of medical informatics systems is often long. Many systems encounter downtimes of hours or even days, which can have a critical effect on daily operations. Such systems remain especially weak in the areas of database and medical imaging data. The scheme design shows the three-layer architecture of the system: application, database, and storage layers. The application layer uses the DICOM protocol (Digital Imaging and Communication in Medicine) and HTTP (Hyper Text Transport Protocol) with AJAX (Asynchronous JavaScript+XML). The database is designed to decentralize in parallel using cluster technology. Consequently, restoration of the database can be done not only with ease but also with improved retrieval speed. In the storage layer, a network RAID (Redundant Array of Independent Disks) system, it is possible to construct exabyte-scale parallel file systems that exploit storage spread. Development and evaluation of the test-bed has been successful in medical information data backup and recovery in a network environment. This paper presents a schematic design of the new medical informatics system that can be accommodated from a recovery and the dynamic Web application for medical imaging distribution using AJAX.

Supervised learning of tools for content-based search of image databases

NASA Astrophysics Data System (ADS)

Delanoy, Richard L.

1996-03-01

A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

Atomic Oxygen Density Retrievals using FUV Observations by the Imaging Ultraviolet Spectrograph on MAVEN

NASA Astrophysics Data System (ADS)

Evans, J. Scott; Stevens, Michael H.; Schneider, Nicholas M.; Stewart, Ian; Deighan, Justin; Jain, Sonal Kumar; Eparvier, Francis; Thiemann, E. M.; Bougher, Stephen W.; Jakosky, Bruce

2016-10-01

We present the first direct retrievals of neutral atomic oxygen in Mars's upper atmosphere using daytime FUV periapse limb scan observations from 130 - 200 km tangent altitude. Atmospheric composition is inferred using the Atmospheric Ultraviolet Radiance Integrated Code [Strickland et al., 1999] adapted to the Martian atmosphere [Evans et al., 2015]. For our retrievals we use O I 135.6 nm emission observed by IUVS on MAVEN under daytime conditions (solar zenith angle < 60 degrees) over both northern and southern hemispheres (latitudes between -65 and +35 degrees) from October 2014 to August 2016. We investigate the sensitivity of atomic oxygen density retrievals to variability in solar irradiance, solar longitude, and local time. We compare our retrievals to predictions from the Mars Global Ionosphere-Thermosphere Model [MGITM, Bougher et al., 2015] and the Mars Climate Database [MCD, Forget et al., 1999] and quantify the differences throughout the altitude region of interest. The retrieved densities are used to characterize global transport of atomic oxygen in the Martian thermosphere.

BIRD: Bio-Image Referral Database. Design and implementation of a new web based and patient multimedia data focused system for effective medical diagnosis and therapy.

PubMed

Pinciroli, Francesco; Masseroli, Marco; Acerbo, Livio A; Bonacina, Stefano; Ferrari, Roberto; Marchente, Mario

2004-01-01

This paper presents a low cost software platform prototype supporting health care personnel in retrieving patient referral multimedia data. These information are centralized in a server machine and structured by using a flexible eXtensible Markup Language (XML) Bio-Image Referral Database (BIRD). Data are distributed on demand to requesting client in an Intranet network and transformed via eXtensible Stylesheet Language (XSL) to be visualized in an uniform way on market browsers. The core server operation software has been developed in PHP Hypertext Preprocessor scripting language, which is very versatile and useful for crafting a dynamic Web environment.

The EXOSAT database and archive

NASA Technical Reports Server (NTRS)

Reynolds, A. P.; Parmar, A. N.

1992-01-01

The EXOSAT database provides on-line access to the results and data products (spectra, images, and lightcurves) from the EXOSAT mission as well as access to data and logs from a number of other missions (such as EINSTEIN, COS-B, ROSAT, and IRAS). In addition, a number of familiar optical, infrared, and x ray catalogs, including the Hubble Space Telescope (HST) guide star catalog are available. The complete database is located at the EXOSAT observatory at ESTEC in the Netherlands and is accessible remotely via a captive account. The database management system was specifically developed to efficiently access the database and to allow the user to perform statistical studies on large samples of astronomical objects as well as to retrieve scientific and bibliographic information on single sources. The system was designed to be mission independent and includes timing, image processing, and spectral analysis packages as well as software to allow the easy transfer of analysis results and products to the user's own institute. The archive at ESTEC comprises a subset of the EXOSAT observations, stored on magnetic tape. Observations of particular interest were copied in compressed format to an optical jukebox, allowing users to retrieve and analyze selected raw data entirely from their terminals. Such analysis may be necessary if the user's needs are not accommodated by the products contained in the database (in terms of time resolution, spectral range, and the finesse of the background subtraction, for instance). Long-term archiving of the full final observation data is taking place at ESRIN in Italy as part of the ESIS program, again using optical media, and ESRIN have now assumed responsibility for distributing the data to the community. Tests showed that raw observational data (typically several tens of megabytes for a single target) can be transferred via the existing networks in reasonable time.

Preparing a collection of radiology examinations for distribution and retrieval.

PubMed

Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B; Shooshan, Sonya E; Rodriguez, Laritza; Antani, Sameer; Thoma, George R; McDonald, Clement J

2016-03-01

Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/). Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

A Parallel Relational Database Management System Approach to Relevance Feedback in Information Retrieval.

ERIC Educational Resources Information Center

Lundquist, Carol; Frieder, Ophir; Holmes, David O.; Grossman, David

1999-01-01

Describes a scalable, parallel, relational database-drive information retrieval engine. To support portability across a wide range of execution environments, all algorithms adhere to the SQL-92 standard. By incorporating relevance feedback algorithms, accuracy is enhanced over prior database-driven information retrieval efforts. Presents…

MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

NASA Astrophysics Data System (ADS)

Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

2015-03-01

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

Exploring access to scientific literature using content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2007-03-01

The number of articles published in the scientific medical literature is continuously increasing, and Web access to the journals is becoming common. Databases such as SPIE Digital Library, IEEE Xplore, indices such as PubMed, and search engines such as Google provide the user with sophisticated full-text search capabilities. However, information in images and graphs within these articles is entirely disregarded. In this paper, we quantify the potential impact of using content-based image retrieval (CBIR) to access this non-text data. Based on the Journal Citations Report (JCR), the journal Radiology was selected for this study. In 2005, 734 articles were published electronically in this journal. This included 2,587 figures, which yields a rate of 3.52 figures per article. Furthermore, 56.4% of these figures are composed of several individual panels, i.e. the figure combines different images and/or graphs. According to the Image Cross-Language Evaluation Forum (ImageCLEF), the error rate of automatic identification of medical images is about 15%. Therefore, it is expected that, by applying ImageCLEF-like techniques, already 95.5% of articles could be retrieved by means of CBIR. The challenge for CBIR in scientific literature, however, is the use of local texture properties to analyze individual image panels in composite illustrations. Using local features for content-based image representation, 8.81 images per article are available, and the predicted correctness rate may increase to 98.3%. From this study, we conclude that CBIR may have a high impact in medical literature research and suggest that additional research in this area is warranted.

Improving healthcare services using web based platform for management of medical case studies.

PubMed

Ogescu, Cristina; Plaisanu, Claudiu; Udrescu, Florian; Dumitru, Silviu

2008-01-01

The paper presents a web based platform for management of medical cases, support for healthcare specialists in taking the best clinical decision. Research has been oriented mostly on multimedia data management, classification algorithms for querying, retrieving and processing different medical data types (text and images). The medical case studies can be accessed by healthcare specialists and by students as anonymous case studies providing trust and confidentiality in Internet virtual environment. The MIDAS platform develops an intelligent framework to manage sets of medical data (text, static or dynamic images), in order to optimize the diagnosis and the decision process, which will reduce the medical errors and will increase the quality of medical act. MIDAS is an integrated project working on medical information retrieval from heterogeneous, distributed medical multimedia database.

Improving Database Simulations for Bayesian Precipitation Retrieval using Non-Spherical Ice Particles

NASA Astrophysics Data System (ADS)

Ringerud, S.; Skofronick Jackson, G.; Kulie, M.; Randel, D.

2016-12-01

NASA's Global Precipitation Measurement Mission (GPM) provides a wealth of both active and passive microwave observations aimed at furthering understanding of global precipitation and the hydrologic cycle. Employing a constellation of passive microwave radiometers increases global coverage and sampling, while the core satellite acts as a transfer standard, enabling consistent retrievals across individual constellation members. The transfer standard is applied in the form of a physically based a priori database constructed for use in Bayesian retrieval algorithms for each radiometer. The database is constructed using hydrometeor profiles optimized for the best fit to simultaneous active/passive core satellite measurements via the GPM Combined Algorithm. Initial validation of GPM rainfall products using the combined database suggests high retrieval errors for convective precipitation over land and at high latitudes. In such regimes, the signal from ice scattering observed at the higher microwave frequencies becomes particularly important for detecting and retrieving precipitation. For cross-track sounders such as MHS and SAPHIR, this signal is crucial. It is therefore important that the scattering signals associated with precipitation are accurately represented and modeled in the retrieval database. In the current GPM combined retrieval and constellation databases, ice hydrometeors are represented as "fluffy spheres", with assumed density and scattering parameters calculated using Mie theory. Resulting simulated Tb agree reasonably well at frequencies up to 89 GHz, but show significant biases at higher frequencies. In this work the database is recreated using an ensemble of non-spherical ice particles with single scattering properties calculated using discrete dipole approximation. Simulated Tb agreement is significantly improved across the high frequencies, decreasing biases by an order of magnitude in several of the channels. The new database is applied for a sample of GPM constellation retrievals and the retrieved precipitation rates compared, to demonstrate areas where the use of more complex ice particles will have the greatest effect upon the final retrievals.

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

NASA Astrophysics Data System (ADS)

Choi, Myungje; Kim, Jhoon; Lee, Jaehwa; Kim, Mijin; Park, Young-Je; Holben, Brent; Eck, Thomas F.; Li, Zhengqiang; Song, Chul H.

2018-01-01

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleigh-corrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τG is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τG has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τA from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τG - τA) is within -0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τG are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

Quantum Private Queries

NASA Astrophysics Data System (ADS)

Giovannetti, Vittorio; Lloyd, Seth; Maccone, Lorenzo

2008-06-01

We propose a cheat sensitive quantum protocol to perform a private search on a classical database which is efficient in terms of communication complexity. It allows a user to retrieve an item from the database provider without revealing which item he or she retrieved: if the provider tries to obtain information on the query, the person querying the database can find it out. The protocol ensures also perfect data privacy of the database: the information that the user can retrieve in a single query is bounded and does not depend on the size of the database. With respect to the known (quantum and classical) strategies for private information retrieval, our protocol displays an exponential reduction in communication complexity and in running-time computational complexity.

PIRIA: a general tool for indexing, search, and retrieval of multimedia content

NASA Astrophysics Data System (ADS)

Joint, Magali; Moellic, Pierre-Alain; Hede, P.; Adam, P.

2004-05-01

The Internet is a continuously expanding source of multimedia content and information. There are many products in development to search, retrieve, and understand multimedia content. But most of the current image search/retrieval engines, rely on a image database manually pre-indexed with keywords. Computers are still powerless to understand the semantic meaning of still or animated image content. Piria (Program for the Indexing and Research of Images by Affinity), the search engine we have developed brings this possibility closer to reality. Piria is a novel search engine that uses the query by example method. A user query is submitted to the system, which then returns a list of images ranked by similarity, obtained by a metric distance that operates on every indexed image signature. These indexed images are compared according to several different classifiers, not only Keywords, but also Form, Color and Texture, taking into account geometric transformations and variance like rotation, symmetry, mirroring, etc. Form - Edges extracted by an efficient segmentation algorithm. Color - Histogram, semantic color segmentation and spatial color relationship. Texture - Texture wavelets and local edge patterns. If required, Piria is also able to fuse results from multiple classifiers with a new classification of index categories: Single Indexer Single Call (SISC), Single Indexer Multiple Call (SIMC), Multiple Indexers Single Call (MISC) or Multiple Indexers Multiple Call (MIMC). Commercial and industrial applications will be explored and discussed as well as current and future development.

Effective method for detecting regions of given colors and the features of the region surfaces

NASA Astrophysics Data System (ADS)

Gong, Yihong; Zhang, HongJiang

1994-03-01

Color can be used as a very important cue for image recognition. In industrial and commercial areas, color is widely used as a trademark or identifying feature in objects, such as packaged goods, advertising signs, etc. In image database systems, one may retrieve an image of interest by specifying prominent colors and their locations in the image (image retrieval by contents). These facts enable us to detect or identify a target object using colors. However, this task depends mainly on how effectively we can identify a color and detect regions of the given color under possibly non-uniform illumination conditions such as shade, highlight, and strong contrast. In this paper, we present an effective method to detect regions matching given colors, along with the features of the region surfaces. We adopt the HVC color coordinates in the method because of its ability of completely separating the luminant and chromatic components of colors. Three basis functions functionally serving as the low-pass, high-pass, and band-pass filters, respectively, are introduced.

The Protein Disease Database of human body fluids: II. Computer methods and data issues.

PubMed

Lemkin, P F; Orr, G A; Goldstein, M P; Creed, G J; Myrick, J E; Merril, C R

1995-01-01

The Protein Disease Database (PDD) is a relational database of proteins and diseases. With this database it is possible to screen for quantitative protein abnormalities associated with disease states. These quantitative relationships use data drawn from the peer-reviewed biomedical literature. Assays may also include those observed in high-resolution electrophoretic gels that offer the potential to quantitate many proteins in a single test as well as data gathered by enzymatic or immunologic assays. We are using the Internet World Wide Web (WWW) and the Web browser paradigm as an access method for wide distribution and querying of the Protein Disease Database. The WWW hypertext transfer protocol and its Common Gateway Interface make it possible to build powerful graphical user interfaces that can support easy-to-use data retrieval using query specification forms or images. The details of these interactions are totally transparent to the users of these forms. Using a client-server SQL relational database, user query access, initial data entry and database maintenance are all performed over the Internet with a Web browser. We discuss the underlying design issues, mapping mechanisms and assumptions that we used in constructing the system, data entry, access to the database server, security, and synthesis of derived two-dimensional gel image maps and hypertext documents resulting from SQL database searches.

Tiny videos: a large data set for nonparametric video retrieval and frame classification.

PubMed

Karpenko, Alexandre; Aarabi, Parham

2011-03-01

In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called "tiny videos" that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation-an exemplar-based clustering algorithm-achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework [24] for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.

New public dataset for spotting patterns in medieval document images

NASA Astrophysics Data System (ADS)

En, Sovann; Nicolas, Stéphane; Petitjean, Caroline; Jurie, Frédéric; Heutte, Laurent

2017-01-01

With advances in technology, a large part of our cultural heritage is becoming digitally available. In particular, in the field of historical document image analysis, there is now a growing need for indexing and data mining tools, thus allowing us to spot and retrieve the occurrences of an object of interest, called a pattern, in a large database of document images. Patterns may present some variability in terms of color, shape, or context, making the spotting of patterns a challenging task. Pattern spotting is a relatively new field of research, still hampered by the lack of available annotated resources. We present a new publicly available dataset named DocExplore dedicated to spotting patterns in historical document images. The dataset contains 1500 images and 1464 queries, and allows the evaluation of two tasks: image retrieval and pattern localization. A standardized benchmark protocol along with ad hoc metrics is provided for a fair comparison of the submitted approaches. We also provide some first results obtained with our baseline system on this new dataset, which show that there is room for improvement and that should encourage researchers of the document image analysis community to design new systems and submit improved results.

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

PubMed

Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

2018-03-01

The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

The Imaging Spectrometric Observatory for the ATLAS 1 mission

NASA Technical Reports Server (NTRS)

Torr, Douglas G.

1995-01-01

The Imaging Spectrometric Observatory (ISO) was flown on the ATLAS 1 mission and was enormously successful, providing a baseline database on the coupled stratospheric, mesospheric, thermospheric, and ionospheric regions. Specific ISO accomplishments include measurements of the hydroxyl radical, studies of the global ionosphere, retrieval of the concentrations of neutral species from the ISO data, studies of mesospheric oxygen emissions, retrieval of mesospheric O from oxygen emissions, studies of the OH Meinel bands and the search for the Herzberg III bands, search for metallic species, studies of thermospheric nitric oxide, auroral study of molecular nitrogen emissions, and studies of thermospheric species. Apart from participation in the data analysis, the primary post-flight responsibility of Marshall Space Flight Center was the delivery of the final post mission dataset. Support provided by the University of Alabama in Huntsville is described.

A comparison of the performance of seven key bibliographic databases in identifying all relevant systematic reviews of interventions for hypertension.

PubMed

Rathbone, John; Carter, Matt; Hoffmann, Tammy; Glasziou, Paul

2016-02-09

Bibliographic databases are the primary resource for identifying systematic reviews of health care interventions. Reliable retrieval of systematic reviews depends on the scope of indexing used by database providers. Therefore, searching one database may be insufficient, but it is unclear how many need to be searched. We sought to evaluate the performance of seven major bibliographic databases for the identification of systematic reviews for hypertension. We searched seven databases (Cochrane library, Database of Abstracts of Reviews of Effects (DARE), Excerpta Medica Database (EMBASE), Epistemonikos, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Health and Turning Research Into Practice (TRIP)) from 2003 to 2015 for systematic reviews of any intervention for hypertension. Citations retrieved were screened for relevance, coded and checked for screening consistency using a fuzzy text matching query. The performance of each database was assessed by calculating its sensitivity, precision, the number of missed reviews and the number of unique records retrieved. Four hundred systematic reviews were identified for inclusion from 11,381 citations retrieved from seven databases. No single database identified all the retrieved systematic reviews for hypertension. EMBASE identified the most reviews (sensitivity 69 %) but also retrieved the most irrelevant citations with 7.2 % precision (Pr). The sensitivity of the Cochrane library was 60 %, DARE 57 %, MEDLINE 57 %, PubMed Health 53 %, Epistemonikos 49 % and TRIP 33 %. EMBASE contained the highest number of unique records (n = 43). The Cochrane library identified seven unique records and had the highest precision (Pr = 30 %), followed by Epistemonikos (n = 2, Pr = 19 %). No unique records were found in PubMed Health (Pr = 24 %) DARE (Pr = 21 %), TRIP (Pr = 10 %) or MEDLINE (Pr = 10 %). Searching EMBASE and the Cochrane library identified 88 % of all systematic reviews in the reference set, and searching the freely available databases (Cochrane, Epistemonikos, MEDLINE) identified 83 % of all the reviews. The databases were re-analysed after systematic reviews of non-conventional interventions (e.g. yoga, acupuncture) were removed. Similarly, no database identified all the retrieved systematic reviews. EMBASE identified the most relevant systematic reviews (sensitivity 73 %) but also retrieved the most irrelevant citations with Pr = 5 %. The sensitivity of the Cochrane database was 62 %, followed by MEDLINE (60 %), DARE (55 %), PubMed Health (54 %), Epistemonikos (50 %) and TRIP (31 %). The precision of the Cochrane library was the highest (20 %), followed by PubMed Health (Pr = 16 %), DARE (Pr = 13 %), Epistemonikos (Pr = 12 %), MEDLINE (Pr = 6 %), TRIP (Pr = 6 %) and EMBASE (Pr = 5 %). EMBASE contained the most unique records (n = 34). The Cochrane library identified seven unique records. The other databases held no unique records. The coverage of bibliographic databases varies considerably due to differences in their scope and content. Researchers wishing to identify systematic reviews should not rely on one database but search multiple databases.

Utilization of a multimedia PACS workstation for surgical planning of epilepsy

NASA Astrophysics Data System (ADS)

Soo Hoo, Kent; Wong, Stephen T.; Hawkins, Randall A.; Knowlton, Robert C.; Laxer, Kenneth D.; Rowley, Howard A.

1997-05-01

Surgical treatment of temporal lobe epilepsy requires the localization of the epileptogenic zone for surgical resection. Currently, clinicians utilize electroencephalography, various neuroimaging modalities, and psychological tests together to determine the location of this zone. We investigate how a multimedia neuroimaging workstation built on top of the UCSF Picture Archiving and Communication System can be used to aid surgical planning of epilepsy and related brain diseases. This usage demonstrates the ability of the workstation to retrieve image and textural data from PACS and other image sources, register multimodality images, visualize and render 3D data sets, analyze images, generate new image and text data from the analysis, and organize all data in a relational database management system.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Content-based image retrieval in medical applications for picture archiving and communication systems

NASA Astrophysics Data System (ADS)

Lehmann, Thomas M.; Guld, Mark O.; Thies, Christian; Fischer, Benedikt; Keysers, Daniel; Kohnen, Michael; Schubert, Henning; Wein, Berthold B.

2003-05-01

Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists" workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access transparency for data, methods, and experiments, (b) replication transparency for methods in development, (c) concurrency transparency for job processing and feature extraction, (d) system transparency at method implementation time, and (e) job distribution transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

Face recognition: database acquisition, hybrid algorithms, and human studies

NASA Astrophysics Data System (ADS)

Gutta, Srinivas; Huang, Jeffrey R.; Singh, Dig; Wechsler, Harry

1997-02-01

One of the most important technologies absent in traditional and emerging frontiers of computing is the management of visual information. Faces are accessible `windows' into the mechanisms that govern our emotional and social lives. The corresponding face recognition tasks considered herein include: (1) Surveillance, (2) CBIR, and (3) CBIR subject to correct ID (`match') displaying specific facial landmarks such as wearing glasses. We developed robust matching (`classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET database. The hybrid classifier architecture consist of an ensemble of connectionist networks--radial basis functions-- and decision trees. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, and (b) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experimental results, proving the feasibility of our approach, yield (i) 96% accuracy, using cross validation (CV), for surveillance on a data base consisting of 904 images (ii) 97% accuracy for CBIR tasks, on a database of 1084 images, and (iii) 93% accuracy, using CV, for CBIR subject to correct ID match tasks on a data base of 200 images.

An interactive system for computer-aided diagnosis of breast masses.

PubMed

Wang, Xingwei; Li, Lihua; Liu, Wei; Xu, Weidong; Lederman, Dror; Zheng, Bin

2012-10-01

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.

Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

Explaining scene composition using kinematic chains of humans: application to Portuguese tiles history

NASA Astrophysics Data System (ADS)

da Silva, Nuno Pinho; Marques, Manuel; Carneiro, Gustavo; Costeira, João P.

2011-03-01

Painted tile panels (Azulejos) are one of the most representative Portuguese forms of art. Most of these panels are inspired on, and sometimes are literal copies of, famous paintings, or prints of those paintings. In order to study the Azulejos, art historians need to trace these roots. To do that they manually search art image databases, looking for images similar to the representation on the tile panel. This is an overwhelming task that should be automated as much as possible. Among several cues, the pose of humans and the general composition of people in a scene is quite discriminative. We build an image descriptor, combining the kinematic chain of each character, and contextual information about their composition, in the scene. Given a query image, our system computes its similarity profile over the database. Using nearest neighbors in the space of the descriptors, the proposed system retrieves the prints that most likely inspired the tiles' work.

Face recognition using tridiagonal matrix enhanced multivariance products representation

NASA Astrophysics Data System (ADS)

Ã-zay, Evrim Korkmaz

2017-01-01

This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.

AQUAdexIM: highly efficient in-memory indexing and querying of astronomy time series images

NASA Astrophysics Data System (ADS)

Hong, Zhi; Yu, Ce; Wang, Jie; Xiao, Jian; Cui, Chenzhou; Sun, Jizhou

2016-12-01

Astronomy has always been, and will continue to be, a data-based science, and astronomers nowadays are faced with increasingly massive datasets, one key problem of which is to efficiently retrieve the desired cup of data from the ocean. AQUAdexIM, an innovative spatial indexing and querying method, performs highly efficient on-the-fly queries under users' request to search for Time Series Images from existing observation data on the server side and only return the desired FITS images to users, so users no longer need to download entire datasets to their local machines, which will only become more and more impractical as the data size keeps increasing. Moreover, AQUAdexIM manages to keep a very low storage space overhead and its specially designed in-memory index structure enables it to search for Time Series Images of a given area of the sky 10 times faster than using Redis, a state-of-the-art in-memory database.

Manchester visual query language

NASA Astrophysics Data System (ADS)

Oakley, John P.; Davis, Darryl N.; Shann, Richard T.

1993-04-01

We report a database language for visual retrieval which allows queries on image feature information which has been computed and stored along with images. The language is novel in that it provides facilities for dealing with feature data which has actually been obtained from image analysis. Each line in the Manchester Visual Query Language (MVQL) takes a set of objects as input and produces another, usually smaller, set as output. The MVQL constructs are mainly based on proven operators from the field of digital image analysis. An example is the Hough-group operator which takes as input a specification for the objects to be grouped, a specification for the relevant Hough space, and a definition of the voting rule. The output is a ranked list of high scoring bins. The query could be directed towards one particular image or an entire image database, in the latter case the bins in the output list would in general be associated with different images. We have implemented MVQL in two layers. The command interpreter is a Lisp program which maps each MVQL line to a sequence of commands which are used to control a specialized database engine. The latter is a hybrid graph/relational system which provides low-level support for inheritance and schema evolution. In the paper we outline the language and provide examples of useful queries. We also describe our solution to the engineering problems associated with the implementation of MVQL.

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists

PubMed Central

Wiley, Laura K.; Sivley, R. Michael; Bush, William S.

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist PMID:23894185

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists.

PubMed

Wiley, Laura K; Sivley, R Michael; Bush, William S

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist.

Influence of aerosol estimation on coastal water products retrieved from HICO images

NASA Astrophysics Data System (ADS)

Patterson, Karen W.; Lamela, Gia

2011-06-01

The Hyperspectral Imager for the Coastal Ocean (HICO) is a hyperspectral sensor which was launched to the International Space Station in September 2009. The Naval Research Laboratory (NRL) has been developing the Coastal Water Signatures Toolkit (CWST) to estimate water depth, bottom type and water column constituents such as chlorophyll, suspended sediments and chromophoric dissolved organic matter from hyperspectral imagery. The CWST uses a look-up table approach, comparing remote sensing reflectance spectra observed in an image to a database of modeled spectra for pre-determined water column constituents, depth and bottom type. In order to successfully use this approach, the remote sensing reflectances must be accurate which implies accurately correcting for the atmospheric contribution to the HICO top of the atmosphere radiances. One tool the NRL is using to atmospherically correct HICO imagery is Correction of Coastal Ocean Atmospheres (COCOA), which is based on Tafkaa 6S. One of the user input parameters to COCOA is aerosol optical depth or aerosol visibility, which can vary rapidly over short distances in coastal waters. Changes to the aerosol thickness results in changes to the magnitude of the remote sensing reflectances. As such, the CWST retrievals for water constituents, depth and bottom type can be expected to vary in like fashion. This work is an illustration of the variability in CWST retrievals due to inaccurate aerosol thickness estimation during atmospheric correction of HICO images.

Multimodality medical image database for temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Siadat, Mohammad-Reza; Soltanian-Zadeh, Hamid; Fotouhi, Farshad A.; Elisevich, Kost

2003-05-01

This paper presents the development of a human brain multi-modality database for surgical candidacy determination in temporal lobe epilepsy. The focus of the paper is on content-based image management, navigation and retrieval. Several medical image-processing methods including our newly developed segmentation method are utilized for information extraction/correlation and indexing. The input data includes T1-, T2-Weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data and EEG data analysis. The database can answer queries regarding issues such as the correlation between the attribute X of the entity Y and the outcome of a temporal lobe epilepsy surgery. The entity Y can be a brain anatomical structure such as the hippocampus. The attribute X can be either a functionality feature of the anatomical structure Y, calculated with SPECT modalities, such as signal average, or a volumetric/morphological feature of the entity Y such as volume or average curvature. The outcome of the surgery can be any surgery assessment such as non-verbal Wechsler memory quotient. A determination is made regarding surgical candidacy by analysis of both textual and image data. The current database system suggests a surgical determination for the cases with relatively small hippocampus and high signal intensity average on FLAIR images within the hippocampus. This indication matches the neurosurgeons expectations/observations. Moreover, as the database gets more populated with patient profiles and individual surgical outcomes, using data mining methods one may discover partially invisible correlations between the contents of different modalities of data and the outcome of the surgery.

Image-based diagnostic aid for interstitial lung disease with secondary data integration

NASA Astrophysics Data System (ADS)

Depeursinge, Adrien; Müller, Henning; Hidki, Asmâa; Poletti, Pierre-Alexandre; Platon, Alexandra; Geissbuhler, Antoine

2007-03-01

Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

Integration of Information Retrieval and Database Management Systems.

ERIC Educational Resources Information Center

Deogun, Jitender S.; Raghavan, Vijay V.

1988-01-01

Discusses the motivation for integrating information retrieval and database management systems, and proposes a probabilistic retrieval model in which records in a file may be composed of attributes (formatted data items) and descriptors (content indicators). The details and resolutions of difficulties involved in integrating such systems are…

Searching for evidence or approval? A commentary on database search in systematic reviews and alternative information retrieval methodologies.

PubMed

Delaney, Aogán; Tamás, Peter A

2018-03-01

Despite recognition that database search alone is inadequate even within the health sciences, it appears that reviewers in fields that have adopted systematic review are choosing to rely primarily, or only, on database search for information retrieval. This commentary reminds readers of factors that call into question the appropriateness of default reliance on database searches particularly as systematic review is adapted for use in new and lower consensus fields. It then discusses alternative methods for information retrieval that require development, formalisation, and evaluation. Our goals are to encourage reviewers to reflect critically and transparently on their choice of information retrieval methods and to encourage investment in research on alternatives. Copyright © 2017 John Wiley & Sons, Ltd.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Nonmaterialized Relations and the Support of Information Retrieval Applications by Relational Database Systems.

ERIC Educational Resources Information Center

Lynch, Clifford A.

1991-01-01

Describes several aspects of the problem of supporting information retrieval system query requirements in the relational database management system (RDBMS) environment and proposes an extension to query processing called nonmaterialized relations. User interactions with information retrieval systems are discussed, and nonmaterialized relations are…

Establishment of an inferior vena cava filter database and interventional radiology led follow-up - retrieval rates and patients lost to follow-up.

PubMed

Klinken, Sven; Humphries, Charlotte; Ferguson, John

2017-10-01

To evaluate the rates of inferior vena cava (IVC) filter retrieval and the number of patient's lost to follow-up, before and after the establishment of an IVC filter database and interventional radiology (inserting physician) led follow-up. On the 1st of June 2012, an electronic interventional radiology database was established at our Institution. In addition, the interventional radiology team took responsibility for follow-up of IVC filters. Data were prospectively collected from the database for all patients who had an IVC filter inserted between the 1st June 2012 and the 31st May 2014. Data on patients who had an IVC filter inserted between the 1st of June 2009 to the 31st of May 2012 were retrospectively reviewed. Patient demographics, insertion indications, filter types, retrieval status, documented retrieval decisions, time in situ, trackable events and complications were obtained in the pre-database (n = 136) and post-database (n = 118) cohorts. Attempted IVC filter retrieval rates were improved from 52.9% to 72.9% (P = 0.001) following the establishment of the database. The number of patients with no documented decision (lost to follow-up) regarding their IVC filter reduced from 31 of 136 (23%) to 0 of 118 patients (P = < 0.001). There was a non-significant reduction in IVC filter dwell time in the post-database group (113 as compared to 137 days, P = 0.129). Following the establishment of an IVC filter database and interventional radiology led follow-up, we demonstrate a significant improvement in the attempted retrieval rates of IVC filters and the number of patient's lost to follow-up. © 2017 The Royal Australian and New Zealand College of Radiologists.

A retrieval algorithm of hydrometer profile for submillimeter-wave radiometer

NASA Astrophysics Data System (ADS)

Liu, Yuli; Buehler, Stefan; Liu, Heguang

2017-04-01

Vertical profiles of particle microphysics perform vital functions for the estimation of climatic feedback. This paper proposes a new algorithm to retrieve the profile of the parameters of the hydrometeor(i.e., ice, snow, rain, liquid cloud, graupel) based on passive submillimeter-wave measurements. These parameters include water content and particle size. The first part of the algorithm builds the database and retrieves the integrated quantities. Database is built up by Atmospheric Radiative Transfer Simulator(ARTS), which uses atmosphere data to simulate the corresponding brightness temperature. Neural network, trained by the precalculated database, is developed to retrieve the water path for each type of particles. The second part of the algorithm analyses the statistical relationship between water path and vertical parameters profiles. Based on the strong dependence existing between vertical layers in the profiles, Principal Component Analysis(PCA) technique is applied. The third part of the algorithm uses the forward model explicitly to retrieve the hydrometeor profiles. Cost function is calculated in each iteration, and Differential Evolution(DE) algorithm is used to adjust the parameter values during the evolutionary process. The performance of this algorithm is planning to be verified for both simulation database and measurement data, by retrieving profiles in comparison with the initial one. Results show that this algorithm has the ability to retrieve the hydrometeor profiles efficiently. The combination of ARTS and optimization algorithm can get much better results than the commonly used database approach. Meanwhile, the concept that ARTS can be used explicitly in the retrieval process shows great potential in providing solution to other retrieval problems.

Unsupervised universal steganalyzer for high-dimensional steganalytic features

NASA Astrophysics Data System (ADS)

Hou, Xiaodan; Zhang, Tao

2016-11-01

The research in developing steganalytic features has been highly successful. These features are extremely powerful when applied to supervised binary classification problems. However, they are incompatible with unsupervised universal steganalysis because the unsupervised method cannot distinguish embedding distortion from varying levels of noises caused by cover variation. This study attempts to alleviate the problem by introducing similarity retrieval of image statistical properties (SRISP), with the specific aim of mitigating the effect of cover variation on the existing steganalytic features. First, cover images with some statistical properties similar to those of a given test image are searched from a retrieval cover database to establish an aided sample set. Then, unsupervised outlier detection is performed on a test set composed of the given test image and its aided sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called SRISP-aided unsupervised outlier detection, requires no training. Thus, it does not suffer from model mismatch mess. Compared with prior unsupervised outlier detectors that do not consider SRISP, the proposed framework not only retains the universality but also exhibits superior performance when applied to high-dimensional steganalytic features.

Methods and apparatus for constructing and implementing a universal extension module for processing objects in a database

NASA Technical Reports Server (NTRS)

Li, Chung-Sheng (Inventor); Smith, John R. (Inventor); Chang, Yuan-Chi (Inventor); Jhingran, Anant D. (Inventor); Padmanabhan, Sriram K. (Inventor); Hsiao, Hui-I (Inventor); Choy, David Mun-Hien (Inventor); Lin, Jy-Jine James (Inventor); Fuh, Gene Y. C. (Inventor); Williams, Robin (Inventor)

2004-01-01

Methods and apparatus for providing a multi-tier object-relational database architecture are disclosed. In one illustrative embodiment of the present invention, a multi-tier database architecture comprises an object-relational database engine as a top tier, one or more domain-specific extension modules as a bottom tier, and one or more universal extension modules as a middle tier. The individual extension modules of the bottom tier operationally connect with the one or more universal extension modules which, themselves, operationally connect with the database engine. The domain-specific extension modules preferably provide such functions as search, index, and retrieval services of images, video, audio, time series, web pages, text, XML, spatial data, etc. The domain-specific extension modules may include one or more IBM DB2 extenders, Oracle data cartridges and/or Informix datablades, although other domain-specific extension modules may be used.

GOCI Yonsei aerosol retrieval version 2 aerosol products: improved algorithm description and error analysis with uncertainty estimation from 5-year validation over East Asia

NASA Astrophysics Data System (ADS)

Choi, M.; Kim, J.; Lee, J.; KIM, M.; Park, Y. J.; Holben, B. N.; Eck, T. F.; Li, Z.; Song, C. H.

2017-12-01

The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed for retrieving hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD showed comparable accuracy compared to ground-based and other satellite-based observations, but still had errors due to uncertainties in surface reflectance and simple cloud masking. Also, it was not capable of near-real-time (NRT) processing because it required a monthly database of each year encompassing the day of retrieval for the determination of surface reflectance. This study describes the improvement of GOCI YAER algorithm to the version 2 (V2) for NRT processing with improved accuracy from the modification of cloud masking, surface reflectance determination using multi-year Rayleigh corrected reflectance and wind speed database, and inversion channels per surface conditions. Therefore, the improved GOCI AOD ( ) is similar with those of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD compared to V1 of the YAER algorithm. The shows reduced median bias and increased ratio within range (i.e. absolute expected error range of MODIS AOD) compared to V1 in the validation results using Aerosol Robotic Network (AERONET) AOD ( ) from 2011 to 2016. The validation using the Sun-Sky Radiometer Observation Network (SONET) over China also shows similar results. The bias of error ( is within -0.1 and 0.1 range as a function of AERONET AOD and AE, scattering angle, NDVI, cloud fraction and homogeneity of retrieved AOD, observation time, month, and year. Also, the diagnostic and prognostic expected error (DEE and PEE, respectively) of are estimated. The estimated multiple PEE of GOCI V2 AOD is well matched with actual error over East Asia, and the GOCI V2 AOD over Korea shows higher ratio within PEE compared to over China and Japan. Hourly AOD products based on the improved GOCI YAER AOD could contribute to better understandings of aerosols in terms of long-term climate changes and short-term air quality monitoring and forecasting perspectives over East Asia, especially rapid diurnal variation and transboundary transport.

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases

PubMed Central

Tiede, Dirk; Baraldi, Andrea; Sudmanns, Martin; Belgiu, Mariana; Lang, Stefan

2017-01-01

ABSTRACT Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model. PMID:29098143

Research of image retrieval technology based on color feature

NASA Astrophysics Data System (ADS)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram make rotating and translation does not change. The HSV color space is used to show color characteristic of image, which is suitable to the visual characteristic of human. Taking advance of human's feeling to color, it quantifies color sector with unequal interval, and get characteristic vector. Finally, it matches the similarity of image with the algorithm of the histogram intersection and the partition-overall histogram. Users can choose a demonstration image to show inquired vision require, and also can adjust several right value through the relevance-feedback method to obtain the best result of search.An image retrieval system based on these approaches is presented. The result of the experiments shows that the image retrieval based on partition-overall histogram can keep the space distribution information while abstracting color feature efficiently, and it is superior to the normal color histograms in precision rate while researching. The query precision rate is more than 95%. In addition, the efficient block expression will lower the complicate degree of the images to be searched, and thus the searching efficiency will be increased. The image retrieval algorithms based on the partition-overall histogram proposed in the paper is efficient and effective.

3D visualization of molecular structures in the MOGADOC database

NASA Astrophysics Data System (ADS)

Vogt, Natalja; Popov, Evgeny; Rudert, Rainer; Kramer, Rüdiger; Vogt, Jürgen

2010-08-01

The MOGADOC database (Molecular Gas-Phase Documentation) is a powerful tool to retrieve information about compounds which have been studied in the gas-phase by electron diffraction, microwave spectroscopy and molecular radio astronomy. Presently the database contains over 34,500 bibliographic references (from the beginning of each method) for about 10,000 inorganic, organic and organometallic compounds and structural data (bond lengths, bond angles, dihedral angles, etc.) for about 7800 compounds. Most of the implemented molecular structures are given in a three-dimensional (3D) presentation. To create or edit and visualize the 3D images of molecules, new tools (special editor and Java-based 3D applet) were developed. Molecular structures in internal coordinates were converted to those in Cartesian coordinates.

Design of a web portal for interdisciplinary image retrieval from multiple online image resources.

PubMed

Kammerer, F J; Frankewitsch, T; Prokosch, H-U

2009-01-01

Images play an important role in medicine. Finding the desired images within the multitude of online image databases is a time-consuming and frustrating process. Existing websites do not meet all the requirements for an ideal learning environment for medical students. This work intends to establish a new web portal providing a centralized access point to a selected number of online image databases. A back-end system locates images on given websites and extracts relevant metadata. The images are indexed using UMLS and the MetaMap system provided by the US National Library of Medicine. Specially developed functions allow to create individual navigation structures. The front-end system suits the specific needs of medical students. A navigation structure consisting of several medical fields, university curricula and the ICD-10 was created. The images may be accessed via the given navigation structure or using different search functions. Cross-references are provided by the semantic relations of the UMLS. Over 25,000 images were identified and indexed. A pilot evaluation among medical students showed good first results concerning the acceptance of the developed navigation structures and search features. The integration of the images from different sources into the UMLS semantic network offers a quick and an easy-to-use learning environment.

Combining Digital Watermarking and Fingerprinting Techniques to Identify Copyrights for Color Images

PubMed Central

Hsieh, Shang-Lin; Chen, Chun-Che; Shen, Wen-Shan

2014-01-01

This paper presents a copyright identification scheme for color images that takes advantage of the complementary nature of watermarking and fingerprinting. It utilizes an authentication logo and the extracted features of the host image to generate a fingerprint, which is then stored in a database and also embedded in the host image to produce a watermarked image. When a dispute over the copyright of a suspect image occurs, the image is first processed by watermarking. If the watermark can be retrieved from the suspect image, the copyright can then be confirmed; otherwise, the watermark then serves as the fingerprint and is processed by fingerprinting. If a match in the fingerprint database is found, then the suspect image will be considered a duplicated one. Because the proposed scheme utilizes both watermarking and fingerprinting, it is more robust than those that only adopt watermarking, and it can also obtain the preliminary result more quickly than those that only utilize fingerprinting. The experimental results show that when the watermarked image suffers slight attacks, watermarking alone is enough to identify the copyright. The results also show that when the watermarked image suffers heavy attacks that render watermarking incompetent, fingerprinting can successfully identify the copyright, hence demonstrating the effectiveness of the proposed scheme. PMID:25114966

Storage and distribution of pathology digital images using integrated web-based viewing systems.

PubMed

Marchevsky, Alberto M; Dulbandzhyan, Ronda; Seely, Kevin; Carey, Steve; Duncan, Raymond G

2002-05-01

Health care providers have expressed increasing interest in incorporating digital images of gross pathology specimens and photomicrographs in routine pathology reports. To describe the multiple technical and logistical challenges involved in the integration of the various components needed for the development of a system for integrated Web-based viewing, storage, and distribution of digital images in a large health system. An Oracle version 8.1.6 database was developed to store, index, and deploy pathology digital photographs via our Intranet. The database allows for retrieval of images by patient demographics or by SNOMED code information. The Intranet of a large health system accessible from multiple computers located within the medical center and at distant private physician offices. The images can be viewed using any of the workstations of the health system that have authorized access to our Intranet, using a standard browser or a browser configured with an external viewer or inexpensive plug-in software, such as Prizm 2.0. The images can be printed on paper or transferred to film using a digital film recorder. Digital images can also be displayed at pathology conferences by using wireless local area network (LAN) and secure remote technologies. The standardization of technologies and the adoption of a Web interface for all our computer systems allows us to distribute digital images from a pathology database to a potentially large group of users distributed in multiple locations throughout a large medical center.

Term Relevance Feedback and Mediated Database Searching: Implications for Information Retrieval Practice and Systems Design.

ERIC Educational Resources Information Center

Spink, Amanda

1995-01-01

This study uses the human approach to examine the sources and effectiveness of search terms selected during 40 mediated interactive database searches and focuses on determining the retrieval effectiveness of search terms identified by users and intermediaries from retrieved items during term relevance feedback. (Author/JKP)

WWW Entrez: A Hypertext Retrieval Tool for Molecular Biology.

ERIC Educational Resources Information Center

Epstein, Jonathan A.; Kans, Jonathan A.; Schuler, Gregory D.

This article describes the World Wide Web (WWW) Entrez server which is based upon the National Center for Biotechnology Information's (NCBI) Entrez retrieval database and software. Entrez is a molecular sequence retrieval system that contains an integrated view of portions of Medline and all publicly available nucleotide and protein databases,…

An algorithm for estimating aerosol optical depth from HIMAWARI-8 data over Ocean

NASA Astrophysics Data System (ADS)

Lee, Kwon Ho

2016-04-01

The paper presents currently developing algorithm for aerosol detection and retrieval over ocean for the next generation geostationary satellite, HIMAWARI-8. Enhanced geostationary remote sensing observations are now enables for aerosol retrieval of dust, smoke, and ash, which began a new era of geostationary aerosol observations. Sixteen channels of the Advanced HIMAWARI Imager (AHI) onboard HIMAWARI-8 offer capabilities for aerosol remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Aerosols were estimated in detection processing from visible and infrared channel radiances, and in retrieval processing using the inversion-optimization of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every ten minutes for pixel sizes of ~8 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously. The instantaneous retrieved AOD is evaluated by the MODIS level 2 operational aerosol products (C006), and the daily retrieved AOD was compared with ground-based measurements from the AERONET databases. The results show that the detection of aerosol and estimated AOD are in good agreement with the MODIS data and ground measurements with a correlation coefficient of ˜0.90 and a bias of 4%. These results suggest that the proposed method applied to the HIMAWARI-8 satellite data can accurately estimate continuous AOD. Acknowledgments This work was supported by "Development of Geostationary Meteorological Satellite Ground Segment(NMSC-2014-01)" program funded by National Meteorological Satellite Centre(NMSC) of Korea Meteorological Administration(KMA).

An intelligent interactive visual database management system for Space Shuttle closeout image management

NASA Technical Reports Server (NTRS)

Ragusa, James M.; Orwig, Gary; Gilliam, Michael; Blacklock, David; Shaykhian, Ali

1994-01-01

Status is given of an applications investigation on the potential for using an expert system shell for classification and retrieval of high resolution, digital, color space shuttle closeout photography. This NASA funded activity has focused on the use of integrated information technologies to intelligently classify and retrieve still imagery from a large, electronically stored collection. A space shuttle processing problem is identified, a working prototype system is described, and commercial applications are identified. A conclusion reached is that the developed system has distinct advantages over the present manual system and cost efficiencies will result as the system is implemented. Further, commercial potential exists for this integrated technology.

Detailed description of the Mayo/IBM PACS

NASA Astrophysics Data System (ADS)

Gehring, Dale G.; Persons, Kenneth R.; Rothman, Melvyn L.; Salutz, James R.; Morin, Richard L.

1991-07-01

The Mayo Clinic and IBM/Rochester have jointly developed a picture archiving system (PACS) for use with Mayo's MRI and Neuro-CT imaging modalities. The system was developed to replace the imaging system's vendor-supplied magnetic tape archiving capability. The system consists of seven MR imagers and nine CT scanners, each interfaced to the PACS via IBM Personal System/2(tm) (PS/2) computers, which act as gateways from the imaging modality to the PACS network. The PAC system operates on the token-ring component of Mayo's city-wide local area network. Also on the PACS network are four optical storage subsystems used for image archival, three optical subsystems used for image retrieval, an IBM Application System/400(tm) (AS/400) computer used for database management and multiple PS/2-based image display systems and their image servers.

Method for localizing and isolating an errant process step

DOEpatents

Tobin, Jr., Kenneth W.; Karnowski, Thomas P.; Ferrell, Regina K.

2003-01-01

A method for localizing and isolating an errant process includes the steps of retrieving from a defect image database a selection of images each image having image content similar to image content extracted from a query image depicting a defect, each image in the selection having corresponding defect characterization data. A conditional probability distribution of the defect having occurred in a particular process step is derived from the defect characterization data. A process step as a highest probable source of the defect according to the derived conditional probability distribution is then identified. A method for process step defect identification includes the steps of characterizing anomalies in a product, the anomalies detected by an imaging system. A query image of a product defect is then acquired. A particular characterized anomaly is then correlated with the query image. An errant process step is then associated with the correlated image.

Developing a Large Lexical Database for Information Retrieval, Parsing, and Text Generation Systems.

ERIC Educational Resources Information Center

Conlon, Sumali Pin-Ngern; And Others

1993-01-01

Important characteristics of lexical databases and their applications in information retrieval and natural language processing are explained. An ongoing project using various machine-readable sources to build a lexical database is described, and detailed designs of individual entries with examples are included. (Contains 66 references.) (EAM)

Integrating Borrowed Records into a Database: Impact on Thesaurus Development and Retrieval.

ERIC Educational Resources Information Center

And Others; Kirtland, Monika

1980-01-01

Discusses three approaches to thesaurus and indexing/retrieval language maintenance for combined databases: reindexing, merging, and initial standardization. Two thesauri for a combined database are evaluated in terms of their compatibility, and indexing practices are compared. Tables and figures help illustrate aspects of the comparison. (SW)

Subject Retrieval from Full-Text Databases in the Humanities

ERIC Educational Resources Information Center

East, John W.

2007-01-01

This paper examines the problems involved in subject retrieval from full-text databases of secondary materials in the humanities. Ten such databases were studied and their search functionality evaluated, focusing on factors such as Boolean operators, document surrogates, limiting by subject area, proximity operators, phrase searching, wildcards,…

A data model and database for high-resolution pathology analytical image informatics.

PubMed

Wang, Fusheng; Kong, Jun; Cooper, Lee; Pan, Tony; Kurc, Tahsin; Chen, Wenjin; Sharma, Ashish; Niedermayr, Cristobal; Oh, Tae W; Brat, Daniel; Farris, Alton B; Foran, David J; Saltz, Joel

2011-01-01

The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features. We have designed a data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features. We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei. Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results.

Context indexing of digital cardiac ultrasound records in PACS

NASA Astrophysics Data System (ADS)

Lobodzinski, S. Suave; Meszaros, Georg N.

1998-07-01

Recent wide adoption of the DICOM 3.0 standard by ultrasound equipment vendors created a need for practical clinical implementations of cardiac imaging study visualization, management and archiving, DICOM 3.0 defines only a logical and physical format for exchanging image data (still images, video, patient and study demographics). All DICOM compliant imaging studies must presently be archived on a 650 Mb recordable compact disk. This is a severe limitation for ultrasound applications where studies of 3 to 10 minutes long are a common practice. In addition, DICOM digital echocardiography objects require physiological signal indexing, content segmentation and characterization. Since DICOM 3.0 is an interchange standard only, it does not define how to database composite video objects. The goal of this research was therefore to address the issues of efficient storage, retrieval and management of DICOM compliant cardiac video studies in a distributed PACS environment. Our Web based implementation has the advantage of accommodating both DICOM defined entity-relation modules (equipment data, patient data, video format, etc.) in standard relational database tables and digital indexed video with its attributes in an object relational database. Object relational data model facilitates content indexing of full motion cardiac imaging studies through bi-directional hyperlink generation that tie searchable video attributes and related objects to individual video frames in the temporal domain. Benefits realized from use of bi-directionally hyperlinked data models in an object relational database include: (1) real time video indexing during image acquisition, (2) random access and frame accurate instant playback of previously recorded full motion imaging data, and (3) time savings from faster and more accurate access to data through multiple navigation mechanisms such as multidimensional queries on an index, queries on a hyperlink attribute, free search and browsing.

SCEGRAM: An image database for semantic and syntactic inconsistencies in scenes.

PubMed

Öhlschläger, Sabine; Võ, Melissa Le-Hoa

2017-10-01

Our visual environment is not random, but follows compositional rules according to what objects are usually found where. Despite the growing interest in how such semantic and syntactic rules - a scene grammar - enable effective attentional guidance and object perception, no common image database containing highly-controlled object-scene modifications has been publically available. Such a database is essential in minimizing the risk that low-level features drive high-level effects of interest, which is being discussed as possible source of controversial study results. To generate the first database of this kind - SCEGRAM - we took photographs of 62 real-world indoor scenes in six consistency conditions that contain semantic and syntactic (both mild and extreme) violations as well as their combinations. Importantly, always two scenes were paired, so that an object was semantically consistent in one scene (e.g., ketchup in kitchen) and inconsistent in the other (e.g., ketchup in bathroom). Low-level salience did not differ between object-scene conditions and was generally moderate. Additionally, SCEGRAM contains consistency ratings for every object-scene condition, as well as object-absent scenes and object-only images. Finally, a cross-validation using eye-movements replicated previous results of longer dwell times for both semantic and syntactic inconsistencies compared to consistent controls. In sum, the SCEGRAM image database is the first to contain well-controlled semantic and syntactic object-scene inconsistencies that can be used in a broad range of cognitive paradigms (e.g., verbal and pictorial priming, change detection, object identification, etc.) including paradigms addressing developmental aspects of scene grammar. SCEGRAM can be retrieved for research purposes from http://www.scenegrammarlab.com/research/scegram-database/ .

(BARS) -- Bibliographic Retrieval System Sandia Shock Compression (SSC) database Shock Physics Index (SPHINX) database. Volume 1: UNIX version query guide customized application for INGRES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herrmann, W.; von Laven, G.M.; Parker, T.

1993-09-01

The Bibliographic Retrieval System (BARS) is a data base management system specially designed to retrieve bibliographic references. Two databases are available, (i) the Sandia Shock Compression (SSC) database which contains over 5700 references to the literature related to stress waves in solids and their applications, and (ii) the Shock Physics Index (SPHINX) which includes over 8000 further references to stress waves in solids, material properties at intermediate and low rates, ballistic and hypervelocity impact, and explosive or shock fabrication methods. There is some overlap in the information in the two data bases.

Medication order communication using fax and document-imaging technologies.

PubMed

Simonian, Armen I

2008-03-15

The implementation of fax and document-imaging technology to electronically communicate medication orders from nursing stations to the pharmacy is described. The evaluation of a commercially available pharmacy order imaging system to improve order communication and to make document retrieval more efficient led to the selection and customization of a system already licensed and used in seven affiliated hospitals. The system consisted of existing fax machines and document-imaging software that would capture images of written orders and send them from nursing stations to a central database server. Pharmacists would then retrieve the images and enter the orders in an electronic medical record system. The pharmacy representatives from all seven hospitals agreed on the configuration and functionality of the custom application. A 30-day trial of the order imaging system was successfully conducted at one of the larger institutions. The new system was then implemented at the remaining six hospitals over a period of 60 days. The transition from a paper-order system to electronic communication via a standardized pharmacy document management application tailored to the specific needs of this health system was accomplished. A health system with seven affiliated hospitals successfully implemented electronic communication and the management of inpatient paper-chart orders by using faxes and document-imaging technology. This standardized application eliminated the problems associated with the hand delivery of paper orders, the use of the pneumatic tube system, and the printing of traditional faxes.

A mobile system for skin cancer diagnosis and monitoring

NASA Astrophysics Data System (ADS)

Gu, Yanliang; Tang, Jinshan

2014-05-01

In this paper, we propose a mobile system for aiding doctors in skin cancer diagnosis and other persons in skin cancer monitoring. The basic idea is to use image retrieval techniques to help the users to find the similar skin cancer cases stored in a database by using smart phones. The query image can be taken by a smart phone from a patient or can be uploaded from other resources. The shapes of the skin lesions are used for matching two skin lesions, which are segmented from skin images using the skin lesion extraction method developed in 1. The features used in the proposed system are obtained by Fourier descriptor. A prototype application has been developed and can be installed in an iPhone. In this application, the iPhone users can use the iPhone as a diagnosis tool to find the potential skin lesions in a persons' skin and compare the skin lesions detected by the iPhone with the skin lesions stored in a database in a remote server.

Precipitation from the GPM Microwave Imager and Constellation Radiometers

NASA Astrophysics Data System (ADS)

Kummerow, Christian; Randel, David; Kirstetter, Pierre-Emmanuel; Kulie, Mark; Wang, Nai-Yu

2014-05-01

Satellite precipitation retrievals from microwave sensors are fundamentally underconstrained requiring either implicit or explicit a-priori information to constrain solutions. The radiometer algorithm designed for the GPM core and constellation satellites makes this a-priori information explicit in the form of a database of possible rain structures from the GPM core satellite and a Bayesian retrieval scheme. The a-priori database will eventually come from the GPM core satellite's combined radar/radiometer retrieval algorithm. That product is physically constrained to ensure radiometric consistency between the radars and radiometers and is thus ideally suited to create the a-priori databases for all radiometers in the GPM constellation. Until a robust product exists, however, the a-priori databases are being generated from the combination of existing sources over land and oceans. Over oceans, the Day-1 GPM radiometer algorithm uses the TRMM PR/TMI physically derived hydrometer profiles that are available from the tropics through sea surface temperatures of approximately 285K. For colder sea surface temperatures, the existing profiles are used with lower hydrometeor layers removed to correspond to colder conditions. While not ideal, the results appear to be reasonable placeholders until the full GPM database can be constructed. It is more difficult to construct physically consistent profiles over land due to ambiguities in surface emissivities as well as details of the ice scattering that dominates brightness temperature signatures over land. Over land, the a-priori databases have therefore been constructed by matching satellite overpasses to surface radar data derived from the WSR-88 network over the continental United States through the National Mosaic and Multi-Sensor QPE (NMQ) initiative. Databases are generated as a function of land type (4 categories of increasing vegetation cover as well as 4 categories of increasing snow depth), land surface temperature and total precipitable water. One year of coincident observations, generating 20 and 80 million database entries, depending upon the sensor, are used in the retrieval algorithm. The remaining areas such as sea ice and high latitude coastal zones are filled with a combination of CloudSat and AMSR-E plus MHS observations together with a model to create the equivalent databases for other radiometers in the constellation. The most noteworthy result from the Day-1 algorithm is the quality of the land products when compared to existing products. Unlike previous versions of land algorithms that depended upon complex screening routines to decide if pixels were precipitating or not, the current scheme is free of conditional rain statements and appears to produce rain rate with much greater fidelity than previous schemes. There results will be shown.

Memory states influence value-based decisions.

PubMed

Duncan, Katherine D; Shohamy, Daphna

2016-11-01

Using memory to guide decisions allows past experience to improve future outcomes. However, the circumstances that modulate how and when memory influences decisions are not well understood. Here, we report that the use of memories to guide decisions depends on the context in which these decisions are made. We show that decisions made in the context of familiar images are more likely to be influenced by past events than are decisions made in the context of novel images (Experiment 1), that this bias persists even when a temporal gap is introduced between the image presentation and the decision (Experiment 2), and that contextual novelty facilitates value learning whereas familiarity facilitates the retrieval and use of previously learned values (Experiment 3). These effects are consistent with neurobiological and computational models of memory, which propose that familiar images evoke a lingering "retrieval state" that facilitates the recollection of other episodic memories. Together, these experiments highlight the importance of episodic memory for decision-making and provide an example of how computational and neurobiological theories can lead to new insights into how and when different types of memories guide our choices. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Artistic image analysis using graph-based learning approaches.

PubMed

Carneiro, Gustavo

2013-08-01

We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures.

In-house access to PACS images and related data through World Wide Web

NASA Astrophysics Data System (ADS)

Mascarini, Christian; Ratib, Osman M.; Trayser, Gerhard; Ligier, Yves; Appel, R. D.

1996-05-01

The development of a hospital wide PACS is in progress at the University Hospital of Geneva and several archive modules are operational since 1992. This PACS is intended for wide distribution of images to clinical wards. As the PACS project and the number of archived images grow rapidly in the hospital, it was necessary to provide an easy, more widely accessible and convenient access to the PACS database for the clinicians in the different wards and clinical units of the hospital. An innovative solution has been developed using tools such as Netscape navigator and NCSA World Wide Web server as an alternative to conventional database query and retrieval software. These tools present the advantages of providing an user interface which is the same independently of the platform being used (Mac, Windows, UNIX, ...), and an easy integration of different types of documents (text, images, ...). A strict access control has been added to this interface. It allows user identification and access rights checking, as defined by the in-house hospital information system, before allowing the navigation through patient data records.

The Chandra Source Catalog: Storage and Interfaces

NASA Astrophysics Data System (ADS)

van Stone, David; Harbo, Peter N.; Tibbetts, Michael S.; Zografou, Panagoula; Evans, Ian N.; Primini, Francis A.; Glotfelty, Kenny J.; Anderson, Craig S.; Bonaventura, Nina R.; Chen, Judy C.; Davis, John E.; Doe, Stephen M.; Evans, Janet D.; Fabbiano, Giuseppina; Galle, Elizabeth C.; Gibbs, Danny G., II; Grier, John D.; Hain, Roger; Hall, Diane M.; He, Xiang Qun (Helen); Houck, John C.; Karovska, Margarita; Kashyap, Vinay L.; Lauer, Jennifer; McCollough, Michael L.; McDowell, Jonathan C.; Miller, Joseph B.; Mitschang, Arik W.; Morgan, Douglas L.; Mossman, Amy E.; Nichols, Joy S.; Nowak, Michael A.; Plummer, David A.; Refsdal, Brian L.; Rots, Arnold H.; Siemiginowska, Aneta L.; Sundheim, Beth A.; Winkelman, Sherry L.

2009-09-01

The Chandra Source Catalog (CSC) is part of the Chandra Data Archive (CDA) at the Chandra X-ray Center. The catalog contains source properties and associated data objects such as images, spectra, and lightcurves. The source properties are stored in relational databases and the data objects are stored in files with their metadata stored in databases. The CDA supports different versions of the catalog: multiple fixed release versions and a live database version. There are several interfaces to the catalog: CSCview, a graphical interface for building and submitting queries and for retrieving data objects; a command-line interface for property and source searches using ADQL; and VO-compliant services discoverable though the VO registry. This poster describes the structure of the catalog and provides an overview of the interfaces.

Rasdaman for Big Spatial Raster Data

NASA Astrophysics Data System (ADS)

Hu, F.; Huang, Q.; Scheele, C. J.; Yang, C. P.; Yu, M.; Liu, K.

2015-12-01

Spatial raster data have grown exponentially over the past decade. Recent advancements on data acquisition technology, such as remote sensing, have allowed us to collect massive observation data of various spatial resolution and domain coverage. The volume, velocity, and variety of such spatial data, along with the computational intensive nature of spatial queries, pose grand challenge to the storage technologies for effective big data management. While high performance computing platforms (e.g., cloud computing) can be used to solve the computing-intensive issues in big data analysis, data has to be managed in a way that is suitable for distributed parallel processing. Recently, rasdaman (raster data manager) has emerged as a scalable and cost-effective database solution to store and retrieve massive multi-dimensional arrays, such as sensor, image, and statistics data. Within this paper, the pros and cons of using rasdaman to manage and query spatial raster data will be examined and compared with other common approaches, including file-based systems, relational databases (e.g., PostgreSQL/PostGIS), and NoSQL databases (e.g., MongoDB and Hive). Earth Observing System (EOS) data collected from NASA's Atmospheric Scientific Data Center (ASDC) will be used and stored in these selected database systems, and a set of spatial and non-spatial queries will be designed to benchmark their performance on retrieving large-scale, multi-dimensional arrays of EOS data. Lessons learnt from using rasdaman will be discussed as well.

Retrieval of Snow Properties for Ku- and Ka-band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro/macro-physical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) duringthe winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

Retrieval of Snow Properties for Ku- and Ka-Band Dual-Frequency Radar

NASA Technical Reports Server (NTRS)

Liao, Liang; Meneghini, Robert; Tokay, Ali; Bliven, Larry F.

2016-01-01

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro-macrophysical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow-scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager Particle Image Probe (SVIPIP) during the winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia.

A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

1993-01-01

A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

Outcome of the third cloud retrieval evaluation workshop

NASA Astrophysics Data System (ADS)

Roebeling, Rob; Baum, Bryan; Bennartz, Ralf; Hamann, Ulrich; Heidinger, Andy; Thoss, Anke; Walther, Andi

2013-05-01

Accurate measurements of global distributions of cloud parameters and their diurnal, seasonal, and interannual variations are needed to improve understanding of the role of clouds in the weather and climate system, and to monitor their time-space variations. Cloud properties retrieved from satellite observations, such as cloud vertical placement, cloud water path and cloud particle size, play an important role for such studies. In order to give climate and weather researchers more confidence in the quality of these retrievals their validity needs to be determined and their error characteristics must be quantified. The purpose of the Cloud Retrieval Evaluation Workshop (CREW), held from 15-18 Nov. 2011 in Madison, Wisconsin, USA, is to enhance knowledge on state-of-art cloud properties retrievals from passive imaging satellites, and pave the path towards optimizing these retrievals for climate monitoring as well as for the analysis of cloud parameterizations in climate and weather models. CREW also seeks to observe and understand methods used to prepare daily and monthly cloud parameter climatologies. An important workshop component is discussion on results of the algorithm and sensor comparisons and validation studies. Hereto a common database with about 12 different cloud properties retrievals from passive imagers (MSG, MODIS, AVHRR, POLDER and/or AIRS), complemented with cloud measurements that serve as a reference (CLOUDSAT, CALIPSO, AMSU, MISR), was prepared for a number of "golden days". The passive imager cloud property retrievals were inter-compared and validated against Cloudsat, Calipso and AMSU observations. In our presentation we summarize the outcome of the inter-comparison and validation work done in the framework of CREW, and elaborate on reasons for observed differences. More in depth discussions were held on retrieval principles and validation, and utilization of cloud parameters for climate research. This was done in parallel breakout sessions on cloud vertical placement, cloud physical properties, and cloud climatologies. We present the recommendations of these sessions, propose a way forward to establish international partnerships on cloud research, and summarize actions defined to tailor CREW activities to missions of international programs, such as the Global Energy and Water Cycle Experiment (GEWEX) and Sustained, Co-Ordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM). Finally, attention is given to increase the traceability and uniformity of different longterm and homogeneous records of cloud parameters.

A Study of NetCDF as an Approach for High Performance Medical Image Storage

NASA Astrophysics Data System (ADS)

Magnus, Marcone; Coelho Prado, Thiago; von Wangenhein, Aldo; de Macedo, Douglas D. J.; Dantas, M. A. R.

2012-02-01

The spread of telemedicine systems increases every day. The systems and PACS based on DICOM images has become common. This rise reflects the need to develop new storage systems, more efficient and with lower computational costs. With this in mind, this article discusses a study for application in NetCDF data format as the basic platform for storage of DICOM images. The study case comparison adopts an ordinary database, the HDF5 and the NetCDF to storage the medical images. Empirical results, using a real set of images, indicate that the time to retrieve images from the NetCDF for large scale images has a higher latency compared to the other two methods. In addition, the latency is proportional to the file size, which represents a drawback to a telemedicine system that is characterized by a large amount of large image files.

Validating a Geographical Image Retrieval System.

ERIC Educational Resources Information Center

Zhu, Bin; Chen, Hsinchun

2000-01-01

Summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. Describes an experiment to validate the performance of this image retrieval system against that of human subjects by examining similarity analysis…

Asymmetric distances for binary embeddings.

PubMed

Gordo, Albert; Perronnin, Florent; Gong, Yunchao; Lazebnik, Svetlana

2014-01-01

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes that binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances that are applicable to a wide variety of embedding techniques including locality sensitive hashing (LSH), locality sensitive binary codes (LSBC), spectral hashing (SH), PCA embedding (PCAE), PCAE with random rotations (PCAE-RR), and PCAE with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.

[Security specifications for electronic medical records on the Internet].

PubMed

Mocanu, Mihai; Mocanu, Carmen

2007-01-01

The extension for the Web applications of the Electronic Medical Record seems both interesting and promising. Correlated with the expansion of Internet in our country, it allows the interconnection of physicians of different specialties and their collaboration for better treatment of patients. In this respect, the ophthalmologic medical applications consider the increased possibilities for monitoring chronic ocular diseases and for the identification of some elements for early diagnosis and risk factors supervision. We emphasize in this survey some possible solutions to the problems of interconnecting medical information systems to the Internet: the achievement of interoperability within medical organizations through the use of open standards, the automated input and processing for ocular imaging, the use of data reduction techniques in order to increase the speed of image retrieval in large databases, and, last but not least, the resolution of security and confidentiality problems in medical databases.

Web Mining for Web Image Retrieval.

ERIC Educational Resources Information Center

Chen, Zheng; Wenyin, Liu; Zhang, Feng; Li, Mingjing; Zhang, Hongjiang

2001-01-01

Presents a prototype system for image retrieval from the Internet using Web mining. Discusses the architecture of the Web image retrieval prototype; document space modeling; user log mining; and image retrieval experiments to evaluate the proposed system. (AEF)

apART: system for the acquisition, processing, archiving, and retrieval of digital images in an open, distributed imaging environment

NASA Astrophysics Data System (ADS)

Schneider, Uwe; Strack, Ruediger

1992-04-01

apART reflects the structure of an open, distributed environment. According to the general trend in the area of imaging, network-capable, general purpose workstations with capabilities of open system image communication and image input are used. Several heterogeneous components like CCD cameras, slide scanners, and image archives can be accessed. The system is driven by an object-oriented user interface where devices (image sources and destinations), operators (derived from a commercial image processing library), and images (of different data types) are managed and presented uniformly to the user. Browsing mechanisms are used to traverse devices, operators, and images. An audit trail mechanism is offered to record interactive operations on low-resolution image derivatives. These operations are processed off-line on the original image. Thus, the processing of extremely high-resolution raster images is possible, and the performance of resolution dependent operations is enhanced significantly during interaction. An object-oriented database system (APRIL), which can be browsed, is integrated into the system. Attribute retrieval is supported by the user interface. Other essential features of the system include: implementation on top of the X Window System (X11R4) and the OSF/Motif widget set; a SUN4 general purpose workstation, inclusive ethernet, magneto optical disc, etc., as the hardware platform for the user interface; complete graphical-interactive parametrization of all operators; support of different image interchange formats (GIF, TIFF, IIF, etc.); consideration of current IPI standard activities within ISO/IEC for further refinement and extensions.

Application and Effects of Linguistic Functions on Information Retrieval in a German Language Full-Text Database: Comparison between Retrieval in Abstract and Full Text.

ERIC Educational Resources Information Center

Tauchert, Wolfgang; And Others

1991-01-01

Describes the PADOK-II project in Germany, which was designed to give information on the effects of linguistic algorithms on retrieval in a full-text database, the German Patent Information System (GPI). Relevance assessments are discussed, statistical evaluations are described, and searches are compared for the full-text section versus the…

A Normative Database and Determinants of Lexical Retrieval for 186 Arabic Nouns: Effects of Psycholinguistic and Morpho-Syntactic Variables on Naming Latency

ERIC Educational Resources Information Center

Khwaileh, Tariq; Body, Richard; Herbert, Ruth

2014-01-01

Research into lexical retrieval requires pictorial stimuli standardised for key psycholinguistic variables. Such databases exist in a number of languages but not in Arabic. In addition there are few studies of the effects of psycholinguistic and morpho-syntactic variables on Arabic lexical retrieval. The current study identified a set of…

NATIONAL PESTICIDE INFORMATION RETRIEVAL SYSTEM (NPIRS)

EPA Science Inventory

The National Pesticide Information Retrieval System (NPIRS) is a collection of pesticide-related databases available through subscription to the Center for Environmental and Regulatory Information Systems, CERIS. The following is a summary of data found in the databases, data sou...

Strategies for medical data extraction and presentation part 2: creating a customizable context and user-specific patient reference database.

PubMed

Reiner, Bruce

2015-06-01

One of the greatest challenges facing healthcare professionals is the ability to directly and efficiently access relevant data from the patient's healthcare record at the point of care; specific to both the context of the task being performed and the specific needs and preferences of the individual end-user. In radiology practice, the relative inefficiency of imaging data organization and manual workflow requirements serves as an impediment to historical imaging data review. At the same time, clinical data retrieval is even more problematic due to the quality and quantity of data recorded at the time of order entry, along with the relative lack of information system integration. One approach to address these data deficiencies is to create a multi-disciplinary patient referenceable database which consists of high-priority, actionable data within the cumulative patient healthcare record; in which predefined criteria are used to categorize and classify imaging and clinical data in accordance with anatomy, technology, pathology, and time. The population of this referenceable database can be performed through a combination of manual and automated methods, with an additional step of data verification introduced for data quality control. Once created, these referenceable databases can be filtered at the point of care to provide context and user-specific data specific to the task being performed and individual end-user requirements.

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

NASA Technical Reports Server (NTRS)

Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

2011-01-01

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Structural texture similarity metrics for image analysis and retrieval.

PubMed

Zujovic, Jana; Pappas, Thrasyvoulos N; Neuhoff, David L

2013-07-01

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of "known-item search," the retrieval of textures that are "identical" to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.

The Retrieval of Aerosol Optical Thickness Using the MERIS Instrument

NASA Astrophysics Data System (ADS)

Mei, L.; Rozanov, V. V.; Vountas, M.; Burrows, J. P.; Levy, R. C.; Lotz, W.

2015-12-01

Retrieval of aerosol properties for satellite instruments without shortwave-IR spectral information, multi-viewing, polarization and/or high-temporal observation ability is a challenging problem for spaceborne aerosol remote sensing. However, space based instruments like the MEdium Resolution Imaging Spectrometer (MERIS) and the successor, Ocean and Land Colour Instrument (OLCI) with high calibration accuracy and high spatial resolution provide unique abilities for obtaining valuable aerosol information for a better understanding of the impact of aerosols on climate, which is still one of the largest uncertainties of global climate change evaluation. In this study, a new Aerosol Optical Thickness (AOT) retrieval algorithm (XBAER: eXtensible Bremen AErosol Retrieval) is presented. XBAER utilizes the global surface spectral library database for the determination of surface properties while the MODIS collection 6 aerosol type treatment is adapted for the aerosol type selection. In order to take the surface Bidirectional Reflectance Distribution Function (BRDF) effect into account for the MERIS reduce resolution (1km) retrieval, a modified Ross-Li mode is used. The AOT is determined in the algorithm using lookup tables including polarization created using Radiative Transfer Model SCIATRAN3.4, by minimizing the difference between atmospheric corrected surface reflectance with given AOT and the surface reflectance calculated from the spectral library. The global comparison with operational MODIS C6 product, Multi-angle Imaging SpectroRadiometer (MISR) product, Advanced Along-Track Scanning Radiometer (AATSR) aerosol product and the validation using AErosol RObotic NETwork (AERONET) show promising results. The current XBAER algorithm is only valid for aerosol remote sensing over land and a similar method will be extended to ocean later.

Hospital nurses' information retrieval behaviours in relation to evidence based nursing: a literature review.

PubMed

Alving, Berit Elisabeth; Christensen, Janne Buck; Thrysøe, Lars

2018-03-01

The purpose of this literature review is to provide an overview of the information retrieval behaviour of clinical nurses, in terms of the use of databases and other information resources and their frequency of use. Systematic searches carried out in five databases and handsearching were used to identify the studies from 2010 to 2016, with a populations, exposures and outcomes (PEO) search strategy, focusing on the question: In which databases or other information resources do hospital nurses search for evidence based information, and how often? Of 5272 titles retrieved based on the search strategy, only nine studies fulfilled the criteria for inclusion. The studies are from the United States, Canada, Taiwan and Nigeria. The results show that hospital nurses' primary choice of source for evidence based information is Google and peers, while bibliographic databases such as PubMed are secondary choices. Data on frequency are only included in four of the studies, and data are heterogenous. The reasons for choosing Google and peers are primarily lack of time; lack of information; lack of retrieval skills; or lack of training in database searching. Only a few studies are published on clinical nurses' retrieval behaviours, and more studies are needed from Europe and Australia. © 2018 Health Libraries Group.

Automation of PCXMC and ImPACT for NASA Astronaut Medical Imaging Dose and Risk Tracking

NASA Technical Reports Server (NTRS)

Bahadori, Amir; Picco, Charles; Flores-McLaughlin, John; Shavers, Mark; Semones, Edward

2011-01-01

To automate astronaut organ and effective dose calculations from occupational X-ray and computed tomography (CT) examinations incorporating PCXMC and ImPACT tools and to estimate the associated lifetime cancer risk per the National Council on Radiation Protection & Measurements (NCRP) using MATLAB(R). Methods: NASA follows guidance from the NCRP on its operational radiation safety program for astronauts. NCRP Report 142 recommends that astronauts be informed of the cancer risks from reported exposures to ionizing radiation from medical imaging. MATLAB(R) code was written to retrieve exam parameters for medical imaging procedures from a NASA database, calculate associated dose and risk, and return results to the database, using the Microsoft .NET Framework. This code interfaces with the PCXMC executable and emulates the ImPACT Excel spreadsheet to calculate organ doses from X-rays and CTs, respectively, eliminating the need to utilize the PCXMC graphical user interface (except for a few special cases) and the ImPACT spreadsheet. Results: Using MATLAB(R) code to interface with PCXMC and replicate ImPACT dose calculation allowed for rapid evaluation of multiple medical imaging exams. The user inputs the exam parameter data into the database and runs the code. Based on the imaging modality and input parameters, the organ doses are calculated. Output files are created for record, and organ doses, effective dose, and cancer risks associated with each exam are written to the database. Annual and post-flight exposure reports, which are used by the flight surgeon to brief the astronaut, are generated from the database. Conclusions: Automating PCXMC and ImPACT for evaluation of NASA astronaut medical imaging radiation procedures allowed for a traceable and rapid method for tracking projected cancer risks associated with over 12,000 exposures. This code will be used to evaluate future medical radiation exposures, and can easily be modified to accommodate changes to the risk calculation procedure.

An integrated content and metadata based retrieval system for art.

PubMed

Lewis, Paul H; Martinez, Kirk; Abas, Fazly Salleh; Fauzi, Mohammad Faizal Ahmad; Chan, Stephen C Y; Addis, Matthew J; Boniface, Mike J; Grimwood, Paul; Stevenson, Alison; Lahanier, Christian; Stevenson, James

2004-03-01

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

Materials properties numerical database system established and operational at CINDAS/Purdue University

NASA Technical Reports Server (NTRS)

Ho, C. Y.; Li, H. H.

1989-01-01

A computerized comprehensive numerical database system on the mechanical, thermophysical, electronic, electrical, magnetic, optical, and other properties of various types of technologically important materials such as metals, alloys, composites, dielectrics, polymers, and ceramics has been established and operational at the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) of Purdue University. This is an on-line, interactive, menu-driven, user-friendly database system. Users can easily search, retrieve, and manipulate the data from the database system without learning special query language, special commands, standardized names of materials, properties, variables, etc. It enables both the direct mode of search/retrieval of data for specified materials, properties, independent variables, etc., and the inverted mode of search/retrieval of candidate materials that meet a set of specified requirements (which is the computer-aided materials selection). It enables also tabular and graphical displays and on-line data manipulations such as units conversion, variables transformation, statistical analysis, etc., of the retrieved data. The development, content, accessibility, etc., of the database system are presented and discussed.

The ISO Data Archive and Interoperability with Other Archives

NASA Astrophysics Data System (ADS)

Salama, Alberto; Arviset, Christophe; Hernández, José; Dowson, John; Osuna, Pedro

The ESA's Infrared Space Observatory (ISO), an unprecedented observatory for infrared astronomy launched in November 1995, successfully made nearly 30,000 scientific observations in its 2.5-year mission. The ISO data can be retrieved from the ISO Data Archive, available at ISO Data Archive , and comprised of about 150,000 observations, including parallel and serendipity mode observations. A user-friendly Java interface permits queries to the database and data retrieval. The interface currently offers a wide variety of links to other archives, such as name resolution with NED and SIMBAD, access to electronic articles from ADS and CDS/VizieR, and access to IRAS data. In the past year development has been focused on improving the IDA interoperability with other astronomical archives, either by accessing other relevant archives or by providing direct access to the ISO data for external services. A mechanism of information transfer has been developed, allowing direct query to the IDA via a Java Server Page, returning quick look ISO images and relevant, observation-specific information embedded in an HTML page. This method has been used to link from the CDS/Vizier Data Centre and ADS, and work with IPAC to allow access to the ISO Archive from IRSA, including display capabilities of the observed sky regions onto other mission images, is in progress. Prospects for further links to and from other archives and databases are also addressed.

An Internet service for manipulating 3D models of human organs reconstructed from computer tomography and magnetic resonance imaging.

PubMed

Clapworthy, G; Krokos, M; Vasilonikolidakis, N

1997-11-01

Our paper describes an integrated methodology addressing the development of an Internet service for medical professionals, medical students and generally, people interested in medicine. The service (currently developed in the framework of IAEVA, a Telematics Application Programme project of the European Union), incorporates a mechanism for retrieving from a relational database (reference library) 3D volumetric models of human organs reconstructed from computer tomography (CT) and/or magnetic resonance imaging (MRI). Retrieval is implemented in a way transparent to the actual physical location of the database. Prospective users are provided with a Solid Object Viewer that offers them manipulation (rotation, zooming, dissection etc.) of 3D volumetric models. The service constitutes an excellent foundation of understanding for medical professionals/students and a mechanism for broad and rapid dissemination of information related to particular pathological conditions; although pathological conditions of the knee and skin are supported currently, our methodology allows easy service extension into other human organs ultimately covering the entire human body. The service accepts most Internet browsers and supports MS-Windows 32 platforms; no graphics accelerators or any specialised hardware are necessary, thereby allowing service availability to the widest possible audience. Nevertheless, the service operates in near real-time not only over high speed expensive network lines but also over low/medium network connections.

Retrieval of cloud properties from POLDER-3 data using the neural network approach

NASA Astrophysics Data System (ADS)

Di Noia, A.; Hasekamp, O. P.

2017-12-01

Satellite multi-angle spectroplarimetry is a useful technique for observing the microphysical properties of clouds and aerosols. Most of the algorithms for the retrieval of cloud and aerosol properties from satellite measurements require multiple calls to radiative transfer models, which make the retrieval computationally expensive. A traditional alternative to these schemes is represented by lookup-tables (LUTs), where the retrieval is performed by choosing, within a predefined database of combinations of clouds or aerosol properties, the combination that best fits the measurements. LUT retrievals are quicker than full-physics, iterative retrievals, but their accuracy is limited by the number of entries stored in the LUT. Another retrieval method capable of producing very quick retrievals without a big sacrifice in accuracy is the neural network method. Neural network methods are routinely applied to several types of satellite measurements, but their application to multi-angle spectropolarimetric data is still in its early stage, because of the difficulty of accounting for the angular variability of the measurements in the training process. We have recently developed a neural network scheme for the retrieval of cloud properties from POLDER-3 data. The neural network retrieval is trained using synthetic measurements performed for realistic combinations of cloud properties and measurement angles, and is able to process an entire orbit in about 20 seconds. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products during one year show encouraging retrieval performance for cloud optical thickness and effective radius. A discussion of the setup of the neural network and of the validation results will be the main topic of our presentation.

Desktop Access to Full-Text NACA and NASA Reports: Systems Developed by NASA Langley Technical Library

NASA Technical Reports Server (NTRS)

Ambur, Manjula Y.; Adams, David L.; Trinidad, P. Paul

1997-01-01

NASA Langley Technical Library has been involved in developing systems for full-text information delivery of NACA/NASA technical reports since 1991. This paper will describe the two prototypes it has developed and the present production system configuration. The prototype systems are a NACA CD-ROM of thirty-three classic paper NACA reports and a network-based Full-text Electronic Reports Documents System (FEDS) constructed from both paper and electronic formats of NACA and NASA reports. The production system is the DigiDoc System (DIGItal Documents) presently being developed based on the experiences gained from the two prototypes. DigiDoc configuration integrates the on-line catalog database World Wide Web interface and PDF technology to provide a powerful and flexible search and retrieval system. It describes in detail significant achievements and lessons learned in terms of data conversion, storage technologies, full-text searching and retrieval, and image databases. The conclusions from the experiences of digitization and full- text access and future plans for DigiDoc system implementation are discussed.

A novel image retrieval algorithm based on PHOG and LSH

NASA Astrophysics Data System (ADS)

Wu, Hongliang; Wu, Weimin; Peng, Jiajin; Zhang, Junyuan

2017-08-01

PHOG can describe the local shape of the image and its relationship between the spaces. The using of PHOG algorithm to extract image features in image recognition and retrieval and other aspects have achieved good results. In recent years, locality sensitive hashing (LSH) algorithm has been superior to large-scale data in solving near-nearest neighbor problems compared with traditional algorithms. This paper presents a novel image retrieval algorithm based on PHOG and LSH. First, we use PHOG to extract the feature vector of the image, then use L different LSH hash table to reduce the dimension of PHOG texture to index values and map to different bucket, and finally extract the corresponding value of the image in the bucket for second image retrieval using Manhattan distance. This algorithm can adapt to the massive image retrieval, which ensures the high accuracy of the image retrieval and reduces the time complexity of the retrieval. This algorithm is of great significance.

How I do it: a practical database management system to assist clinical research teams with data collection, organization, and reporting.

PubMed

Lee, Howard; Chapiro, Julius; Schernthaner, Rüdiger; Duran, Rafael; Wang, Zhijun; Gorodetski, Boris; Geschwind, Jean-François; Lin, MingDe

2015-04-01

The objective of this study was to demonstrate that an intra-arterial liver therapy clinical research database system is a more workflow efficient and robust tool for clinical research than a spreadsheet storage system. The database system could be used to generate clinical research study populations easily with custom search and retrieval criteria. A questionnaire was designed and distributed to 21 board-certified radiologists to assess current data storage problems and clinician reception to a database management system. Based on the questionnaire findings, a customized database and user interface system were created to perform automatic calculations of clinical scores including staging systems such as the Child-Pugh and Barcelona Clinic Liver Cancer, and facilitates data input and output. Questionnaire participants were favorable to a database system. The interface retrieved study-relevant data accurately and effectively. The database effectively produced easy-to-read study-specific patient populations with custom-defined inclusion/exclusion criteria. The database management system is workflow efficient and robust in retrieving, storing, and analyzing data. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Method and system for the diagnosis of disease using retinal image content and an archive of diagnosed human patient data

DOEpatents

Tobin, Kenneth W; Karnowski, Thomas P; Chaum, Edward

2013-08-06

A method for diagnosing diseases having retinal manifestations including retinal pathologies includes the steps of providing a CBIR system including an archive of stored digital retinal photography images and diagnosed patient data corresponding to the retinal photography images, the stored images each indexed in a CBIR database using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the stored images. A query image of the retina of a patient is obtained. Using image processing, regions or structures in the query image are identified. The regions or structures are then described using the plurality of feature vectors. At least one relevant stored image from the archive based on similarity to the regions or structures is retrieved, and an eye disease or a disease having retinal manifestations in the patient is diagnosed based on the diagnosed patient data associated with the relevant stored image(s).

Application of new type of distributed multimedia databases to networked electronic museum

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1999-01-01

Recently, various kinds of multimedia application systems have actively been developed based on the achievement of advanced high sped communication networks, computer processing technologies, and digital contents-handling technologies. Under this background, this paper proposed a new distributed multimedia database system which can effectively perform a new function of cooperative retrieval among distributed databases. The proposed system introduces a new concept of 'Retrieval manager' which functions as an intelligent controller so that the user can recognize a set of distributed databases as one logical database. The logical database dynamically generates and performs a preferred combination of retrieving parameters on the basis of both directory data and the system environment. Moreover, a concept of 'domain' is defined in the system as a managing unit of retrieval. The retrieval can effectively be performed by cooperation of processing among multiple domains. Communication language and protocols are also defined in the system. These are used in every action for communications in the system. A language interpreter in each machine translates a communication language into an internal language used in each machine. Using the language interpreter, internal processing, such internal modules as DBMS and user interface modules can freely be selected. A concept of 'content-set' is also introduced. A content-set is defined as a package of contents. Contents in the content-set are related to each other. The system handles a content-set as one object. The user terminal can effectively control the displaying of retrieved contents, referring to data indicating the relation of the contents in the content- set. In order to verify the function of the proposed system, a networked electronic museum was experimentally built. The results of this experiment indicate that the proposed system can effectively retrieve the objective contents under the control to a number of distributed domains. The result also indicate that the system can effectively work even if the system becomes large.

Interactive Exploration for Continuously Expanding Neuron Databases.

PubMed

Li, Zhongyu; Metaxas, Dimitris N; Lu, Aidong; Zhang, Shaoting

2017-02-15

This paper proposes a novel framework to help biologists explore and analyze neurons based on retrieval of data from neuron morphological databases. In recent years, the continuously expanding neuron databases provide a rich source of information to associate neuronal morphologies with their functional properties. We design a coarse-to-fine framework for efficient and effective data retrieval from large-scale neuron databases. In the coarse-level, for efficiency in large-scale, we employ a binary coding method to compress morphological features into binary codes of tens of bits. Short binary codes allow for real-time similarity searching in Hamming space. Because the neuron databases are continuously expanding, it is inefficient to re-train the binary coding model from scratch when adding new neurons. To solve this problem, we extend binary coding with online updating schemes, which only considers the newly added neurons and update the model on-the-fly, without accessing the whole neuron databases. In the fine-grained level, we introduce domain experts/users in the framework, which can give relevance feedback for the binary coding based retrieval results. This interactive strategy can improve the retrieval performance through re-ranking the above coarse results, where we design a new similarity measure and take the feedback into account. Our framework is validated on more than 17,000 neuron cells, showing promising retrieval accuracy and efficiency. Moreover, we demonstrate its use case in assisting biologists to identify and explore unknown neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

Document image retrieval through word shape coding.

PubMed

Lu, Shijian; Li, Linlin; Tan, Chew Lim

2008-11-01

This paper presents a document retrieval technique that is capable of searching document images without OCR (optical character recognition). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features including character ascenders/descenders, character holes, and character water reservoirs. With the annotated word shape codes, document images can be retrieved by either query keywords or a query document image. Experimental results show that the proposed document image retrieval technique is fast, efficient, and tolerant to various types of document degradation.

Impact of line parameter database, continuum absorption, full grind configuration, and L1B update on GOSAT TIR methane retrieval

NASA Astrophysics Data System (ADS)

Yamada, A.; Saitoh, N.; Nonogaki, R.; Imasu, R.; Shiomi, K.; Kuze, A.

2016-12-01

The thermal infrared (TIR) band of Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT) observes CH4 profile at wavenumber range from 1210 cm-1 to 1360 cm-1 including CH4 ν4 band. The current retrieval algorithm (V1.0) uses LBLRTM V12.1 with AER V3.1 line database to calculate optical depth. LBLRTM V12.1 include MT_CKD 2.5.2 model to calculate continuum absorption. The continuum absorption has large uncertainty, especially temperature dependent coefficient, between BPS model and MT_CKD model in the wavenumber region of 1210-1250 cm-1(Paynter and Ramaswamy, 2014). The purpose of this study is to assess the impact on CH4 retrieval from the line parameter databases and the uncertainty of continuum absorption. We used AER v1.0 database, HITRAN2004 database, HITRAN2008 database, AER V3.2 database, and HITRAN2012 database (Rothman et al. 2005, 2009, and 2013. Clough et al., 2005). AER V1.0 database is based on HITRAN2000. The CH4 line parameters of AER V3.1 and V3.2 databases are developed from HITRAN2008 including updates until May 2009 with line mixing parameters. We compared the retrieved CH4 with the HIPPO CH4 observation (Wofsy et al., 2012). The difference of AER V3.2 was the smallest and 24.1 ± 45.9 ppbv. The differences of AER V1.0, HITRAN2004, HITRAN2008, and HITRAN2012 were 35.6 ± 46.5 ppbv, 37.6 ± 46.3 ppbv, 32.1 ± 46.1 ppbv, and 35.2 ± 46.0 ppbv, respectively. Compare AER V3.2 case to HITRAN2008 case, the line coupling effect reduced difference by 8.0 ppbv. Median values of Residual difference from HITRAN2008 to AER V1.0, HITRAN2004, AER V3.2, and HITRAN2012 were 0.6 K, 0.1 K, -0.08 K, and 0.08 K, respectively, while median values of transmittance difference were less than 0.0003 and transmittance differences have small wavenumber dependence. We also discuss the retrieval error from the uncertainty of the continuum absorption, the test of full grid configuration for retrieval, and the retrieval results using GOSAT TIR L1B V203203, which are sample products to evaluate the next level 1B algorithm.

Reversible watermarking for knowledge digest embedding and reliability control in medical images.

PubMed

Coatrieux, Gouenou; Le Guillou, Clara; Cauvin, Jean-Michel; Roux, Christian

2009-03-01

To improve medical image sharing in applications such as e-learning or remote diagnosis aid, we propose to make the image more usable by watermarking it with a digest of its associated knowledge. The aim of such a knowledge digest (KD) is for it to be used for retrieving similar images with either the same findings or differential diagnoses. It summarizes the symbolic descriptions of the image, the symbolic descriptions of the findings semiology, and the similarity rules that contribute to balancing the importance of previous descriptors when comparing images. Instead of modifying the image file format by adding some extra header information, watermarking is used to embed the KD in the pixel gray-level values of the corresponding images. When shared through open networks, watermarking also helps to convey reliability proofs (integrity and authenticity) of an image and its KD. The interest of these new image functionalities is illustrated in the updating of the distributed users' databases within the framework of an e-learning application demonstrator of endoscopic semiology.

Automated Construction of Coverage Catalogues of Aster Satellite Image for Urban Areas of the World

NASA Astrophysics Data System (ADS)

Miyazaki, H.; Iwao, K.; Shibasaki, R.

2012-07-01

We developed an algorithm to determine a combination of satellite images according to observation extent and image quality. The algorithm was for testing necessity for completing coverage of the search extent. The tests excluded unnecessary images with low quality and preserve necessary images with good quality. The search conditions of the satellite images could be extended, indicating the catalogue could be constructed with specified periods required for time series analysis. We applied the method to a database of metadata of ASTER satellite images archived in GEO Grid of National Institute of Advanced Industrial Science and Technology (AIST), Japan. As indexes of populated places with geographical coordinates, we used a database of 3372 populated place of more than 0.1 million populations retrieved from GRUMP Settlement Points, a global gazetteer of cities, which has geographical names of populated places associated with geographical coordinates and population data. From the coordinates of populated places, 3372 extents were generated with radiuses of 30 km, a half of swath of ASTER satellite images. By merging extents overlapping each other, they were assembled into 2214 extents. As a result, we acquired combinations of good quality for 1244 extents, those of low quality for 96 extents, incomplete combinations for 611 extents. Further improvements would be expected by introducing pixel-based cloud assessment and pixel value correction over seasonal variations.

The use of a personal digital assistant for wireless entry of data into a database via the Internet.

PubMed

Fowler, D L; Hogle, N J; Martini, F; Roh, M S

2002-01-01

Researchers typically record data on a worksheet and at some later time enter it into the database. Wireless data entry and retrieval using a personal digital assistant (PDA) at the site of patient contact can simplify this process and improve efficiency. A surgeon and a nurse coordinator provided the content for the database. The computer programmer created the database, placed the pages of the database on the PDA screen, and researched and installed security measures. Designing the database took 6 months. Meeting Health Insurance Portability and Accountability Act of 1996 (HIPAA) requirements for patient confidentiality, satisfying institutional Information Services requirements, and ensuring connectivity required an additional 8 months before the functional system was complete. It is now possible to achieve wireless entry and retrieval of data using a PDA. Potential advantages include collection and entry of data at the same time, easy entry of data from multiple sites, and retrieval of data at the patient's bedside.

Object instance recognition using motion cues and instance specific appearance models

NASA Astrophysics Data System (ADS)

Schumann, Arne

2014-03-01

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

A Simulation Model Of A Picture Archival And Communication System

NASA Astrophysics Data System (ADS)

D'Silva, Vijay; Perros, Harry; Stockbridge, Chris

1988-06-01

A PACS architecture was simulated to quantify its performance. The model consisted of reading stations, acquisition nodes, communication links, a database management system, and a storage system consisting of magnetic and optical disks. Two levels of storage were simulated, a high-speed magnetic disk system for short term storage, and optical disk jukeboxes for long term storage. The communications link was a single bus via which image data were requested and delivered. Real input data to the simulation model were obtained from surveys of radiology procedures (Bowman Gray School of Medicine). From these the following inputs were calculated: - the size of short term storage necessary - the amount of long term storage required - the frequency of access of each store, and - the distribution of the number of films requested per diagnosis. The performance measures obtained were - the mean retrieval time for an image, - mean queue lengths, and - the utilization of each device. Parametric analysis was done for - the bus speed, - the packet size for the communications link, - the record size on the magnetic disk, - compression ratio, - influx of new images, - DBMS time, and - diagnosis think times. Plots give the optimum values for those values of input speed and device performance which are sufficient to achieve subsecond image retrieval times

Informatics in radiology: RADTF: a semantic search-enabled, natural language processor-generated radiology teaching file.

PubMed

Do, Bao H; Wu, Andrew; Biswal, Sandip; Kamaya, Aya; Rubin, Daniel L

2010-11-01

Storing and retrieving radiology cases is an important activity for education and clinical research, but this process can be time-consuming. In the process of structuring reports and images into organized teaching files, incidental pathologic conditions not pertinent to the primary teaching point can be omitted, as when a user saves images of an aortic dissection case but disregards the incidental osteoid osteoma. An alternate strategy for identifying teaching cases is text search of reports in radiology information systems (RIS), but retrieved reports are unstructured, teaching-related content is not highlighted, and patient identifying information is not removed. Furthermore, searching unstructured reports requires sophisticated retrieval methods to achieve useful results. An open-source, RadLex(®)-compatible teaching file solution called RADTF, which uses natural language processing (NLP) methods to process radiology reports, was developed to create a searchable teaching resource from the RIS and the picture archiving and communication system (PACS). The NLP system extracts and de-identifies teaching-relevant statements from full reports to generate a stand-alone database, thus converting existing RIS archives into an on-demand source of teaching material. Using RADTF, the authors generated a semantic search-enabled, Web-based radiology archive containing over 700,000 cases with millions of images. RADTF combines a compact representation of the teaching-relevant content in radiology reports and a versatile search engine with the scale of the entire RIS-PACS collection of case material. ©RSNA, 2010

Java-based browsing, visualization and processing of heterogeneous medical data from remote repositories.

PubMed

Masseroli, M; Bonacina, S; Pinciroli, F

2004-01-01

The actual development of distributed information technologies and Java programming enables employing them also in the medical arena to support the retrieval, integration and evaluation of heterogeneous data and multimodal images in a web browser environment. With this aim, we used them to implement a client-server architecture based on software agents. The client side is a Java applet running in a web browser and providing a friendly medical user interface to browse and visualize different patient and medical test data, integrating them properly. The server side manages secure connections and queries to heterogeneous remote databases and file systems containing patient personal and clinical data. Based on the Java Advanced Imaging API, processing and analysis tools were developed to support the evaluation of remotely retrieved bioimages through the quantification of their features in different regions of interest. The Java platform-independence allows the centralized management of the implemented prototype and its deployment to each site where an intranet or internet connection is available. Giving healthcare providers effective support for comprehensively browsing, visualizing and evaluating medical images and records located in different remote repositories, the developed prototype can represent an important aid in providing more efficient diagnoses and medical treatments.

Studies in the Use of Color for Image Indexing and Retrieval in Specialized Databases

DTIC Science & Technology

2001-09-01

mA| x ��T�{� qR�&o x­o z{qÍ�h �[� » Ï ª h�u T=� q S ¸ T=V mAq®Ë{� Ë U ��¸[z{q¦Í�h �[� »çÏ ª hyu T=� q�tA|��{|Kq��Âorx � sKx6�#� X q T�X xmvx=sz{tAq

Ultrafast Target Recognition via Super-Parallel Holograph Based Correlator, RAM and Associative Memory

DTIC Science & Technology

2008-03-11

JTC) 2𔃾 based on a dynamic material answers the challenge of fast correlation with large databases. Images retrieved from the SPHRAM and used as the...transform (JTC) and matched spatial filter or VanderLugt ( VLC ) correlators, either of which can be implemented in real-time by degenerate four wave-mixing in...proposed system, consisting of the SPHROM coupled with a shift-invariant real-time VLC . The correlation is performed in the VLC architecture to

Data-Base Software For Tracking Technological Developments

NASA Technical Reports Server (NTRS)

Aliberti, James A.; Wright, Simon; Monteith, Steve K.

1996-01-01

Technology Tracking System (TechTracS) computer program developed for use in storing and retrieving information on technology and related patent information developed under auspices of NASA Headquarters and NASA's field centers. Contents of data base include multiple scanned still images and quick-time movies as well as text. TechTracS includes word-processing, report-editing, chart-and-graph-editing, and search-editing subprograms. Extensive keyword searching capabilities enable rapid location of technologies, innovators, and companies. System performs routine functions automatically and serves multiple users.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.

PubMed

Wei, Xiu-Shen; Luo, Jian-Hao; Wu, Jianxin; Zhou, Zhi-Hua

2017-06-01

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

NASA Astrophysics Data System (ADS)

QingJie, Wei; WenBin, Wang

2017-06-01

In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval

Combining textual and visual information for image retrieval in the medical domain.

PubMed

Gkoufas, Yiannis; Morou, Anna; Kalamboukis, Theodore

2011-01-01

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP).

Indexing and retrieval of MPEG compressed video

NASA Astrophysics Data System (ADS)

Kobla, Vikrant; Doermann, David S.

1998-04-01

To keep pace with the increased popularity of digital video as an archival medium, the development of techniques for fast and efficient analysis of ideo streams is essential. In particular, solutions to the problems of storing, indexing, browsing, and retrieving video data from large multimedia databases are necessary to a low access to these collections. Given that video is often stored efficiently in a compressed format, the costly overhead of decompression can be reduced by analyzing the compressed representation directly. In earlier work, we presented compressed domain parsing techniques which identified shots, subshots, and scenes. In this article, we present efficient key frame selection, feature extraction, indexing, and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame type independent representation which normalizes spatial and temporal features including frame type, frame size, macroblock encoding, and motion compensation vectors. Features for indexing are derived directly from this representation and mapped to a low- dimensional space where they can be accessed using standard database techniques. Spatial information is used as primary index into the database and temporal information is used to rank retrieved clips and enhance the robustness of the system. The techniques presented enable efficient indexing, querying, and retrieval of compressed video as demonstrated by our system which typically takes a fraction of a second to retrieve similar video scenes from a database, with over 95 percent recall.

SLIMMER--A UNIX System-Based Information Retrieval System.

ERIC Educational Resources Information Center

Waldstein, Robert K.

1988-01-01

Describes an information retrieval system developed at Bell Laboratories to create and maintain a variety of different but interrelated databases, and to provide controlled access to these databases. The components discussed include the interfaces, indexing rules, display languages, response time, and updating procedures of the system. (6 notes…

Outcome of the Third Cloud Retrieval Evaluation Workshop

NASA Astrophysics Data System (ADS)

Roebeling, R.; Baum, B.; Bennartz, R.; Hamann, U.; Heidinger, A.; Thoss, A.; Walther, A.

2012-04-01

Accurate measurements of global distributions of cloud parameters and their diurnal, seasonal, and inter-annual variations are needed to improve the understanding of the role of clouds in the weather and climate system, and to monitor their time-space variations. Cloud properties retrieved from satellite observations, such as cloud vertical placement, cloud water path and cloud particle size, play an important role such studies. In order to give climate and weather researchers more confidence in the quality of these retrievals their validity needs to be determined and their error characteristics need to be quantified. The purpose of the Cloud Retrieval Evaluation Workshop (CREW), which was held from 15-18 November 2011 in Madison, Wisconsin, USA, is to enhance our knowledge on state-of-art cloud properties retrievals from passive imaging satellites, and pave the path towards optimising these retrievals for climate monitoring as well as for the analysis of cloud parameterizations in climate and weather models. CREW also seeks to observe and understand methods that are used to prepare daily and monthly cloud parameter climatologies. An important component of the workshop is the discussion on the results of the algorithm and sensor comparisons and validation studies. Hereto a common database with about 12 different cloud properties retrievals from passive imagers (MSG, MODIS, AVHRR, POLDER and/or AIRS), complemented with cloud measurements that serve as a reference (CLOUDSAT, CALIPSO, AMSU, MISR), was prepared for a number of "golden days". The passive imager cloud property retrievals were inter-compared and validated against Cloudsat, Calipso and AMSU observations. In our presentation we will summarize the outcome of the inter-comparison and validation work done in the framework of CREW, and elaborate on the reasons for the observed differences. More in depth discussions were held on retrieval principles and validation, and the utilization of cloud parameters for climate research. This was done in parallel breakout sessions on cloud vertical placement; cloud physical properties, and cloud climatologies. We will present the recommendations of these sessions, propose a way forward to establish international partnerships on cloud research, and summarize the actions defined to tailor the CREW activities to missions of international programs, such as the Global Energy and Water Cycle Experiment (GEWEX) and Sustained, Co-Ordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM). Finally, attention will be given to increase the traceability and uniformity of different long-term and homogeneous records of cloud parameters.

A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

PubMed

Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

2010-01-01

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.

The CREW intercomparison of SEVIRI cloud retrievals

NASA Astrophysics Data System (ADS)

Hamann, U.; Walther, A.; Bennartz, R.; Thoss, A.; Meirink, J. M.; Roebeling, R.

2012-12-01

About 70% of the earth's surface is covered with clouds. They strongly influence the radiation balance and the water cycle of the earth. Hence the detailed monitoring of cloud properties - such as cloud fraction, cloud top temperature, cloud particle size, and cloud water path - is important to understand the role of clouds in the weather and the climate system. The remote sensing with passive sensors is an essential mean for the global observation of the cloud parameters, but is nevertheless challenging. This presentation focuses on the inter-comparison and validation of cloud physical properties retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard METEOSAT. For this study we use retrievals from 12 state-of-art algorithms (Eumetsat, KNMI, NASA Langley, NASA Goddard, University Madison/Wisconsin, DWD, DLR, Meteo-France, KMI, FU Berlin, UK MetOffice) that are made available through the common database of the CREW (Cloud Retrieval Evaluation Working) group. Cloud detection, cloud top phase, height, and temperature, as well as optical properties and water path are validated with CLOUDSAT, CALIPSO, MISR, and AMSR-E measurements. Special emphasis is given to challenging retrieval conditions. Semi-transparent clouds over the earth's surface or another cloud layer modify the measured brightness temperature and increase the retrieval uncertainty. The consideration of the three-dimensional radiative effects is especially important for large viewing angles and broken cloud fields. Aerosols might be misclassified as cloud and may increase the retrieval uncertainty, too. Due to the availability of the high number of sophisticated retrieval datasets, the advantages of different retrieval approaches can be examined and suggestions for future retrieval developments can be made. We like to thank Eumetsat for sponsoring the CREW project including this work.nstitutes that participate in the CREW project.

Prospective memory and aging: evidence for preserved spontaneous retrieval with exact but not related cues.

PubMed

Mullet, Hillary G; Scullin, Michael K; Hess, Theodore J; Scullin, Rachel B; Arnold, Kathleen M; Einstein, Gilles O

2013-12-01

We examined whether normal aging spares or compromises cue-driven spontaneous retrieval processes that support prospective remembering. In Experiment 1, young and older adults performed prospective-memory tasks that required either strategic monitoring processes for retrieval (nonfocal) or for which participants relied on spontaneous retrieval processes (focal). We found age differences for nonfocal, but not focal, prospective-memory performance. Experiments 2 and 3 used an intention-interference paradigm in which participants were asked to perform a prospective-memory task (e.g., press "Q" when the word money appears) in the context of an image-rating task and were then told to suspend their prospective-memory intention until after completing an intervening lexical-decision task. During the lexical-decision task, we presented the exact prospective-memory cue (e.g., money; Experiments 2 and 3) or a semantically related lure (e.g., wallet; Experiment 3), and we inferred spontaneous retrieval from slowed lexical-decision responses to these items relative to matched control items. Young and older adults showed significant slowing when the exact prospective-memory cue was presented. Only young adults, however, showed significant slowing to the semantically related lure items. Collectively, these results partially support the multiprocess theory prediction that aging spares spontaneous retrieval processes. Spontaneous retrieval processes may become less sensitive with aging, such that older adults are less likely to respond to cues that do not exactly match their encoded targets. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Automatic gang graffiti recognition and interpretation

NASA Astrophysics Data System (ADS)

Parra, Albert; Boutin, Mireille; Delp, Edward J.

2017-09-01

One of the roles of emergency first responders (e.g., police and fire departments) is to prevent and protect against events that can jeopardize the safety and well-being of a community. In the case of criminal gang activity, tools are needed for finding, documenting, and taking the necessary actions to mitigate the problem or issue. We describe an integrated mobile-based system capable of using location-based services, combined with image analysis, to track and analyze gang activity through the acquisition, indexing, and recognition of gang graffiti images. This approach uses image analysis methods for color recognition, image segmentation, and image retrieval and classification. A database of gang graffiti images is described that includes not only the images but also metadata related to the images, such as date and time, geoposition, gang, gang member, colors, and symbols. The user can then query the data in a useful manner. We have implemented these features both as applications for Android and iOS hand-held devices and as a web-based interface.

A high-performance spatial database based approach for pathology imaging algorithm evaluation

PubMed Central

Wang, Fusheng; Kong, Jun; Gao, Jingjing; Cooper, Lee A.D.; Kurc, Tahsin; Zhou, Zhengwen; Adler, David; Vergara-Niedermayr, Cristobal; Katigbak, Bryan; Brat, Daniel J.; Saltz, Joel H.

2013-01-01

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation. PMID:23599905

Scalable ranked retrieval using document images

NASA Astrophysics Data System (ADS)

Jain, Rajiv; Oard, Douglas W.; Doermann, David

2013-12-01

Despite the explosion of text on the Internet, hard copy documents that have been scanned as images still play a significant role for some tasks. The best method to perform ranked retrieval on a large corpus of document images, however, remains an open research question. The most common approach has been to perform text retrieval using terms generated by optical character recognition. This paper, by contrast, examines whether a scalable segmentation-free image retrieval algorithm, which matches sub-images containing text or graphical objects, can provide additional benefit in satisfying a user's information needs on a large, real world dataset. Results on 7 million scanned pages from the CDIP v1.0 test collection show that content based image retrieval finds a substantial number of documents that text retrieval misses, and that when used as a basis for relevance feedback can yield improvements in retrieval effectiveness.

JANE, A new information retrieval system for the Radiation Shielding Information Center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trubey, D.K.

A new information storage and retrieval system has been developed for the Radiation Shielding Information Center (RSIC) at Oak Ridge National Laboratory to replace mainframe systems that have become obsolete. The database contains citations and abstracts of literature which were selected by RSIC analysts and indexed with terms from a controlled vocabulary. The database, begun in 1963, has been maintained continuously since that time. The new system, called JANE, incorporates automatic indexing techniques and on-line retrieval using the RSIC Data General Eclipse MV/4000 minicomputer, Automatic indexing and retrieval techniques based on fuzzy-set theory allow the presentation of results in ordermore » of Retrieval Status Value. The fuzzy-set membership function depends on term frequency in the titles and abstracts and on Term Discrimination Values which indicate the resolving power of the individual terms. These values are determined by the Cover Coefficient method. The use of a commercial database base to store and retrieve the indexing information permits rapid retrieval of the stored documents. Comparisons of the new and presently-used systems for actual searches of the literature indicate that it is practical to replace the mainframe systems with a minicomputer system similar to the present version of JANE. 18 refs., 10 figs.« less

Constraining the Structure of Hot Jupiter Atmospheres Using a Hybrid Version of the NEMESIS Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Badhan, Mahmuda A.; Mandell, Avi M.; Hesman, Brigette; Nixon, Conor; Deming, Drake; Irwin, Patrick; Barstow, Joanna; Garland, Ryan

2015-11-01

Understanding the formation environments and evolution scenarios of planets in nearby planetary systems requires robust measures for constraining their atmospheric physical properties. Here we have utilized a combination of two different parameter retrieval approaches, Optimal Estimation and Markov Chain Monte Carlo, as part of the well-validated NEMESIS atmospheric retrieval code, to infer a range of temperature profiles and molecular abundances of H2O, CO2, CH4 and CO from available dayside thermal emission observations of several hot-Jupiter candidates. In order to keep the number of parameters low and henceforth retrieve more plausible profile shapes, we have used a parametrized form of the temperature profile based upon an analytic radiative equilibrium derivation in Guillot et al. 2010 (Line et al. 2012, 2014). We show retrieval results on published spectroscopic and photometric data from both the Hubble Space Telescope and Spitzer missions, and compare them with simulations from the upcoming JWST mission. In addition, since NEMESIS utilizes correlated distribution of absorption coefficients (k-distribution) amongst atmospheric layers to compute these models, updates to spectroscopic databases can impact retrievals quite significantly for such high-temperature atmospheres. As high-temperature line databases are continually being improved, we also compare retrievals between old and newer databases.

Broadband Phase Retrieval for Image-Based Wavefront Sensing

NASA Technical Reports Server (NTRS)

Dean, Bruce H.

2007-01-01

A focus-diverse phase-retrieval algorithm has been shown to perform adequately for the purpose of image-based wavefront sensing when (1) broadband light (typically spanning the visible spectrum) is used in forming the images by use of an optical system under test and (2) the assumption of monochromaticity is applied to the broadband image data. Heretofore, it had been assumed that in order to obtain adequate performance, it is necessary to use narrowband or monochromatic light. Some background information, including definitions of terms and a brief description of pertinent aspects of image-based phase retrieval, is prerequisite to a meaningful summary of the present development. Phase retrieval is a general term used in optics to denote estimation of optical imperfections or aberrations of an optical system under test. The term image-based wavefront sensing refers to a general class of algorithms that recover optical phase information, and phase-retrieval algorithms constitute a subset of this class. In phase retrieval, one utilizes the measured response of the optical system under test to produce a phase estimate. The optical response of the system is defined as the image of a point-source object, which could be a star or a laboratory point source. The phase-retrieval problem is characterized as image-based in the sense that a charge-coupled-device camera, preferably of scientific imaging quality, is used to collect image data where the optical system would normally form an image. In a variant of phase retrieval, denoted phase-diverse phase retrieval [which can include focus-diverse phase retrieval (in which various defocus planes are used)], an additional known aberration (or an equivalent diversity function) is superimposed as an aid in estimating unknown aberrations by use of an image-based wavefront-sensing algorithm. Image-based phase-retrieval differs from such other wavefront-sensing methods, such as interferometry, shearing interferometry, curvature wavefront sensing, and Shack-Hartmann sensing, all of which entail disadvantages in comparison with image-based methods. The main disadvantages of these non-image based methods are complexity of test equipment and the need for a wavefront reference.

Snow cover retrieval over Rhone and Po river basins from MODIS optical satellite data (2000-2009).

NASA Astrophysics Data System (ADS)

Dedieu, Jean-Pierre, ,, Dr.; Boos, Alain; Kiage, Wiliam; Pellegrini, Matteo

2010-05-01

Estimation of the Snow Covered Area (SCA) is an important issue for meteorological application and hydrological modeling of runoff. With spectral bands in the visible, near and middle infrared, the MODIS optical satellite sensor can be used to detect snow cover because of large differences between reflectance from snow covered and snow free surfaces. At the same time, it allows separation between snow and clouds. Moreover, the sensor provides a daily coverage of large areas (2,500 km range). However, as the pixel size is 500m x 500m, a MODIS pixel may be partially covered by snow, particularly in Alpine areas, where snow may not be present in valleys lying at lower altitudes. Also, variation of reflectance due to differential sunlit effects as a function of slope and aspect, as well as bidirectional effects may be present in images. Nevertheless, it is possible to estimate snow cover at the Sub-Pixel level with a relatively good accuracy and with very good results if the sub-pixel estimations are integrated for a few pixels relative to an entire watershed. Integrated into the EU-FP7 ACQWA Project (www.acqwa.ch), this approach was first applied over Alpine area of Rhone river basin upper Geneva Lake: Canton du Valais, Switzerland (5 375 km²). In a second step over Alps, rolling hills and plain areas in Po catchment for Val d'Aosta and Piemonte regions, Italy (37 190 km²). Watershed boundaries were provided respectively by GRID (Ch) and ARPA (It) partners. The complete satellite images database was extracted from the U.S. MODIS/NASA website (http://modis.gsfc.nasa.gov/) for MOD09_B1 Reflectance images, and from the MODIS/NSIDC website (http://nsidc.org/index.html) for MOD10_A2 snow cover images. Only the Terra platform was used because images are acquired in the morning and are therefore better correlated with dry snow surface, avoiding cloud coverage of the afternoon (Aqua Platform). The MOD9 Image reflectance and MOD10_A2 products were respectively analyzed to retrieve (i) Fractional Snow cover at sub-pixel scale, and (ii) maximum snow cover. All products were retrieved at 8-days over a complete time period of 10 years (2000-2009), giving 500 images for each river basin. Digital Model Elevation was given by NASA/SRTM database at 90-m resolution and used (i) for illumination versus topography correction on snow cover, (ii) geometric rectification of images. Geographic projection is WGS84, UTM 32. Fractional Snow cover mapping was derived from the NDSI linear regression method (Salomonson et al., 2004). Cloud mask was given by MODIS-NASA library (radiometric threshold) and completed by inverse slope regression to avoid lowlands fog confusing with thin snow cover (Po river basin). Maximum Snow Cover mapping was retrieved from the NSIDC database classification (Hall et al., 2001). Validation step was processed using comparison between MODIS Snow maps outputs and meteorological data provided by network of 87 meteorological stations: temperature, precipitation, snow depth measurement. A 0.92 correlation was observed for snow/non snow cover and can be considered as quite satisfactory, given the radiometric problems encountered in mountainous areas, particularly in snowmelt season. The 10-years time period results indicates a main difference between (i) regular snow accumulation and depletion in Rhone and (ii) the high temporal and spatial variability of snow cover for Po. Then, a high sensitivity to low variation of air temperature, often close to 1° C was observed. This is the case in particular for the beginning and the end of the winter season. The regional snow cover depletion is both influenced by thermal positives anomalies (e.g. 2000 and 2006), and the general trend of rising atmospheric temperatures since the late 1980s, particularly for Po river basin. Results will be combined with two hydrological models: Topkapi and Fest.

Medical Language Processing for Knowledge Representation and Retrievals

PubMed Central

Lyman, Margaret; Sager, Naomi; Chi, Emile C.; Tick, Leo J.; Nhan, Ngo Thanh; Su, Yun; Borst, Francois; Scherrer, Jean-Raoul

1989-01-01

The Linguistic String Project-Medical Language Processor, a system for computer analysis of narrative patient documents in English, is being adapted for French Lettres de Sortie. The system converts the free-text input to a semantic representation which is then mapped into a relational database. Retrievals of clinical data from the database are described.

Systematically Retrieving Research: A Case Study Evaluating Seven Databases

ERIC Educational Resources Information Center

Taylor, Brian; Wylie, Emma; Dempster, Martin; Donnelly, Michael

2007-01-01

Objective: Developing the scientific underpinnings of social welfare requires effective and efficient methods of retrieving relevant items from the increasing volume of research. Method: We compared seven databases by running the nearest equivalent search on each. The search topic was chosen for relevance to social work practice with older people.…

Non-Agricultural Databases and Thesauri: Retrieval of Subject Headings and Non-Controlled Terms in Relation to Agriculture

ERIC Educational Resources Information Center

Bartol, Tomaz

2012-01-01

Purpose: The paper aims to assess the utility of non-agriculture-specific information systems, databases, and respective controlled vocabularies (thesauri) in organising and retrieving agricultural information. The purpose is to identify thesaurus-linked tree structures, controlled subject headings/terms (heading words, descriptors), and principal…

Spanish personal name variations in national and international biomedical databases: implications for information retrieval and bibliometric studies

PubMed Central

Ruiz-Pérez, R.; López-Cózar, E. Delgado; Jiménez-Contreras, E.

2002-01-01

Objectives: The study sought to investigate how Spanish names are handled by national and international databases and to identify mistakes that can undermine the usefulness of these databases for locating and retrieving works by Spanish authors. Methods: The authors sampled 172 articles published by authors from the University of Granada Medical School between 1987 and 1996 and analyzed the variations in how each of their names was indexed in Science Citation Index (SCI), MEDLINE, and Índice Médico Español (IME). The number and types of variants that appeared for each author's name were recorded and compared across databases to identify inconsistencies in indexing practices. We analyzed the relationship between variability (number of variants of an author's name) and productivity (number of items the name was associated with as an author), the consequences for retrieval of information, and the most frequent indexing structures used for Spanish names. Results: The proportion of authors who appeared under more then one name was 48.1% in SCI, 50.7% in MEDLINE, and 69.0% in IME. Productivity correlated directly with variability: more than 50% of the authors listed on five to ten items appeared under more than one name in any given database, and close to 100% of the authors listed on more than ten items appeared under two or more variants. Productivity correlated inversely with retrievability: as the number of variants for a name increased, the number of items retrieved under each variant decreased. For the most highly productive authors, the number of items retrieved under each variant tended toward one. The most frequent indexing methods varied between databases. In MEDLINE and IME, names were indexed correctly as “first surname second surname, first name initial middle name initial” (if present) in 41.7% and 49.5% of the records, respectively. However, in SCI, the most frequent method was “first surname, first name initial second name initial” (48.0% of the records) and first surname and second surname run together, first name initial (18.3%). Conclusions: Retrievability on the basis of author's name was poor in all three databases. Each database uses accurate indexing methods, but these methods fail to result in consistency or coherence for specific entries. The likely causes of inconsistency are: (1) use by authors of variants of their names during their publication careers, (2) lack of authority control in all three databases, (3) the use of an inappropriate indexing method for Spanish names in SCI, (4) authors' inconsistent behaviors, and (5) possible editorial interventions by some journals. We offer some suggestions as to how to avert the proliferation of author name variants in the databases. PMID:12398248

A searching and reporting system for relational databases using a graph-based metadata representation.

PubMed

Hewitt, Robin; Gobbi, Alberto; Lee, Man-Ling

2005-01-01

Relational databases are the current standard for storing and retrieving data in the pharmaceutical and biotech industries. However, retrieving data from a relational database requires specialized knowledge of the database schema and of the SQL query language. At Anadys, we have developed an easy-to-use system for searching and reporting data in a relational database to support our drug discovery project teams. This system is fast and flexible and allows users to access all data without having to write SQL queries. This paper presents the hierarchical, graph-based metadata representation and SQL-construction methods that, together, are the basis of this system's capabilities.

Data Compression in Full-Text Retrieval Systems.

ERIC Educational Resources Information Center

Bell, Timothy C.; And Others

1993-01-01

Describes compression methods for components of full-text systems such as text databases on CD-ROM. Topics discussed include storage media; structures for full-text retrieval, including indexes, inverted files, and bitmaps; compression tools; memory requirements during retrieval; and ranking and information retrieval. (Contains 53 references.)…

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

Search and retrieval of medical images for improved diagnosis of neurodegenerative diseases

NASA Astrophysics Data System (ADS)

Ekin, Ahmet; Jasinschi, Radu; Turan, Erman; Engbers, Rene; van der Grond, Jeroen; van Buchem, Mark A.

2007-01-01

In the medical world, the accuracy of diagnosis is mainly affected by either lack of sufficient understanding of some diseases or the inter-, and/or intra-observer variability of the diagnoses. The former requires understanding the progress of diseases at much earlier stages, extraction of important information from ever growing amounts of data, and finally finding correlations with certain features and complications that will illuminate the disease progression. The latter (inter-, and intra- observer variability) is caused by the differences in the experience levels of different medical experts (inter-observer variability) or by mental and physical tiredness of one expert (intra-observer variability). We believe that the use of large databases can help improve the current status of disease understanding and decision making. By comparing large number of patients, some of the otherwise hidden relations can be revealed that results in better understanding, patients with similar complications can be found, the diagnosis and treatment can be compared so that the medical expert can make a better diagnosis. To this effect, this paper introduces a search and retrieval system for brain MR databases and shows that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in the clinics. We propose to use Kendall's correlation value to automatically compare various returns to a query. We also describe a fully automated and fast brain MR image analysis system to detect degenerative iron accumulation in brain, as it is the case in Alzheimer's and Parkinson's. The system is composed of several novel image processing algorithms and has been extensively tested in Leiden University Medical Center over so far more than 600 patients.

A cloud-based multimodality case file for mobile devices.

PubMed

Balkman, Jason D; Loehfelm, Thomas W

2014-01-01

Recent improvements in Web and mobile technology, along with the widespread use of handheld devices in radiology education, provide unique opportunities for creating scalable, universally accessible, portable image-rich radiology case files. A cloud database and a Web-based application for radiologic images were developed to create a mobile case file with reasonable usability, download performance, and image quality for teaching purposes. A total of 75 radiology cases related to breast, thoracic, gastrointestinal, musculoskeletal, and neuroimaging subspecialties were included in the database. Breast imaging cases are the focus of this article, as they best demonstrate handheld display capabilities across a wide variety of modalities. This case subset also illustrates methods for adapting radiologic content to cloud platforms and mobile devices. Readers will gain practical knowledge about storage and retrieval of cloud-based imaging data, an awareness of techniques used to adapt scrollable and high-resolution imaging content for the Web, and an appreciation for optimizing images for handheld devices. The evaluation of this software demonstrates the feasibility of adapting images from most imaging modalities to mobile devices, even in cases of full-field digital mammograms, where high resolution is required to represent subtle pathologic features. The cloud platform allows cases to be added and modified in real time by using only a standard Web browser with no application-specific software. Challenges remain in developing efficient ways to generate, modify, and upload radiologic and supplementary teaching content to this cloud-based platform. Online supplemental material is available for this article. ©RSNA, 2014.

Current Development at the Southern California Earthquake Data Center (SCEDC)

NASA Astrophysics Data System (ADS)

Appel, V. L.; Clayton, R. W.

2005-12-01

Over the past year, the SCEDC completed or is near completion of three featured projects: Station Information System (SIS) Development: The SIS will provide users with an interface into complete and accurate station metadata for all current and historic data at the SCEDC. The goal of this project is to develop a system that can interact with a single database source to enter, update and retrieve station metadata easily and efficiently. The system will provide accurate station/channel information for active stations to the SCSN real-time processing system, as will as station/channel information for stations that have parametric data at the SCEDC i.e., for users retrieving data via STP. Additionally, the SIS will supply information required to generate dataless SEED and COSMOS V0 volumes and allow stations to be added to the system with a minimum, but incomplete set of information using predefined defaults that can be easily updated as more information becomes available. Finally, the system will facilitate statewide metadata exchange for both real-time processing and provide a common approach to CISN historic station metadata. Moment Tensor Solutions: The SCEDC is currently archiving and delivering Moment Magnitudes and Moment Tensor Solutions (MTS) produced by the SCSN in real-time and post-processing solutions for events spanning back to 1999. The automatic MTS runs on all local events with magnitudes > 3.0, and all regional events > 3.5. The distributed solution automatically creates links from all USGS Simpson Maps to a text e-mail summary solution, creates a .gif image of the solution, and updates the moment tensor database tables at the SCEDC. Searchable Scanned Waveforms Site: The Caltech Seismological Lab has made available 12,223 scanned images of pre-digital analog recordings of major earthquakes recorded in Southern California between 1962 and 1992 at http://www.data.scec.org/research/scans/. The SCEDC has developed a searchable web interface that allows users to search the available files, select multiple files for download and then retrieve a zipped file containing the results. Scanned images of paper records for M>3.5 southern California earthquakes and several significant teleseisms are available for download via the SCEDC through this search tool.

Comparing the quality of accessing medical literature using content-based visual and textual information retrieval

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Kahn, Charles E., Jr.; Hersh, William

2009-02-01

Content-based visual information (or image) retrieval (CBIR) has been an extremely active research domain within medical imaging over the past ten years, with the goal of improving the management of visual medical information. Many technical solutions have been proposed, and application scenarios for image retrieval as well as image classification have been set up. However, in contrast to medical information retrieval using textual methods, visual retrieval has only rarely been applied in clinical practice. This is despite the large amount and variety of visual information produced in hospitals every day. This information overload imposes a significant burden upon clinicians, and CBIR technologies have the potential to help the situation. However, in order for CBIR to become an accepted clinical tool, it must demonstrate a higher level of technical maturity than it has to date. Since 2004, the ImageCLEF benchmark has included a task for the comparison of visual information retrieval algorithms for medical applications. In 2005, a task for medical image classification was introduced and both tasks have been run successfully for the past four years. These benchmarks allow an annual comparison of visual retrieval techniques based on the same data sets and the same query tasks, enabling the meaningful comparison of various retrieval techniques. The datasets used from 2004-2007 contained images and annotations from medical teaching files. In 2008, however, the dataset used was made up of 67,000 images (along with their associated figure captions and the full text of their corresponding articles) from two Radiological Society of North America (RSNA) scientific journals. This article describes the results of the medical image retrieval task of the ImageCLEF 2008 evaluation campaign. We compare the retrieval results of both visual and textual information retrieval systems from 15 research groups on the aforementioned data set. The results show clearly that, currently, visual retrieval alone does not achieve the performance necessary for real-world clinical applications. Most of the common visual retrieval techniques have a MAP (Mean Average Precision) of around 2-3%, which is much lower than that achieved using textual retrieval (MAP=29%). Advanced machine learning techniques, together with good training data, have been shown to improve the performance of visual retrieval systems in the past. Multimodal retrieval (basing retrieval on both visual and textual information) can achieve better results than purely visual, but only when carefully applied. In many cases, multimodal retrieval systems performed even worse than purely textual retrieval systems. On the other hand, some multimodal retrieval systems demonstrated significantly increased early precision, which has been shown to be a desirable behavior in real-world systems.

Documentation of a spatial data-base management system for monitoring pesticide application in Washington

USGS Publications Warehouse

Schurr, K.M.; Cox, S.E.

1994-01-01

The Pesticide-Application Data-Base Management System was created as a demonstration project and was tested with data submitted to the Washington State Department of Agriculture by pesticide applicators from a small geographic area. These data were entered into the Department's relational data-base system and uploaded into the system's ARC/INFO files. Locations for pesticide applica- tions are assigned within the Public Land Survey System grids, and ARC/INFO programs in the Pesticide-Application Data-Base Management System can subdivide each survey section into sixteen idealized quarter-quarter sections for display map grids. The system provides data retrieval and geographic information system plotting capabilities from a menu of seven basic retrieval options. Additionally, ARC/INFO coverages can be created from the retrieved data when required for particular applications. The Pesticide-Application Data-Base Management System, or the general principles used in the system, could be adapted to other applica- tions or to other states.

Performing private database queries in a real-world environment using a quantum protocol.

PubMed

Chan, Philip; Lucio-Martinez, Itzel; Mo, Xiaofan; Simon, Christoph; Tittel, Wolfgang

2014-06-10

In the well-studied cryptographic primitive 1-out-of-N oblivious transfer, a user retrieves a single element from a database of size N without the database learning which element was retrieved. While it has previously been shown that a secure implementation of 1-out-of-N oblivious transfer is impossible against arbitrarily powerful adversaries, recent research has revealed an interesting class of private query protocols based on quantum mechanics in a cheat sensitive model. Specifically, a practical protocol does not need to guarantee that the database provider cannot learn what element was retrieved if doing so carries the risk of detection. The latter is sufficient motivation to keep a database provider honest. However, none of the previously proposed protocols could cope with noisy channels. Here we present a fault-tolerant private query protocol, in which the novel error correction procedure is integral to the security of the protocol. Furthermore, we present a proof-of-concept demonstration of the protocol over a deployed fibre.

Performing private database queries in a real-world environment using a quantum protocol

PubMed Central

Chan, Philip; Lucio-Martinez, Itzel; Mo, Xiaofan; Simon, Christoph; Tittel, Wolfgang

2014-01-01

In the well-studied cryptographic primitive 1-out-of-N oblivious transfer, a user retrieves a single element from a database of size N without the database learning which element was retrieved. While it has previously been shown that a secure implementation of 1-out-of-N oblivious transfer is impossible against arbitrarily powerful adversaries, recent research has revealed an interesting class of private query protocols based on quantum mechanics in a cheat sensitive model. Specifically, a practical protocol does not need to guarantee that the database provider cannot learn what element was retrieved if doing so carries the risk of detection. The latter is sufficient motivation to keep a database provider honest. However, none of the previously proposed protocols could cope with noisy channels. Here we present a fault-tolerant private query protocol, in which the novel error correction procedure is integral to the security of the protocol. Furthermore, we present a proof-of-concept demonstration of the protocol over a deployed fibre. PMID:24913129

Pathology report data extraction from relational database using R, with extraction from reports on melanoma of skin as an example.

PubMed

Ye, Jay J

2016-01-01

Different methods have been described for data extraction from pathology reports with varying degrees of success. Here a technique for directly extracting data from relational database is described. Our department uses synoptic reports modified from College of American Pathologists (CAP) Cancer Protocol Templates to report most of our cancer diagnoses. Choosing the melanoma of skin synoptic report as an example, R scripting language extended with RODBC package was used to query the pathology information system database. Reports containing melanoma of skin synoptic report in the past 4 and a half years were retrieved and individual data elements were extracted. Using the retrieved list of the cases, the database was queried a second time to retrieve/extract the lymph node staging information in the subsequent reports from the same patients. 426 synoptic reports corresponding to unique lesions of melanoma of skin were retrieved, and data elements of interest were extracted into an R data frame. The distribution of Breslow depth of melanomas grouped by year is used as an example of intra-report data extraction and analysis. When the new pN staging information was present in the subsequent reports, 82% (77/94) was precisely retrieved (pN0, pN1, pN2 and pN3). Additional 15% (14/94) was retrieved with certain ambiguity (positive or knowing there was an update). The specificity was 100% for both. The relationship between Breslow depth and lymph node status was graphed as an example of lesion-specific multi-report data extraction and analysis. R extended with RODBC package is a simple and versatile approach well-suited for the above tasks. The success or failure of the retrieval and extraction depended largely on whether the reports were formatted and whether the contents of the elements were consistently phrased. This approach can be easily modified and adopted for other pathology information systems that use relational database for data management.

Managing an Archive of Images

NASA Technical Reports Server (NTRS)

Andres, Vince; Walter, David; Hallal, Charles; Jones, Helene; Callac, Chris

2004-01-01

The SSC Multimedia Archive is an automated electronic system to manage images, acquired both by film and digital cameras, for the Public Affairs Office (PAO) at Stennis Space Center (SSC). Previously, the image archive was based on film photography and utilized a manual system that, by today s standards, had become inefficient and expensive. Now, the SSC Multimedia Archive, based on a server at SSC, contains both catalogs and images for pictures taken both digitally and with a traditional, film-based camera, along with metadata about each image. After a "shoot," a photographer downloads the images into the database. Members of the PAO can use a Web-based application to search, view and retrieve images, approve images for publication, and view and edit metadata associated with the images. Approved images are archived and cross-referenced with appropriate descriptions and information. Security is provided by allowing administrators to explicitly grant access privileges to personnel to only access components of the system that they need to (i.e., allow only photographers to upload images, only PAO designated employees may approve images).

Web image retrieval using an effective topic and content-based technique

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Prabhakara, Rashmi

2005-03-01

There has been an exponential growth in the amount of image data that is available on the World Wide Web since the early development of Internet. With such a large amount of information and image available and its usefulness, an effective image retrieval system is thus greatly needed. In this paper, we present an effective approach with both image matching and indexing techniques that improvise on existing integrated image retrieval methods. This technique follows a two-phase approach, integrating query by topic and query by example specification methods. In the first phase, The topic-based image retrieval is performed by using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. This technique consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. In the second phase, we use query by example specification to perform a low-level content-based image match in order to retrieve smaller and relatively closer results of the example image. From this, information related to the image feature is automatically extracted from the query image. The main objective of our approach is to develop a functional image search and indexing technique and to demonstrate that better retrieval results can be achieved.

Evaluating performance of biomedical image retrieval systems – an overview of the medical image retrieval task at ImageCLEF 2004–2013

PubMed Central

Kalpathy-Cramer, Jayashree; de Herrera, Alba García Seco; Demner-Fushman, Dina; Antani, Sameer; Bedrick, Steven; Müller, Henning

2014-01-01

Medical image retrieval and classification have been extremely active research topics over the past 15 years. With the ImageCLEF benchmark in medical image retrieval and classification a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluations campaigns. A detailed analysis of the data also highlights the value of the resources created. PMID:24746250

Generating descriptive visual words and visual phrases for large-scale image applications.

PubMed

Zhang, Shiliang; Tian, Qi; Hua, Gang; Huang, Qingming; Gao, Wen

2011-09-01

Bag-of-visual Words (BoWs) representation has been applied for various problems in the fields of multimedia and computer vision. The basic idea is to represent images as visual documents composed of repeatable and distinctive visual elements, which are comparable to the text words. Notwithstanding its great success and wide adoption, visual vocabulary created from single-image local descriptors is often shown to be not as effective as desired. In this paper, descriptive visual words (DVWs) and descriptive visual phrases (DVPs) are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs. Since images are the carriers of visual objects and scenes, a descriptive visual element set can be composed by the visual words and their combinations which are effective in representing certain visual objects or scenes. Based on this idea, a general framework is proposed for generating DVWs and DVPs for image applications. In a large-scale image database containing 1506 object and scene categories, the visual words and visual word pairs descriptive to certain objects or scenes are identified and collected as the DVWs and DVPs. Experiments show that the DVWs and DVPs are informative and descriptive and, thus, are more comparable with the text words than the classic visual words. We apply the identified DVWs and DVPs in several applications including large-scale near-duplicated image retrieval, image search re-ranking, and object recognition. The combination of DVW and DVP performs better than the state of the art in large-scale near-duplicated image retrieval in terms of accuracy, efficiency and memory consumption. The proposed image search re-ranking algorithm: DWPRank outperforms the state-of-the-art algorithm by 12.4% in mean average precision and about 11 times faster in efficiency.

Impact of line parameter database and continuum absorption on GOSAT TIR methane retrieval

NASA Astrophysics Data System (ADS)

Yamada, A.; Saitoh, N.; Nonogaki, R.; Imasu, R.; Shiomi, K.; Kuze, A.

2017-12-01

The current methane retrieval algorithm (V1) at wavenumber range from 1210 cm-1 to 1360 cm-1 including CH4 ν 4 band from the thermal infrared (TIR) band of Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT) uses LBLRTM V12.1 with AER V3.1 line database and MT CKD 2.5.2 continuum absorption model to calculate optical depth. Since line parameter databases have been updated and the continuum absorption may have large uncertainty, the purpose of this study is to assess the impact on {CH}4 retrieval from the choice of line parameter databases and the uncertainty of continuum absorption. We retrieved {CH}4 profiles with replacement of line parameter database from AER V3.1 to AER v1.0, HITRAN 2004, HITRAN 2008, AER V3.2, or HITRAN 2012 (Rothman et al. 2005, 2009, and 2013. Clough et al., 2005), we assumed 10% larger continuum absorption coefficients and 50% larger temperature dependent coefficient of continuum absorption based on the report by Paynter and Ramaswamy (2014). We compared the retrieved CH4 with the HIPPO CH4 observation (Wofsy et al., 2012). The difference from HIPPO observation of AER V3.2 was the smallest and 24.1 ± 45.9 ppbv. The differences of AER V1.0, HITRAN 2004, HITRAN 2008, and HITRAN 2012 were 35.6 ± 46.5 ppbv, 37.6 ± 46.3 ppbv, 32.1 ± 46.1 ppbv, and 35.2 ± 46.0 ppbv, respectively. Maximum {CH}4 retrieval differences were -0.4 ppbv at the layer of 314 hPa when we used 10% larger absorption coefficients of {H}2O foreign continuum. Comparing AER V3.2 case to HITRAN 2008 case, the line coupling effect reduced difference by 8.0 ppbv. Line coupling effects were important for GOSAT TIR {CH}4 retrieval. Effects from the uncertainty of continuum absorption were negligible small for GOSAT TIR CH4 retrieval.

Performance analysis of algorithms for retrieval of magnetic resonance images for interactive teleradiology

NASA Astrophysics Data System (ADS)

Atkins, M. Stella; Hwang, Robert; Tang, Simon

2001-05-01

We have implemented a prototype system consisting of a Java- based image viewer and a web server extension component for transmitting Magnetic Resonance Images (MRI) to an image viewer, to test the performance of different image retrieval techniques. We used full-resolution images, and images compressed/decompressed using the Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm. We examined the SPIHT decompression algorithm using both non- progressive and progressive transmission, focusing on the running times of the algorithm, client memory usage and garbage collection. We also compared the Java implementation with a native C++ implementation of the non- progressive SPIHT decompression variant. Our performance measurements showed that for uncompressed image retrieval using a 10Mbps Ethernet, a film of 16 MR images can be retrieved and displayed almost within interactive times. The native C++ code implementation of the client-side decoder is twice as fast as the Java decoder. If the network bandwidth is low, the high communication time for retrieving uncompressed images may be reduced by use of SPIHT-compressed images, although the image quality is then degraded. To provide diagnostic quality images, we also investigated the retrieval of up to 3 images on a MR film at full-resolution, using progressive SPIHT decompression. The Java-based implementation of progressive decompression performed badly, mainly due to the memory requirements for maintaining the image states, and the high cost of execution of the Java garbage collector. Hence, in systems where the bandwidth is high, such as found in a hospital intranet, SPIHT image compression does not provide advantages for image retrieval performance.

Controlled and Uncontrolled Subject Descriptions in the CF Database: A Comparison of Optimal Cluster-Based Retrieval Results.

ERIC Educational Resources Information Center

Shaw, W. M., Jr.

1993-01-01

Describes a study conducted on the cystic fibrosis (CF) database, a subset of MEDLINE, that investigated clustering structure and the effectiveness of cluster-based retrieval as a function of the exhaustivity of the uncontrolled subject descriptions. Results are compared to calculations for controlled descriptions based on Medical Subject Headings…

Information Retrieval Center: A Proposal for the Implementation of CD-ROM Database Technology at Memphis State University Libraries.

ERIC Educational Resources Information Center

Evans, John; Park, Betsy

This planning proposal recommends that Memphis State University Libraries make information on CD-ROM (compact disc--read only memory) available in the Reference Department by establishing an Information Retrieval Center (IRC). Following a brief introduction and statement of purpose, the library's databases, users, staffing, facilities, and…

Fault-tolerant symmetrically-private information retrieval

NASA Astrophysics Data System (ADS)

Wang, Tian-Yin; Cai, Xiao-Qiu; Zhang, Rui-Ling

2016-08-01

We propose two symmetrically-private information retrieval protocols based on quantum key distribution, which provide a good degree of database and user privacy while being flexible, loss-resistant and easily generalized to a large database similar to the precedent works. Furthermore, one protocol is robust to a collective-dephasing noise, and the other is robust to a collective-rotation noise.

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

PubMed Central

Wei, Wei; Ji, Zhanglong; He, Yupeng; Zhang, Kai; Ha, Yuanchi; Li, Qi; Ohno-Machado, Lucila

2018-01-01

Abstract The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search. As a result, users spend time searching datasets in known repositories, and they typically do not find new repositories. The biomedical and healthcare data discovery index ecosystem (bioCADDIE) team organized a challenge to solicit new indexing and searching strategies for retrieving biomedical datasets across repositories. We describe the work of one team that built a retrieval pipeline and examined its performance. The pipeline used online resources to supplement dataset metadata, automatically generated queries from users’ free-text questions, produced high-quality retrieval results and achieved the highest inferred Normalized Discounted Cumulative Gain among competitors. The results showed that it is a promising solution for cross-database, cross-domain and cross-repository biomedical dataset retrieval. Database URL: https://github.com/w2wei/dataset_retrieval_pipeline PMID:29688374

NVST Data Archiving System Based On FastBit NoSQL Database

NASA Astrophysics Data System (ADS)

Liu, Ying-bo; Wang, Feng; Ji, Kai-fan; Deng, Hui; Dai, Wei; Liang, Bo

2014-06-01

The New Vacuum Solar Telescope (NVST) is a 1-meter vacuum solar telescope that aims to observe the fine structures of active regions on the Sun. The main tasks of the NVST are high resolution imaging and spectral observations, including the measurements of the solar magnetic field. The NVST has been collecting more than 20 million FITS files since it began routine observations in 2012 and produces a maximum observational records of 120 thousand files in a day. Given the large amount of files, the effective archiving and retrieval of files becomes a critical and urgent problem. In this study, we implement a new data archiving system for the NVST based on the Fastbit Not Only Structured Query Language (NoSQL) database. Comparing to the relational database (i.e., MySQL; My Structured Query Language), the Fastbit database manifests distinctive advantages on indexing and querying performance. In a large scale database of 40 million records, the multi-field combined query response time of Fastbit database is about 15 times faster and fully meets the requirements of the NVST. Our study brings a new idea for massive astronomical data archiving and would contribute to the design of data management systems for other astronomical telescopes.

Medical Images Remote Consultation

NASA Astrophysics Data System (ADS)

Ferraris, Maurizio; Frixione, Paolo; Squarcia, Sandro

Teleconsultation of digital images among different medical centers is now a reality. The problem to be solved is how to interconnect all the clinical diagnostic devices in a hospital in order to allow physicians and health physicists, working in different places, to discuss on interesting clinical cases visualizing the same diagnostic images at the same time. Applying World Wide Web technologies, the proposed system can be easily used by people with no specific computer knowledge providing a verbose help to guide the user through the right steps of execution. Diagnostic images are retrieved from a relational database or from a standard DICOM-PACS through the DICOM-WWW gateway allowing connection of the usual Web browsers to DICOM applications via the HTTP protocol. The system, which is proposed for radiotherapy implementation, where radiographies play a fundamental role, can be easily converted to different field of medical applications where a remote access to secure data are compulsory.

Computer systems for annotation of single molecule fragments

DOEpatents

Schwartz, David Charles; Severin, Jessica

2016-07-19

There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.

A humming retrieval system based on music fingerprint

NASA Astrophysics Data System (ADS)

Han, Xingkai; Cao, Baiyu

2011-10-01

In this paper, we proposed an improved music information retrieval method utilizing the music fingerprint. The goal of this method is to represent the music with compressed musical information. Based on the selected MIDI files, which are generated automatically as our music target database, we evaluate the accuracy, effectiveness, and efficiency of this method. In this research we not only extract the feature sequence, which can represent the file effectively, from the query and melody database, but also make it possible for retrieving the results in an innovative way. We investigate on the influence of noise to the performance of our system. As experimental result shows, the retrieval accuracy arriving at up to91% without noise is pretty well

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

NASA Astrophysics Data System (ADS)

Patadia, Falguni; Levy, Robert C.; Mattoo, Shana

2018-06-01

Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from the underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon the Moderate Resolution Imaging Spectroradiometer (MODIS, on board Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, on board Suomi-NPP) sensors, use wavelength bands in window regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper, we use the High-resolution TRANsmission (HITRAN) database and Line-By-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are similar, they are different enough that applying MODIS-specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases of up to 0.07. As recent studies have been attempting to create a long-term data record by joining multiple satellite data sets, including MODIS and VIIRS, the consistency of gas correction has become even more crucial.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

NASA Astrophysics Data System (ADS)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Spotting words in handwritten Arabic documents

NASA Astrophysics Data System (ADS)

Srihari, Sargur; Srinivasan, Harish; Babu, Pavithra; Bhole, Chetan

2006-01-01

The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-- where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20,000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.

Supervised graph hashing for histopathology image retrieval and classification.

PubMed

Shi, Xiaoshuang; Xing, Fuyong; Xu, KaiDi; Xie, Yuanpu; Su, Hai; Yang, Lin

2017-12-01

In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used. Copyright © 2017 Elsevier B.V. All rights reserved.

Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements

NASA Astrophysics Data System (ADS)

Wiensz, J. T.; Degenstein, D. A.; Lloyd, N. D.; Bourassa, A. E.

2013-01-01

We present a technique for estimating the optical thickness of subvisual cirrus clouds detected by OSIRIS (Optical Spectrograph and Infrared Imaging System), a limb-viewing satellite instrument that measures scattered radiances from the UV to the near-IR. The measurement set is composed of a ratio of limb radiance profiles at two wavelengths that indicates the presence of cloud-scattering regions. Cross-sections and phase functions from an in situ database are used to simulate scattering by cloud-particles. With appropriate configurations discussed in this paper, the SASKTRAN successive-orders of scatter radiative transfer model is able to simulate accurately the in-cloud radiances from OSIRIS. Configured in this way, the model is used with a multiplicative algebraic reconstruction technique (MART) to retrieve the cloud extinction profile for an assumed effective cloud particle size. The sensitivity of these retrievals to key auxiliary model parameters is shown, and it is shown that the retrieved extinction profile, for an assumed effective cloud particle size, models well the measured in-cloud radiances from OSIRIS. The greatest sensitivity of the retrieved optical thickness is to the effective cloud particle size. Since OSIRIS has an 11-yr record of subvisual cirrus cloud detections, the work described in this manuscript provides a very useful method for providing a long-term global record of the properties of these clouds.

MRML: an extensible communication protocol for interoperability and benchmarking of multimedia information retrieval systems

NASA Astrophysics Data System (ADS)

Mueller, Wolfgang; Mueller, Henning; Marchand-Maillet, Stephane; Pun, Thierry; Squire, David M.; Pecenovic, Zoran; Giess, Christoph; de Vries, Arjen P.

2000-10-01

While in the area of relational databases interoperability is ensured by common communication protocols (e.g. ODBC/JDBC using SQL), Content Based Image Retrieval Systems (CBIRS) and other multimedia retrieval systems are lacking both a common query language and a common communication protocol. Besides its obvious short term convenience, interoperability of systems is crucial for the exchange and analysis of user data. In this paper, we present and describe an extensible XML-based query markup language, called MRML (Multimedia Retrieval markup Language). MRML is primarily designed so as to ensure interoperability between different content-based multimedia retrieval systems. Further, MRML allows researchers to preserve their freedom in extending their system as needed. MRML encapsulates multimedia queries in a way that enable multimedia (MM) query languages, MM content descriptions, MM query engines, and MM user interfaces to grow independently from each other, reaching a maximum of interoperability while ensuring a maximum of freedom for the developer. For benefitting from this, only a few simple design principles have to be respected when extending MRML for one's fprivate needs. The design of extensions withing the MRML framework will be described in detail in the paper. MRML has been implemented and tested for the CBIRS Viper, using the user interface Snake Charmer. Both are part of the GNU project and can be downloaded at our site.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Archetype relational mapping - a practical openEHR persistence solution.

PubMed

Wang, Li; Min, Lingtong; Wang, Rui; Lu, Xudong; Duan, Huilong

2015-11-05

One of the primary obstacles to the widespread adoption of openEHR methodology is the lack of practical persistence solutions for future-proof electronic health record (EHR) systems as described by the openEHR specifications. This paper presents an archetype relational mapping (ARM) persistence solution for the archetype-based EHR systems to support healthcare delivery in the clinical environment. First, the data requirements of the EHR systems are analysed and organized into archetype-friendly concepts. The Clinical Knowledge Manager (CKM) is queried for matching archetypes; when necessary, new archetypes are developed to reflect concepts that are not encompassed by existing archetypes. Next, a template is designed for each archetype to apply constraints related to the local EHR context. Finally, a set of rules is designed to map the archetypes to data tables and provide data persistence based on the relational database. A comparison study was conducted to investigate the differences among the conventional database of an EHR system from a tertiary Class A hospital in China, the generated ARM database, and the Node + Path database. Five data-retrieving tests were designed based on clinical workflow to retrieve exams and laboratory tests. Additionally, two patient-searching tests were designed to identify patients who satisfy certain criteria. The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests. The time difference of query executions conducted by the ARM database and the conventional database is less than 130 %. The ARM database was approximately 6-50 times more efficient than the conventional database in the patient-searching tests, while the Node + Path database requires far more time than the other two databases to execute both the data-retrieving and the patient-searching tests. The ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements. ARM performance is similar to that of conventionally-designed EHR systems, and can be applied in a practical clinical environment. System components such as ARM can greatly facilitate the adoption of openEHR architecture within EHR systems.

A Well-Calibrated Ocean Algorithm for Special Sensor Microwave/Imager

NASA Technical Reports Server (NTRS)

Wentz, Frank J.

1997-01-01

I describe an algorithm for retrieving geophysical parameters over the ocean from special sensor microwave/imager (SSM/I) observations. This algorithm is based on a model for the brightness temperature T(sub B) of the ocean and intervening atmosphere. The retrieved parameters are the near-surface wind speed W, the columnar water vapor V, the columnar cloud liquid water L, and the line-of-sight wind W(sub LS). I restrict my analysis to ocean scenes free of rain, and when the algorithm detects rain, the retrievals are discarded. The model and algorithm are precisely calibrated using a very large in situ database containing 37,650 SSM/I overpasses of buoys and 35,108 overpasses of radiosonde sites. A detailed error analysis indicates that the T(sub B) model rms accuracy is between 0.5 and 1 K and that the rms retrieval accuracies for wind, vapor, and cloud are 0.9 m/s, 1.2 mm, and 0.025 mm, respectively. The error in specifying the cloud temperature will introduce an additional 10% error in the cloud water retrieval. The spatial resolution for these accuracies is 50 km. The systematic errors in the retrievals are smaller than the rms errors, being about 0.3 m/s, 0.6 mm, and 0.005 mm for W, V, and L, respectively. The one exception is the systematic error in wind speed of -1.0 m/s that occurs for observations within +/-20 deg of upwind. The inclusion of the line-of-sight wind W(sub LS) in the retrieval significantly reduces the error in wind speed due to wind direction variations. The wind error for upwind observations is reduced from -3.0 to -1.0 m/s. Finally, I find a small signal in the 19-GHz, horizontal polarization (h(sub pol) T(sub B) residual DeltaT(sub BH) that is related to the effective air pressure of the water vapor profile. This information may be of some use in specifying the vertical distribution of water vapor.

A multi-image approach to CADx of breast cancer with integration into PACS

NASA Astrophysics Data System (ADS)

Elter, Matthias; Wittenberg, Thomas; Schulz-Wendtland, Rüdiger; Deserno, Thomas M.

2009-02-01

While screening mammography is accepted as the most adequate technique for the early detection of breast cancer, its low positive predictive value leads to many breast biopsies performed on benign lesions. Therefore, we have previously developed a knowledge-based system for computer-aided diagnosis (CADx) of mammographic lesions. It supports the radiologist in the discrimination of benign and malignant lesions. So far, our approach operates on the lesion level and employs the paradigm of content-based image retrieval (CBIR). Similar lesions with known diagnosis are retrieved automatically from a library of references. However, radiologists base their diagnostic decisions on additional resources, such as related mammographic projections, other modalities (e.g. ultrasound, MRI), and clinical data. Nonetheless, most CADx systems disregard the relation between the craniocaudal (CC) and mediolateral-oblique (MLO) views of conventional mammography. Therefore, we extend our approach to the full case level: (i) Multi-frame features are developed that jointly describe a lesion in different views of mammography. Taking into account the geometric relation between different images, these features can also be extracted from multi-modal data; (ii) the CADx system architecture is extended appropriately; (iii) the CADx system is integrated into the radiology information system (RIS) and the picture archiving and communication system (PACS). Here, the framework for image retrieval in medical applications (IRMA) is used to support access to the patient's health care record. Of particular interest is the application of the proposed CADx system to digital breast tomosynthesis (DBT), which has the potential to succeed digital mammography as the standard technique for breast cancer screening. The proposed system is a natural extension of CADx approaches that integrate only two modalities. However, we are still collecting a large enough database of breast lesions with images from multiple modalities to evaluate the benefits of the proposed approach on.

Palm-Vein Classification Based on Principal Orientation Features

PubMed Central

Zhou, Yujia; Liu, Yaqin; Feng, Qianjin; Yang, Feng; Huang, Jing; Nie, Yixiao

2014-01-01

Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database. PMID:25383715

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

An integrated one-step system to extract, analyze and annotate all relevant information from image-based cell screening of chemical libraries.

PubMed

Rabal, Obdulia; Link, Wolfgang; Serelde, Beatriz G; Bischoff, James R; Oyarzabal, Julen

2010-04-01

Here we report the development and validation of a complete solution to manage and analyze the data produced by image-based phenotypic screening campaigns of small-molecule libraries. In one step initial crude images are analyzed for multiple cytological features, statistical analysis is performed and molecules that produce the desired phenotypic profile are identified. A naïve Bayes classifier, integrating chemical and phenotypic spaces, is built and utilized during the process to assess those images initially classified as "fuzzy"-an automated iterative feedback tuning. Simultaneously, all this information is directly annotated in a relational database containing the chemical data. This novel fully automated method was validated by conducting a re-analysis of results from a high-content screening campaign involving 33 992 molecules used to identify inhibitors of the PI3K/Akt signaling pathway. Ninety-two percent of confirmed hits identified by the conventional multistep analysis method were identified using this integrated one-step system as well as 40 new hits, 14.9% of the total, originally false negatives. Ninety-six percent of true negatives were properly recognized too. A web-based access to the database, with customizable data retrieval and visualization tools, facilitates the posterior analysis of annotated cytological features which allows identification of additional phenotypic profiles; thus, further analysis of original crude images is not required.

Optical image transformation and encryption by phase-retrieval-based double random-phase encoding and compressive ghost imaging

NASA Astrophysics Data System (ADS)

Yuan, Sheng; Yang, Yangrui; Liu, Xuemei; Zhou, Xin; Wei, Zhenzhuo

2018-01-01

An optical image transformation and encryption scheme is proposed based on double random-phase encoding (DRPE) and compressive ghost imaging (CGI) techniques. In this scheme, a secret image is first transformed into a binary image with the phase-retrieval-based DRPE technique, and then encoded by a series of random amplitude patterns according to the ghost imaging (GI) principle. Compressive sensing, corrosion and expansion operations are implemented to retrieve the secret image in the decryption process. This encryption scheme takes the advantage of complementary capabilities offered by the phase-retrieval-based DRPE and GI-based encryption techniques. That is the phase-retrieval-based DRPE is used to overcome the blurring defect of the decrypted image in the GI-based encryption, and the CGI not only reduces the data amount of the ciphertext, but also enhances the security of DRPE. Computer simulation results are presented to verify the performance of the proposed encryption scheme.

SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands

NASA Astrophysics Data System (ADS)

Ho, Chris M. W.; Marshall, Garland R.

1993-12-01

SPLICE is a program that processes partial query solutions retrieved from 3D, structural databases to generate novel, aggregate ligands. It is designed to interface with the database searching program FOUNDATION, which retrieves fragments containing any combination of a user-specified minimum number of matching query elements. SPLICE eliminates aspects of structures that are physically incapable of binding within the active site. Then, a systematic rule-based procedure is performed upon the remaining fragments to ensure receptor complementarity. All modifications are automated and remain transparent to the user. Ligands are then assembled by linking components into composite structures through overlapping bonds. As a control experiment, FOUNDATION and SPLICE were used to reconstruct a know HIV-1 protease inhibitor after it had been fragmented, reoriented, and added to a sham database of fifty different small molecules. To illustrate the capabilities of this program, a 3D search query containing the pharmacophoric elements of an aspartic proteinase-inhibitor crystal complex was searched using FOUNDATION against a subset of the Cambridge Structural Database. One hundred thirty-one compounds were retrieved, each containing any combination of at least four query elements. Compounds were automatically screened and edited for receptor complementarity. Numerous combinations of fragments were discovered that could be linked to form novel structures, containing a greater number of pharmacophoric elements than any single retrieved fragment.

Diagnostic report acquisition unit for the Mayo/IBM PACS project

NASA Astrophysics Data System (ADS)

Brooks, Everett G.; Rothman, Melvyn L.

1991-07-01

The Mayo Clinic and IBM Rochester have jointly developed a picture archive and control system (PACS) for use with Mayo's MRI and Neuro-CT imaging modalities. One of the challenges of developing a useful PACS involves integrating the diagnostic reports with the electronic images so they can be displayed simultaneously. By the time a diagnostic report is generated for a particular case, its images have already been captured and archived by the PACS. To integrate the report with the images, the authors have developed an IBM Personal System/2 computer (PS/2) based diagnostic report acquisition unit (RAU). A typed copy of the report is transmitted via facsimile to the RAU where it is stacked electronically with other reports that have been sent previously but not yet processed. By processing these reports at the RAU, the information they contain is integrated with the image database and a copy of the report is archived electronically on an IBM Application System/400 computer (AS/400). When a user requests a set of images for viewing, the report is automatically integrated with the image data. By using a hot key, the user can toggle on/off the report on the display screen. This report describes process, hardware, and software employed to integrate the diagnostic report information into the PACS, including how the report images are captured, transmitted, and entered into the AS/400 database. Also described is how the archived reports and their associated medical images are located and merged for retrieval and display. The methods used to detect and process error conditions are also discussed.

Optically secured information retrieval using two authenticated phase-only masks.

PubMed

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-10-23

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices.

Optically secured information retrieval using two authenticated phase-only masks

PubMed Central

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-01-01

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices. PMID:26494213

Optically secured information retrieval using two authenticated phase-only masks

NASA Astrophysics Data System (ADS)

Wang, Xiaogang; Chen, Wen; Mei, Shengtao; Chen, Xudong

2015-10-01

We propose an algorithm for jointly designing two phase-only masks (POMs) that allow for the encryption and noise-free retrieval of triple images. The images required for optical retrieval are first stored in quick-response (QR) codes for noise-free retrieval and flexible readout. Two sparse POMs are respectively calculated from two different images used as references for authentication based on modified Gerchberg-Saxton algorithm (GSA) and pixel extraction, and are then used as support constraints in a modified double-phase retrieval algorithm (MPRA), together with the above-mentioned QR codes. No visible information about the target images or the reference images can be obtained from each of these authenticated POMs. This approach allows users to authenticate the two POMs used for image reconstruction without visual observation of the reference images. It also allows user to friendly access and readout with mobile devices.

Content-Based Medical Image Retrieval

NASA Astrophysics Data System (ADS)

Müller, Henning; Deserno, Thomas M.

This chapter details the necessity for alternative access concepts to the currently mainly text-based methods in medical information retrieval. This need is partly due to the large amount of visual data produced, the increasing variety of medical imaging data and changing user patterns. The stored visual data contain large amounts of unused information that, if well exploited, can help diagnosis, teaching and research. The chapter briefly reviews the history of image retrieval and its general methods before technologies that have been developed in the medical domain are focussed. We also discuss evaluation of medical content-based image retrieval (CBIR) systems and conclude with pointing out their strengths, gaps, and further developments. As examples, the MedGIFT project and the Image Retrieval in Medical Applications (IRMA) framework are presented.

High resolution satellite image indexing and retrieval using SURF features and bag of visual words

NASA Astrophysics Data System (ADS)

Bouteldja, Samia; Kourgli, Assia

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

NASA Astrophysics Data System (ADS)

Gundreddy, Rohith Reddy; Tan, Maxine; Qui, Yuchen; Zheng, Bin

2015-03-01

The purpose of this study is to develop and test a new content-based image retrieval (CBIR) scheme that enables to achieve higher reproducibility when it is implemented in an interactive computer-aided diagnosis (CAD) system without significantly reducing lesion classification performance. This is a new Fourier transform based CBIR algorithm that determines image similarity of two regions of interest (ROI) based on the difference of average regional image pixel value distribution in two Fourier transform mapped images under comparison. A reference image database involving 227 ROIs depicting the verified soft-tissue breast lesions was used. For each testing ROI, the queried lesion center was systematically shifted from 10 to 50 pixels to simulate inter-user variation of querying suspicious lesion center when using an interactive CAD system. The lesion classification performance and reproducibility as the queried lesion center shift were assessed and compared among the three CBIR schemes based on Fourier transform, mutual information and Pearson correlation. Each CBIR scheme retrieved 10 most similar reference ROIs and computed a likelihood score of the queried ROI depicting a malignant lesion. The experimental results shown that three CBIR schemes yielded very comparable lesion classification performance as measured by the areas under ROC curves with the p-value greater than 0.498. However, the CBIR scheme using Fourier transform yielded the highest invariance to both queried lesion center shift and lesion size change. This study demonstrated the feasibility of improving robustness of the interactive CAD systems by adding a new Fourier transform based image feature to CBIR schemes.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine.

PubMed

Bao, Shunxing; Weitendorf, Frederick D; Plassard, Andrew J; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A

2017-02-11

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging.

Theoretical and empirical comparison of big data image processing with Apache Hadoop and Sun Grid Engine

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2017-03-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and nonrelevant for medical imaging.

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

The new interactive CESAR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, P.B.; Yatabe, M.

1987-01-01

In this report the Nuclear Criticality Safety Analytical Methods Resource Center describes a new interactive version of CESAR, a critical experiments storage and retrieval program available on the Nuclear Criticality Information System (NCIS) database at Lawrence Livermore National Laboratory. The original version of CESAR did not include interactive search capabilities. The CESAR database was developed to provide a convenient, readily accessible means of storing and retrieving code input data for the SCALE Criticality Safety Analytical Sequences and the codes comprising those sequences. The database includes data for both cross section preparation and criticality safety calculations. 3 refs., 1 tab.

Health indicators 1991.

PubMed

Dawson, N

1991-01-01

This is the second edition of a database developed by the Canadian Centre for Health Information (CCHI). It features 49 health indicators, under one cover containing the most recent data available from a variety of national surveys. This information may be used to establish health goals for the population and to offer objective measures of their success. The database can be accessed through CANSIM, Statistics Canada's socio-economic electronic database and retrieval system, or through a personal computer package which enables the user to retrieve and analyze the 1.2 million data points in the system.

[Preparation of the database and the homepage on chemical accidents relating to health hazard].

PubMed

Yamamoto, M; Morita, M; Kaminuma, T

1998-01-01

We collected the data on accidents due to chemicals occurred in Japan, and prepared the database. We also set up the World Wide Web homepage containing the explanation on accidents due to chemicals and the retrieval page for the database. We designed the retrieval page so that users can search the data from keywords such as chemicals (e.g. chlorine gas, hydrogen sulfide, pesticides), places (e.g. home, factory, vehicles, tank), causes (e.g. reaction, leakage, exhaust gas) and others (e.g. cleaning, painting, transportation).

Quantification of signal detection performance degradation induced by phase-retrieval in propagation-based x-ray phase-contrast imaging

NASA Astrophysics Data System (ADS)

Chou, Cheng-Ying; Anastasio, Mark A.

2016-04-01

In propagation-based X-ray phase-contrast (PB XPC) imaging, the measured image contains a mixture of absorption- and phase-contrast. To obtain separate images of the projected absorption and phase (i.e., refractive) properties of a sample, phase retrieval methods can be employed. It has been suggested that phase-retrieval can always improve image quality in PB XPC imaging. However, when objective (task-based) measures of image quality are employed, this is not necessarily true and phase retrieval can be detrimental. In this work, signal detection theory is utilized to quantify the performance of a Hotelling observer (HO) for detecting a known signal in a known background. Two cases are considered. In the first case, the HO acts directly on the measured intensity data. In the second case, the HO acts on either the retrieved phase or absorption image. We demonstrate that the performance of the HO is superior when acting on the measured intensity data. The loss of task-specific information induced by phase-retrieval is quantified by computing the efficiency of the HO as the ratio of the test statistic signal-to-noise ratio (SNR) for the two cases. The effect of the system geometry on this efficiency is systematically investigated. Our findings confirm that phase-retrieval can impair signal detection performance in XPC imaging.

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE PAGES

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

2014-12-01

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Gradient descent algorithm applied to wavefront retrieval from through-focus images by an extreme ultraviolet microscope with partially coherent source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamazoe, Kenji; Mochi, Iacopo; Goldberg, Kenneth A.

The wavefront retrieval by gradient descent algorithm that is typically applied to coherent or incoherent imaging is extended to retrieve a wavefront from a series of through-focus images by partially coherent illumination. For accurate retrieval, we modeled partial coherence as well as object transmittance into the gradient descent algorithm. However, this modeling increases the computation time due to the complexity of partially coherent imaging simulation that is repeatedly used in the optimization loop. To accelerate the computation, we incorporate not only the Fourier transform but also an eigenfunction decomposition of the image. As a demonstration, the extended algorithm is appliedmore » to retrieve a field-dependent wavefront of a microscope operated at extreme ultraviolet wavelength (13.4 nm). The retrieved wavefront qualitatively matches the expected characteristics of the lens design.« less

Research on keyword retrieval method of HBase database based on index structure

NASA Astrophysics Data System (ADS)

Gong, Pijin; Lv, Congmin; Gong, Yongsheng; Ma, Haozhi; Sun, Yang; Wang, Lu

2017-10-01

With the rapid development of manned spaceflight engineering, the scientific experimental data in space application system is increasing rapidly. How to efficiently query the specific data in the mass data volume has become a problem. In this paper, a method of retrieving the object data based on the object attribute as the keyword is proposed. The HBase database is used to store the object data and object attributes, and the secondary index is constructed. The research shows that this method is a good way to retrieve specified data based on object attributes.

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

TissueWikiMobile: an Integrative Protein Expression Image Browser for Pathological Knowledge Sharing and Annotation on a Mobile Device

PubMed Central

Cheng, Chihwen; Stokes, Todd H.; Hang, Sovandy; Wang, May D.

2016-01-01

Doctors need fast and convenient access to medical data. This motivates the use of mobile devices for knowledge retrieval and sharing. We have developed TissueWikiMobile on the Apple iPhone and iPad to seamlessly access TissueWiki, an enormous repository of medical histology images. TissueWiki is a three terabyte database of antibody information and histology images from the Human Protein Atlas (HPA). Using TissueWikiMobile, users are capable of extracting knowledge from protein expression, adding annotations to highlight regions of interest on images, and sharing their professional insight. By providing an intuitive human computer interface, users can efficiently operate TissueWikiMobile to access important biomedical data without losing mobility. TissueWikiMobile furnishes the health community a ubiquitous way to collaborate and share their expert opinions not only on the performance of various antibodies stains but also on histology image annotation. PMID:27532057

Characterizing region of interest in image using MPEG-7 visual descriptors

NASA Astrophysics Data System (ADS)

Ryu, Min-Sung; Park, Soo-Jun; Won, Chee Sun

2005-08-01

In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

Retrieval of bilingual autobiographical memories: effects of cue language and cue imageability.

PubMed

Mortensen, Linda; Berntsen, Dorthe; Bohn, Ocke-Schwen

2015-01-01

An important issue in theories of bilingual autobiographical memory is whether linguistically encoded memories are represented in language-specific stores or in a common language-independent store. Previous research has found that autobiographical memory retrieval is facilitated when the language of the cue is the same as the language of encoding, consistent with language-specific memory stores. The present study examined whether this language congruency effect is influenced by cue imageability. Danish-English bilinguals retrieved autobiographical memories in response to Danish and English high- or low-imageability cues. Retrieval latencies were shorter to Danish than English cues and shorter to high- than low-imageability cues. Importantly, the cue language effect was stronger for low-than high-imageability cues. To examine the relationship between cue language and the language of internal retrieval, participants identified the language in which the memories were internally retrieved. More memories were retrieved when the cue language was the same as the internal language than when the cue was in the other language, and more memories were identified as being internally retrieved in Danish than English, regardless of the cue language. These results provide further evidence for language congruency effects in bilingual memory and suggest that this effect is influenced by cue imageability.

A biomedical information system for retrieval and manipulation of NHANES data.

PubMed

Mukherjee, Sukrit; Martins, David; Norris, Keith C; Jenders, Robert A

2013-01-01

The retrieval and manipulation of data from large public databases like the U.S. National Health and Nutrition Examination Survey (NHANES) may require sophisticated statistical software and significant expertise that may be unavailable in the university setting. In response, we have developed the Data Retrieval And Manipulation System (DReAMS), an automated information system to handle all processes of data extraction and cleaning and then joining different subsets to produce analysis-ready output. The system is a browser-based data warehouse application in which the input data from flat files or operational systems are aggregated in a structured way so that the desired data can be read, recoded, queried and extracted efficiently. The current pilot implementation of the system provides access to a limited amount of NHANES database. We plan to increase the amount of data available through the system in the near future and to extend the techniques to other large databases from CDU archive with a current holding of about 53 databases.

A Community Data Model for Hydrologic Observations

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Horsburgh, J. S.; Zaslavsky, I.; Maidment, D. R.; Valentine, D.; Jennings, B.

2006-12-01

The CUAHSI Hydrologic Information System project is developing information technology infrastructure to support hydrologic science. Hydrologic information science involves the description of hydrologic environments in a consistent way, using data models for information integration. This includes a hydrologic observations data model for the storage and retrieval of hydrologic observations in a relational database designed to facilitate data retrieval for integrated analysis of information collected by multiple investigators. It is intended to provide a standard format to facilitate the effective sharing of information between investigators and to facilitate analysis of information within a single study area or hydrologic observatory, or across hydrologic observatories and regions. The observations data model is designed to store hydrologic observations and sufficient ancillary information (metadata) about the observations to allow them to be unambiguously interpreted and used and provide traceable heritage from raw measurements to usable information. The design is based on the premise that a relational database at the single observation level is most effective for providing querying capability and cross dimension data retrieval and analysis. This premise is being tested through the implementation of a prototype hydrologic observations database, and the development of web services for the retrieval of data from and ingestion of data into the database. These web services hosted by the San Diego Supercomputer center make data in the database accessible both through a Hydrologic Data Access System portal and directly from applications software such as Excel, Matlab and ArcGIS that have Standard Object Access Protocol (SOAP) capability. This paper will (1) describe the data model; (2) demonstrate the capability for representing diverse data in the same database; (3) demonstrate the use of the database from applications software for the performance of hydrologic analysis across different observation types.

Social media based NPL system to find and retrieve ARM data: Concept paper

DOE Office of Scientific and Technical Information (OSTI.GOV)

Devarakonda, Ranjeet; Giansiracusa, Michael T.; Kumar, Jitendra

Information connectivity and retrieval has a role in our daily lives. The most pervasive source of online information is databases. The amount of data is growing at rapid rate and database technology is improving and having a profound effect. Almost all online applications are storing and retrieving information from databases. One challenge in supplying the public with wider access to informational databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it may notmore » be practical to make the public aware of the structure of the database. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide more intuitive method for generating database queries and delivering responses. Social media makes it possible to interact with a wide section of the population. Through this medium, and with the help of Natural Language Processing (NLP) we can make the data of the Atmospheric Radiation Measurement Data Center (ADC) more accessible to the public. We propose an architecture for using Apache Lucene/Solr [1], OpenML [2,3], and Kafka [4] to generate an automated query/response system with inputs from Twitter5, our Cassandra DB, and our log database. Using the Twitter API and NLP we can give the public the ability to ask questions of our database and get automated responses.« less

[Exploration and construction of the full-text database of acupuncture literature in the Republic of China].

PubMed

Fei, Lin; Zhao, Jing; Leng, Jiahao; Zhang, Shujian

2017-10-12

The ALIPORC full-text database is targeted at a specific full-text database of acupuncture literature in the Republic of China. Starting in 2015, till now, the database has been getting completed, focusing on books relevant with acupuncture, articles and advertising documents, accomplished or published in the Republic of China. The construction of this database aims to achieve the source sharing of acupuncture medical literature in the Republic of China through the retrieval approaches to diversity and accurate content presentation, contributes to the exchange of scholars, reduces the paper damage caused by paging and simplify the retrieval of the rare literature. The writers have made the explanation of the database in light of sources, characteristics and current situation of construction; and have discussed on improving the efficiency and integrity of the database and deepening the development of acupuncture literature in the Republic of China.

Large-scale retrieval for medical image analytics: A comprehensive review.

PubMed

Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

2018-01-01

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

World Wide Web Based Image Search Engine Using Text and Image Content Features

NASA Astrophysics Data System (ADS)

Luo, Bo; Wang, Xiaogang; Tang, Xiaoou

2003-01-01

Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image meta-search engine to retrieve images from the Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retrieve a broad coverage of images with a high recall rate and a relatively low precision. An image content based ordering is then performed on the initial image set. All the images are clustered into different folders based on the image content features. In addition, the images can be re-ranked by the content features according to the user feedback. Such a design makes it truly practical to use both text and image content for image retrieval over the Internet. Experimental results confirm the efficiency of the system.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.

ERIC Educational Resources Information Center

Wang, James Z.; Du, Yanping

Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…

Web Image Retrieval Using Self-Organizing Feature Map.

ERIC Educational Resources Information Center

Wu, Qishi; Iyengar, S. Sitharama; Zhu, Mengxia

2001-01-01

Provides an overview of current image retrieval systems. Describes the architecture of the SOFM (Self Organizing Feature Maps) based image retrieval system, discussing the system architecture and features. Introduces the Kohonen model, and describes the implementation details of SOFM computation and its learning algorithm. Presents a test example…

Automated semantic indexing of figure captions to improve radiology image retrieval.

PubMed

Kahn, Charles E; Rubin, Daniel L

2009-01-01

We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals. The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval. Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine. Estimated precision was 0.897 (95% confidence interval, 0.857-0.937). Estimated recall was 0.930 (95% confidence interval, 0.838-1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone. Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval.

JICST Factual Database JICST DNA Database

NASA Astrophysics Data System (ADS)

Shirokizawa, Yoshiko; Abe, Atsushi

Japan Information Center of Science and Technology (JICST) has started the on-line service of DNA database in October 1988. This database is composed of EMBL Nucleotide Sequence Library and Genetic Sequence Data Bank. The authors outline the database system, data items and search commands. Examples of retrieval session are presented.

Ontological interpretation of biomedical database content.

PubMed

Santana da Silva, Filipe; Jansen, Ludger; Freitas, Fred; Schulz, Stefan

2017-06-26

Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

Accelerating simulation for the multiple-point statistics algorithm using vector quantization

NASA Astrophysics Data System (ADS)

Zuo, Chen; Pan, Zhibin; Liang, Hao

2018-03-01

Multiple-point statistics (MPS) is a prominent algorithm to simulate categorical variables based on a sequential simulation procedure. Assuming training images (TIs) as prior conceptual models, MPS extracts patterns from TIs using a template and records their occurrences in a database. However, complex patterns increase the size of the database and require considerable time to retrieve the desired elements. In order to speed up simulation and improve simulation quality over state-of-the-art MPS methods, we propose an accelerating simulation for MPS using vector quantization (VQ), called VQ-MPS. First, a variable representation is presented to make categorical variables applicable for vector quantization. Second, we adopt a tree-structured VQ to compress the database so that stationary simulations are realized. Finally, a transformed template and classified VQ are used to address nonstationarity. A two-dimensional (2D) stationary channelized reservoir image is used to validate the proposed VQ-MPS. In comparison with several existing MPS programs, our method exhibits significantly better performance in terms of computational time, pattern reproductions, and spatial uncertainty. Further demonstrations consist of a 2D four facies simulation, two 2D nonstationary channel simulations, and a three-dimensional (3D) rock simulation. The results reveal that our proposed method is also capable of solving multifacies, nonstationarity, and 3D simulations based on 2D TIs.

Structural scene analysis and content-based image retrieval applied to bone age assessment

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Brosig, André; Deserno, Thomas M.; Ott, Bastian; Günther, Rolf W.

2009-02-01

Radiological bone age assessment is based on global or local image regions of interest (ROI), such as epiphyseal regions or the area of carpal bones. Usually, these regions are compared to a standardized reference and a score determining the skeletal maturity is calculated. For computer-assisted diagnosis, automatic ROI extraction is done so far by heuristic approaches. In this work, we apply a high-level approach of scene analysis for knowledge-based ROI segmentation. Based on a set of 100 reference images from the IRMA database, a so called structural prototype (SP) is trained. In this graph-based structure, the 14 phalanges and 5 metacarpal bones are represented by nodes, with associated location, shape, as well as texture parameters modeled by Gaussians. Accordingly, the Gaussians describing the relative positions, relative orientation, and other relative parameters between two nodes are associated to the edges. Thereafter, segmentation of a hand radiograph is done in several steps: (i) a multi-scale region merging scheme is applied to extract visually prominent regions; (ii) a graph/sub-graph matching to the SP robustly identifies a subset of the 19 bones; (iii) the SP is registered to the current image for complete scene-reconstruction (iv) the epiphyseal regions are extracted from the reconstructed scene. The evaluation is based on 137 images of Caucasian males from the USC hand atlas. Overall, an error rate of 32% is achieved, for the 6 middle distal and medial/distal epiphyses, 23% of all extractions need adjustments. On average 9.58 of the 14 epiphyseal regions were extracted successfully per image. This is promising for further use in content-based image retrieval (CBIR) and CBIR-based automatic bone age assessment.

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

NASA Astrophysics Data System (ADS)

Rahman, Md M.; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

This paper presents a novel approach to biomedical image retrieval by mapping image regions to local concepts and represent images in a weighted entropy-based concept feature space. The term concept refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist user in interactively select a Region-Of-Interest (ROI) and search for similar image ROIs. Further, a spatial verification step is used as a post-processing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval, is validated through experiments on a data set of 450 lung CT images extracted from journal articles from four different collections.

WE-E-BRB-11: Riview a Web-Based Viewer for Radiotherapy.

PubMed

Apte, A; Wang, Y; Deasy, J

2012-06-01

Collaborations involving radiotherapy data collection, such as the recently proposed international radiogenomics consortium, require robust, web-based tools to facilitate reviewing treatment planning information. We present the architecture and prototype characteristics for a web-based radiotherapy viewer. The web-based environment developed in this work consists of the following components: 1) Import of DICOM/RTOG data: CERR was leveraged to import DICOM/RTOG data and to convert to database friendly RT objects. 2) Extraction and Storage of RT objects: The scan and dose distributions were stored as .png files per slice and view plane. The file locations were written to the MySQL database. Structure contours and DVH curves were written to the database as numeric data. 3) Web interfaces to query, retrieve and visualize the RT objects: The Web application was developed using HTML 5 and Ruby on Rails (RoR) technology following the MVC philosophy. The open source ImageMagick library was utilized to overlay scan, dose and structures. The application allows users to (i) QA the treatment plans associated with a study, (ii) Query and Retrieve patients matching anonymized ID and study, (iii) Review up to 4 plans simultaneously in 4 window panes (iv) Plot DVH curves for the selected structures and dose distributions. A subset of data for lung cancer patients was used to prototype the system. Five user accounts were created to have access to this study. The scans, doses, structures and DVHs for 10 patients were made available via the web application. A web-based system to facilitate QA, and support Query, Retrieve and the Visualization of RT data was prototyped. The RIVIEW system was developed using open source and free technology like MySQL and RoR. We plan to extend the RIVIEW system further to be useful in clinical trial data collection, outcomes research, cohort plan review and evaluation. © 2012 American Association of Physicists in Medicine.

Using Conceptual Categories of Questions To Measure Differences in Retrieval Performance.

ERIC Educational Resources Information Center

Keyes, John G.

1996-01-01

To investigate the relationship between the retrieval mechanism and the level of question elaboration, this study divided 100 questions from the cystic fibrosis database into five conceptual categories based on their semantic representations. Two retrieval methods were chosen to investigate potential differences in outcomes across conceptual…

The Effects of Noisy Data on Text Retrieval.

ERIC Educational Resources Information Center

Taghva, Kazem; And Others

1994-01-01

Discusses the use of optical character recognition (OCR) for inputting documents in an information retrieval system and describes a study that used an OCR-generated database and its corresponding corrected version to examine query evaluation in the presence of noisy data. Scanning technology, recognition technology, and retrieval technology are…

KARL: A Knowledge-Assisted Retrieval Language. Presentation visuals. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Triantafyllopoulos, Spiros

1985-01-01

A collection of presentation visuals associated with the companion report entitled KARL: A Knowledge-Assisted Retrieval Language, is presented. Information is given on data retrieval, natural language database front ends, generic design objectives, processing capababilities and the query processing cycle.

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Multimedia Classifier

NASA Astrophysics Data System (ADS)

Costache, G. N.; Gavat, I.

2004-09-01

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases

NASA Astrophysics Data System (ADS)

Vega, J.; Murari, A.; Rattá, G. A.; Castro, P.; Pereira, A.; Portas, A.

2008-03-01

Diagnostics of present day reactor class fusion experiments, like the Joint European Torus (JET), generate thousands of signals (time series and video images) in each discharge. There is a direct correspondence between the physical phenomena taking place in the plasma and the set of structural shapes (patterns) that they form in the signals: bumps, unexpected amplitude changes, abrupt peaks, periodic components, high intensity zones or specific edge contours. A major difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behavior, i.e. discharges with "similar" patterns. Pattern recognition techniques are efficient tools to search for similar structural forms within the database in a fast an intelligent way. To this end, classification systems must be developed to be used as indexation methods to directly fetch the more similar patterns.

Inferring Cirrus Size Distributions Through Satellite Remote Sensing and Microphysical Databases

NASA Technical Reports Server (NTRS)

Mitchell, David; D'Entremont, Robert P.; Lawson, R. Paul

2010-01-01

Since cirrus clouds have a substantial influence on the global energy balance that depends on their microphysical properties, climate models should strive to realistically characterize the cirrus ice particle size distribution (PSD), at least in a climatological sense. To date, the airborne in situ measurements of the cirrus PSD have contained large uncertainties due to errors in measuring small ice crystals (D<60 m). This paper presents a method to remotely estimate the concentration of the small ice crystals relative to the larger ones using the 11- and 12- m channels aboard several satellites. By understanding the underlying physics producing the emissivity difference between these channels, this emissivity difference can be used to infer the relative concentration of small ice crystals. This is facilitated by enlisting temperature-dependent characterizations of the PSD (i.e., PSD schemes) based on in situ measurements. An average cirrus emissivity relationship between 12 and 11 m is developed here using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and is used to retrieve the PSD based on six different PSD schemes. The PSDs from the measurement-based PSD schemes are compared with corresponding retrieved PSDs to evaluate differences in small ice crystal concentrations. The retrieved PSDs generally had lower concentrations of small ice particles, with total number concentration independent of temperature. In addition, the temperature dependence of the PSD effective diameter De and fall speed Vf for these retrieved PSD schemes exhibited less variability relative to the unmodified PSD schemes. The reduced variability in the retrieved De and Vf was attributed to the lower concentrations of small ice crystals in the retrieved PSD.

Information content of visible and midinfrared radiances for retrieving tropical ice cloud properties

NASA Astrophysics Data System (ADS)

Chang, Kai-Wei; L'Ecuyer, Tristan S.; Kahn, Brian H.; Natraj, Vijay

2017-05-01

Hyperspectral instruments such as Atmospheric Infrared Sounder (AIRS) have spectrally dense observations effective for ice cloud retrievals. However, due to the large number of channels, only a small subset is typically used. It is crucial that this subset of channels be chosen to contain the maximum possible information about the retrieved variables. This study describes an information content analysis designed to select optimal channels for ice cloud retrievals. To account for variations in ice cloud properties, we perform channel selection over an ensemble of cloud regimes, extracted with a clustering algorithm, from a multiyear database at a tropical Atmospheric Radiation Measurement site. Multiple satellite viewing angles over land and ocean surfaces are considered to simulate the variations in observation scenarios. The results suggest that AIRS channels near wavelengths of 14, 10.4, 4.2, and 3.8 μm contain the most information. With an eye toward developing a joint AIRS-MODIS (Moderate Resolution Imaging Spectroradiometer) retrieval, the analysis is also applied to combined measurements from both instruments. While application of this method to MODIS yields results consistent with previous channel sensitivity studies, the analysis shows that this combination may yield substantial improvement in cloud retrievals. MODIS provides most information on optical thickness and particle size, aided by a better constraint on cloud vertical placement from AIRS. An alternate scenario where cloud top boundaries are supplied by the active sensors in the A-train is also explored. The more robust cloud placement afforded by active sensors shifts the optimal channels toward the window region and shortwave infrared, further constraining optical thickness and particle size.

Compressed domain indexing of losslessly compressed images

NASA Astrophysics Data System (ADS)

Schaefer, Gerald

2001-12-01

Image retrieval and image compression have been pursued separately in the past. Only little research has been done on a synthesis of the two by allowing image retrieval to be performed directly in the compressed domain of images without the need to uncompress them first. In this paper methods for image retrieval in the compressed domain of losslessly compressed images are introduced. While most image compression techniques are lossy, i.e. discard visually less significant information, lossless techniques are still required in fields like medical imaging or in situations where images must not be changed due to legal reasons. The algorithms in this paper are based on predictive coding methods where a pixel is encoded based on the pixel values of its (already encoded) neighborhood. The first method is based on an understanding that predictively coded data is itself indexable and represents a textural description of the image. The second method operates directly on the entropy encoded data by comparing codebooks of images. Experiments show good image retrieval results for both approaches.

Retrievable Inferior Vena Cava Filters: Factors that Affect Retrieval Success

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geisbuesch, Philipp, E-mail: philippgeisbuesch@gmx.de; Benenati, James F.; Pena, Constantino S.

Purpose: To report and analyze the indications, procedural success, and complications of retrievable inferior vena cava filters (rIVCF) placement and to identify parameters that influence retrieval attempt and failure. Methods: Between January 2005 and December 2010, a total of 200 patients (80 men, median age 67 years, range 11-95 years) received a rIVCF with the clinical possibility that it could be removed. All patients with rIVCF were prospectively entered into a database and followed until retrieval or a decision not to retrieve the filter was made. A retrospective analysis of this database was performed. Results: Sixty-one percent of patients hadmore » an accepted indication for filter placement; 39% of patients had a relative indication. There was a tendency toward a higher retrieval rate in patients with relative indications (40% vs. 55%, P = 0.076). Filter placement was technically successful in all patients, with no procedure-related mortality. The retrieval rate was 53%. Patient age of >80 years (odds ratio [OR] 0.056, P > 0.0001) and presence of malignancy (OR 0.303, P = 0.003) was associated with a significantly reduced probability for attempted retrieval. Retrieval failure occurred in 7% (6 of 91) of all retrieval attempts. A time interval of > 90 days between implantation and attempted retrieval was associated with retrieval failure (OR 19.8, P = 0.009). Conclusions: Patient age >80 years and a history of malignancy are predictors of a reduced probability for retrieval attempt. The rate of retrieval failure is low and seems to be associated with a time interval of >90 days between filter placement and retrieval.« less

Linear information retrieval method in X-ray grating-based phase contrast imaging and its interchangeability with tomographic reconstruction

NASA Astrophysics Data System (ADS)

Wu, Z.; Gao, K.; Wang, Z. L.; Shao, Q. G.; Hu, R. F.; Wei, C. X.; Zan, G. B.; Wali, F.; Luo, R. H.; Zhu, P. P.; Tian, Y. C.

2017-06-01

In X-ray grating-based phase contrast imaging, information retrieval is necessary for quantitative research, especially for phase tomography. However, numerous and repetitive processes have to be performed for tomographic reconstruction. In this paper, we report a novel information retrieval method, which enables retrieving phase and absorption information by means of a linear combination of two mutually conjugate images. Thanks to the distributive law of the multiplication as well as the commutative law and associative law of the addition, the information retrieval can be performed after tomographic reconstruction, thus simplifying the information retrieval procedure dramatically. The theoretical model of this method is established in both parallel beam geometry for Talbot interferometer and fan beam geometry for Talbot-Lau interferometer. Numerical experiments are also performed to confirm the feasibility and validity of the proposed method. In addition, we discuss its possibility in cone beam geometry and its advantages compared with other methods. Moreover, this method can also be employed in other differential phase contrast imaging methods, such as diffraction enhanced imaging, non-interferometric imaging, and edge illumination.

Phase retrieval using regularization method in intensity correlation imaging

NASA Astrophysics Data System (ADS)

Li, Xiyu; Gao, Xin; Tang, Jia; Lu, Changming; Wang, Jianli; Wang, Bin

2014-11-01

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object's image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

Utilizing semantic networks to database and retrieve generalized stochastic colored Petri nets

NASA Technical Reports Server (NTRS)

Farah, Jeffrey J.; Kelley, Robert B.

1992-01-01

Previous work has introduced the Planning Coordinator (PCOORD), a coordinator functioning within the hierarchy of the Intelligent Machine Mode. Within the structure of the Planning Coordinator resides the Primitive Structure Database (PSDB) functioning to provide the primitive structures utilized by the Planning Coordinator in the establishing of error recovery or on-line path plans. This report further explores the Primitive Structure Database and establishes the potential of utilizing semantic networks as a means of efficiently storing and retrieving the Generalized Stochastic Colored Petri Nets from which the error recovery plans are derived.

Video-assisted segmentation of speech and audio track

NASA Astrophysics Data System (ADS)

Pandit, Medha; Yusoff, Yusseri; Kittler, Josef; Christmas, William J.; Chilton, E. H. S.

1999-08-01

Video database research is commonly concerned with the storage and retrieval of visual information invovling sequence segmentation, shot representation and video clip retrieval. In multimedia applications, video sequences are usually accompanied by a sound track. The sound track contains potential cues to aid shot segmentation such as different speakers, background music, singing and distinctive sounds. These different acoustic categories can be modeled to allow for an effective database retrieval. In this paper, we address the problem of automatic segmentation of audio track of multimedia material. This audio based segmentation can be combined with video scene shot detection in order to achieve partitioning of the multimedia material into semantically significant segments.

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments.

PubMed

Al-Nawashi, Malek; Al-Hazaimeh, Obaida M; Saraee, Mohamad

2017-01-01

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

DBMap: a TreeMap-based framework for data navigation and visualization of brain research registry

NASA Astrophysics Data System (ADS)

Zhang, Ming; Zhang, Hong; Tjandra, Donny; Wong, Stephen T. C.

2003-05-01

The purpose of this study is to investigate and apply a new, intuitive and space-conscious visualization framework to facilitate efficient data presentation and exploration of large-scale data warehouses. We have implemented the DBMap framework for the UCSF Brain Research Registry. Such a novel utility would facilitate medical specialists and clinical researchers in better exploring and evaluating a number of attributes organized in the brain research registry. The current UCSF Brain Research Registry consists of a federation of disease-oriented database modules, including Epilepsy, Brain Tumor, Intracerebral Hemorrphage, and CJD (Creuzfeld-Jacob disease). These database modules organize large volumes of imaging and non-imaging data to support Web-based clinical research. While the data warehouse supports general information retrieval and analysis, there lacks an effective way to visualize and present the voluminous and complex data stored. This study investigates whether the TreeMap algorithm can be adapted to display and navigate categorical biomedical data warehouse or registry. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of computer screen with an intuitive user interface. The paper will describe, DBMap, the proposed new data visualization framework for large biomedical databases. Built upon XML, Java and JDBC technologies, the prototype system includes a set of software modules that reside in the application server tier and provide interface to backend database tier and front-end Web tier of the brain registry.

Medical Image Retrieval: A Multimodal Approach

PubMed Central

Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

2014-01-01

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system. PMID:26309389

A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

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.

MISR Interactive Explorer (MINX) : Production Digitizing to Retrieve Smoke Plume Heights and Validating Heights Against Lidar Data

NASA Technical Reports Server (NTRS)

Dunst, Ben

2011-01-01

The height at which smoke from a wildfire is injected into the atmosphere is an important parameter for climatology, because it determines how far the smoke can be transported. Using the MINX program to analyze MISR (Multi-angle Imaging Spectro-Radiometer) data, I digitized wildfire smoke plumes to add to an existing database of these heights for use by scientists studying smoke transport and plume dynamics. In addition to using MINX to do production digitizing of heights, I assisted in gathering lidar data for an ongoing validation of MINX and helped evaluate those data.

Natural texture retrieval based on perceptual similarity measurement

NASA Astrophysics Data System (ADS)

Gao, Ying; Dong, Junyu; Lou, Jianwen; Qi, Lin; Liu, Jun

2018-04-01

A typical texture retrieval system performs feature comparison and might not be able to make human-like judgments of image similarity. Meanwhile, it is commonly known that perceptual texture similarity is difficult to be described by traditional image features. In this paper, we propose a new texture retrieval scheme based on texture perceptual similarity. The key of the proposed scheme is that prediction of perceptual similarity is performed by learning a non-linear mapping from image features space to perceptual texture space by using Random Forest. We test the method on natural texture dataset and apply it on a new wallpapers dataset. Experimental results demonstrate that the proposed texture retrieval scheme with perceptual similarity improves the retrieval performance over traditional image features.

Complex amplitude reconstruction by iterative amplitude-phase retrieval algorithm with reference

NASA Astrophysics Data System (ADS)

Shen, Cheng; Guo, Cheng; Tan, Jiubin; Liu, Shutian; Liu, Zhengjun

2018-06-01

Multi-image iterative phase retrieval methods have been successfully applied in plenty of research fields due to their simple but efficient implementation. However, there is a mismatch between the measurement of the first long imaging distance and the sequential interval. In this paper, an amplitude-phase retrieval algorithm with reference is put forward without additional measurements or priori knowledge. It gets rid of measuring the first imaging distance. With a designed update formula, it significantly raises the convergence speed and the reconstruction fidelity, especially in phase retrieval. Its superiority over the original amplitude-phase retrieval (APR) method is validated by numerical analysis and experiments. Furthermore, it provides a conceptual design of a compact holographic image sensor, which can achieve numerical refocusing easily.

Leveraging Terminologies for Retrieval of Radiology Reports with Critical Imaging Findings

PubMed Central

Warden, Graham I.; Lacson, Ronilda; Khorasani, Ramin

2011-01-01

Introduction: Communication of critical imaging findings is an important component of medical quality and safety. A fundamental challenge includes retrieval of radiology reports that contain these findings. This study describes the expressiveness and coverage of existing medical terminologies for critical imaging findings and evaluates radiology report retrieval using each terminology. Methods: Four terminologies were evaluated: National Cancer Institute Thesaurus (NCIT), Radiology Lexicon (RadLex), Systemized Nomenclature of Medicine (SNOMED-CT), and International Classification of Diseases (ICD-9-CM). Concepts in each terminology were identified for 10 critical imaging findings. Three findings were subsequently selected to evaluate document retrieval. Results: SNOMED-CT consistently demonstrated the highest number of overall terms (mean=22) for each of ten critical findings. However, retrieval rate and precision varied between terminologies for the three findings evaluated. Conclusion: No single terminology is optimal for retrieving radiology reports with critical findings. The expressiveness of a terminology does not consistently correlate with radiology report retrieval. PMID:22195212

Development of a database for Louisiana highway bridge scour data : technical summary.

DOT National Transportation Integrated Search

1999-10-01

The objectives of the project included: 1) developed a database with manipulation capabilities such as data retrieval, visualization, and update; 2) Input the existing scour data from DOTD files into the database.

Intelligent retrieval of medical images from the Internet

NASA Astrophysics Data System (ADS)

Tang, Yau-Kuo; Chiang, Ted T.

1996-05-01

The object of this study is using Internet resources to provide a cost-effective, user-friendly method to access the medical image archive system and to provide an easy method for the user to identify the images required. This paper describes the prototype system architecture, the implementation, and results. In the study, we prototype the Intelligent Medical Image Retrieval (IMIR) system as a Hypertext Transport Prototype server and provide Hypertext Markup Language forms for user, as an Internet client, using browser to enter image retrieval criteria for review. We are developing the intelligent retrieval engine, with the capability to map the free text search criteria to the standard terminology used for medical image identification. We evaluate retrieved records based on the number of the free text entries matched and their relevance level to the standard terminology. We are in the integration and testing phase. We have collected only a few different types of images for testing and have trained a few phrases to map the free text to the standard medical terminology. Nevertheless, we are able to demonstrate the IMIR's ability to search, retrieve, and review medical images from the archives using general Internet browser. The prototype also uncovered potential problems in performance, security, and accuracy. Additional studies and enhancements will make the system clinically operational.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

PubMed

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

PubMed Central

Song, Wei; Mei, Haibin

2017-01-01

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. PMID:28737699

Dynamic "inline" images: context-sensitive retrieval and integration of images into Web documents.

PubMed

Kahn, Charles E

2008-09-01

Integrating relevant images into web-based information resources adds value for research and education. This work sought to evaluate the feasibility of using "Web 2.0" technologies to dynamically retrieve and integrate pertinent images into a radiology web site. An online radiology reference of 1,178 textual web documents was selected as the set of target documents. The ARRS GoldMiner image search engine, which incorporated 176,386 images from 228 peer-reviewed journals, retrieved images on demand and integrated them into the documents. At least one image was retrieved in real-time for display as an "inline" image gallery for 87% of the web documents. Each thumbnail image was linked to the full-size image at its original web site. Review of 20 randomly selected Collaborative Hypertext of Radiology documents found that 69 of 72 displayed images (96%) were relevant to the target document. Users could click on the "More" link to search the image collection more comprehensively and, from there, link to the full text of the article. A gallery of relevant radiology images can be inserted easily into web pages on any web server. Indexing by concepts and keywords allows context-aware image retrieval, and searching by document title and subject metadata yields excellent results. These techniques allow web developers to incorporate easily a context-sensitive image gallery into their documents.

Automated Semantic Indexing of Figure Captions to Improve Radiology Image Retrieval

PubMed Central

Kahn, Charles E.; Rubin, Daniel L.

2009-01-01

Objective We explored automated concept-based indexing of unstructured figure captions to improve retrieval of images from radiology journals. Design The MetaMap Transfer program (MMTx) was used to map the text of 84,846 figure captions from 9,004 peer-reviewed, English-language articles to concepts in three controlled vocabularies from the UMLS Metathesaurus, version 2006AA. Sampling procedures were used to estimate the standard information-retrieval metrics of precision and recall, and to evaluate the degree to which concept-based retrieval improved image retrieval. Measurements Precision was estimated based on a sample of 250 concepts. Recall was estimated based on a sample of 40 concepts. The authors measured the impact of concept-based retrieval to improve upon keyword-based retrieval in a random sample of 10,000 search queries issued by users of a radiology image search engine. Results Estimated precision was 0.897 (95% confidence interval, 0.857–0.937). Estimated recall was 0.930 (95% confidence interval, 0.838–1.000). In 5,535 of 10,000 search queries (55%), concept-based retrieval found results not identified by simple keyword matching; in 2,086 searches (21%), more than 75% of the results were found by concept-based search alone. Conclusion Concept-based indexing of radiology journal figure captions achieved very high precision and recall, and significantly improved image retrieval. PMID:19261938

User-oriented evaluation of a medical image retrieval system for radiologists.

PubMed

Markonis, Dimitrios; Holzer, Markus; Baroz, Frederic; De Castaneda, Rafael Luis Ruiz; Boyer, Célia; Langs, Georg; Müller, Henning

2015-10-01

This article reports the user-oriented evaluation of a text- and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful additions. Another objective is to investigate the system's added value to radiology information retrieval. The study provides an insight into required specifications and potential shortcomings of medical image retrieval systems through a concrete methodology for conducting user tests. User tests with a working image retrieval system of images from the biomedical literature were performed in an iterative manner, where each iteration had the participants perform radiology information seeking tasks and then refining the system as well as the user study design itself. During these tasks the interaction of the users with the system was monitored, usability aspects were measured, retrieval success rates recorded and feedback was collected through survey forms. In total, 16 radiologists participated in the user tests. The success rates in finding relevant information were on average 87% and 78% for image and case retrieval tasks, respectively. The average time for a successful search was below 3 min in both cases. Users felt quickly comfortable with the novel techniques and tools (after 5 to 15 min), such as content-based image retrieval and relevance feedback. User satisfaction measures show a very positive attitude toward the system's functionalities while the user feedback helped identifying the system's weak points. The participants proposed several potentially useful new functionalities, such as filtering by imaging modality and search for articles using image examples. The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems. Copyright © 2015. Published by Elsevier Ireland Ltd.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

Cost-effective data storage/archival subsystem for functional PACS

NASA Astrophysics Data System (ADS)

Chen, Y. P.; Kim, Yongmin

1993-09-01

Not the least of the requirements of a workable PACS is the ability to store and archive vast amounts of information. A medium-size hospital will generate between 1 and 2 TBytes of data annually on a fully functional PACS. A high-speed image transmission network coupled with a comparably high-speed central data storage unit can make local memory and magnetic disks in the PACS workstations less critical and, in an extreme case, unnecessary. Under these circumstances, the capacity and performance of the central data storage subsystem and database is critical in determining the response time at the workstations, thus significantly affecting clinical acceptability. The central data storage subsystem not only needs to provide sufficient capacity to store about ten days worth of images (five days worth of new studies, and on the average, about one comparison study for each new study), but also supplies images to the requesting workstation in a timely fashion. The database must provide fast retrieval responses upon users' requests for images. This paper analyzes both advantages and disadvantages of multiple parallel transfer disks versus RAID disks for short-term central data storage subsystem, as well as optical disk jukebox versus digital recorder tape subsystem for long-term archive. Furthermore, an example high-performance cost-effective storage subsystem which integrates both the RAID disks and high-speed digital tape subsystem as a cost-effective PACS data storage/archival unit are presented.

Duplicate document detection in DocBrowse

NASA Astrophysics Data System (ADS)

Chalana, Vikram; Bruce, Andrew G.; Nguyen, Thien

1998-04-01

Duplicate documents are frequently found in large databases of digital documents, such as those found in digital libraries or in the government declassification effort. Efficient duplicate document detection is important not only to allow querying for similar documents, but also to filter out redundant information in large document databases. We have designed three different algorithm to identify duplicate documents. The first algorithm is based on features extracted from the textual content of a document, the second algorithm is based on wavelet features extracted from the document image itself, and the third algorithm is a combination of the first two. These algorithms are integrated within the DocBrowse system for information retrieval from document images which is currently under development at MathSoft. DocBrowse supports duplicate document detection by allowing (1) automatic filtering to hide duplicate documents, and (2) ad hoc querying for similar or duplicate documents. We have tested the duplicate document detection algorithms on 171 documents and found that text-based method has an average 11-point precision of 97.7 percent while the image-based method has an average 11- point precision of 98.9 percent. However, in general, the text-based method performs better when the document contains enough high-quality machine printed text while the image- based method performs better when the document contains little or no quality machine readable text.

Experiments and Analysis on a Computer Interface to an Information-Retrieval Network.

ERIC Educational Resources Information Center

Marcus, Richard S.; Reintjes, J. Francis

A primary goal of this project was to develop an interface that would provide direct access for inexperienced users to existing online bibliographic information retrieval networks. The experiment tested the concept of a virtual-system mode of access to a network of heterogeneous interactive retrieval systems and databases. An experimental…

HUC--A User Designed System for All Recorded Knowledge and Information.

ERIC Educational Resources Information Center

Hilton, Howard J.

This paper proposes a user designed system, HUC, intended to provide a single index and retrieval system covering all recorded knowledge and information capable of being retrieved from all modes of storage, from manual to the most sophisticated retrieval system. The concept integrates terminal hardware, software, and database structure to allow…

A new natural hazards data-base for volcanic ash and SO2 from global satellite remote sensing measurements

NASA Astrophysics Data System (ADS)

Prata, F.; Stebel, K.

2013-12-01

Over the last few years there has been a recognition of the utility of satellite measurements to identify and track volcanic emissions that present a natural hazard to human populations. Mitigation of the volcanic hazard to life and the environment requires understanding of the properties of volcanic emissions, identifying the hazard in near real-time and being able to provide timely and accurate forecasts to affected areas. Amongst the many ways to measure volcanic emissions, satellite remote sensing is capable of providing global quantitative retrievals of important microphysical parameters such as ash mass loading, ash particle effective radius, infrared optical depth, SO2 partial and total column abundance, plume altitude, aerosol optical depth and aerosol absorbing index. The eruption of Eyjafjallajokull in April-May, 2010 led to increased research and measurement programs to better characterize properties of volcanic ash and the need to establish a data-base in which to store and access these data was confirmed. The European Space Agency (ESA) has recognized the importance of having a quality controlled data-base of satellite retrievals and has funded an activity (VAST) to develop novel remote sensing retrieval schemes and a data-base, initially focused on several recent hazardous volcanic eruptions. As a first step, satellite retrievals for the eruptions of Eyjafjallajokull, Grimsvotn, Puyhue-Cordon Caulle, Nabro, Merapi, Okmok, Kasatochi and Sarychev Peak are being considered. Here we describe the data, retrievals and methods being developed for the data-base. Three important applications of the data-base are illustrated related to the ash/aviation problem, to the impact of the Merapi volcanic eruption on the local population, and to estimate SO2 fluxes from active volcanoes-as a means to diagnose future unrest. Dispersion model simulations are also being included in the data-base. In time, data from conventional in situ sampling instruments, airborne and ground-based remote sensing platforms and other meta-data (bulk ash and gas properties, volcanic setting, volcanic eruption chronologies, hazards and impacts etc.) will be added. The data-base has the potential to provide the natural hazards community with the first dynamic atmospheric volcanic hazards map and will be a valuable tool particularly for global transport.

A client/server system for Internet access to biomedical text/image databanks.

PubMed

Thoma, G R; Long, L R; Berman, L E

1996-01-01

Internet access to mixed text/image databanks is finding application in the medical world. An example is a database of medical X-rays and associated data consisting of demographic, socioeconomic, physician's exam, medical laboratory and other information collected as part of a nationwide health survey conducted by the government. Another example is a collection of digitized cryosection images, CT and MR taken of cadavers as part of the National Library of Medicine's Visible Human Project. In both cases, the challenge is to provide access to both the image and the associated text for a wide end user community to create atlases, conduct epidemiological studies, to develop image-specific algorithms for compression, enhancement and other types of image processing, among many other applications. The databanks mentioned above are being created in prototype form. This paper describes the prototype system developed for the archiving of the data and the client software to enable a broad range of end users to access the archive, retrieve text and image data, display the data and manipulate the images. System design considerations include; data organization in a relational database management system with object-oriented extensions; a hierarchical organization of the image data by different resolution levels for different user classes; client design based on common hardware and software platforms incorporating SQL search capability, X Window, Motif and TAE (a development environment supporting rapid prototyping and management of graphic-oriented user interfaces); potential to include ultra high resolution display monitors as a user option; intuitive user interface paradigm for building complex queries; and contrast enhancement, magnification and mensuration tools for better viewing by the user.

Phase retrieval by coherent modulation imaging.

PubMed

Zhang, Fucai; Chen, Bo; Morrison, Graeme R; Vila-Comamala, Joan; Guizar-Sicairos, Manuel; Robinson, Ian K

2016-11-18

Phase retrieval is a long-standing problem in imaging when only the intensity of the wavefield can be recorded. Coherent diffraction imaging is a lensless technique that uses iterative algorithms to recover amplitude and phase contrast images from diffraction intensity data. For general samples, phase retrieval from a single-diffraction pattern has been an algorithmic and experimental challenge. Here we report a method of phase retrieval that uses a known modulation of the sample exit wave. This coherent modulation imaging method removes inherent ambiguities of coherent diffraction imaging and uses a reliable, rapidly converging iterative algorithm involving three planes. It works for extended samples, does not require tight support for convergence and relaxes dynamic range requirements on the detector. Coherent modulation imaging provides a robust method for imaging in materials and biological science, while its single-shot capability will benefit the investigation of dynamical processes with pulsed sources, such as X-ray free-electron lasers.

Electronic Photography

NASA Technical Reports Server (NTRS)

Payne, Meredith Lindsay

1995-01-01

The main objective was to assist in the production of electronic images in the Electronic Photography Lab (EPL). The EPL is a new facility serving the electronic photographic needs of the Langley community. The purpose of the Electronic Photography lab is to provide Langley with access to digital imaging technology. Although the EPL has been in operation for less than one year, almost 1,000 images have been produced. The decision to establish the lab was made after careful determination of the centers needs for electronic photography. The LaRC community requires electronic photography for the production of electronic printing, Web sites, desktop publications, and its increased enhancement capabilities. In addition to general use, other considerations went into the planning of the EPL. For example, electronic photography is much less of a burden on the environment compared to conventional photography. Also, the possibilities of an on-line database and retrieval system could make locating past work more efficient. Finally, information in an electronic image is quantified, making measurements and calculations easier for the researcher.

[Design and implementation of medical instrument standard information retrieval system based on APS.NET].

PubMed

Yu, Kaijun

2010-07-01

This paper Analys the design goals of Medical Instrumentation standard information retrieval system. Based on the B /S structure,we established a medical instrumentation standard retrieval system with ASP.NET C # programming language, IIS f Web server, SQL Server 2000 database, in the. NET environment. The paper also Introduces the system structure, retrieval system modules, system development environment and detailed design of the system.

Cry-Bt identifier: a biological database for PCR detection of Cry genes present in transgenic plants.

PubMed

Singh, Vinay Kumar; Ambwani, Sonu; Marla, Soma; Kumar, Anil

2009-10-23

We describe the development of a user friendly tool that would assist in the retrieval of information relating to Cry genes in transgenic crops. The tool also helps in detection of transformed Cry genes from Bacillus thuringiensis present in transgenic plants by providing suitable designed primers for PCR identification of these genes. The tool designed based on relational database model enables easy retrieval of information from the database with simple user queries. The tool also enables users to access related information about Cry genes present in various databases by interacting with different sources (nucleotide sequences, protein sequence, sequence comparison tools, published literature, conserved domains, evolutionary and structural data). http://insilicogenomics.in/Cry-btIdentifier/welcome.html.

Microcomputer Database Management Systems for Bibliographic Data.

ERIC Educational Resources Information Center

Pollard, Richard

1986-01-01

Discusses criteria for evaluating microcomputer database management systems (DBMS) used for storage and retrieval of bibliographic data. Two popular types of microcomputer DBMS--file management systems and relational database management systems--are evaluated with respect to these criteria. (Author/MBR)

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine

PubMed Central

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2016-01-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., “short” processing times and/or “large” datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply “large scale” processing transitions into “big data” and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging. PMID:28736473

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Why Save Your Course as a Relational Database?

ERIC Educational Resources Information Center

Hamilton, Gregory C.; Katz, David L.; Davis, James E.

2000-01-01

Describes a system that stores course materials for computer-based training programs in a relational database called Of Course! Outlines the basic structure of the databases; explains distinctions between Of Course! and other authoring languages; and describes how data is retrieved from the database and presented to the student. (Author/LRW)

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

NASA Astrophysics Data System (ADS)

Vasilkov, Alexander; Qin, Wenhan; Krotkov, Nickolay; Lamsal, Lok; Spurr, Robert; Haffner, David; Joiner, Joanna; Yang, Eun-Su; Marchenko, Sergey

2017-01-01

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Design of a graphical user interface for an intelligent multimedia information system for radiology research

NASA Astrophysics Data System (ADS)

Taira, Ricky K.; Wong, Clement; Johnson, David; Bhushan, Vikas; Rivera, Monica; Huang, Lu J.; Aberle, Denise R.; Cardenas, Alfonso F.; Chu, Wesley W.

1995-05-01

With the increase in the volume and distribution of images and text available in PACS and medical electronic health-care environments it becomes increasingly important to maintain indexes that summarize the content of these multi-media documents. Such indices are necessary to quickly locate relevant patient cases for research, patient management, and teaching. The goal of this project is to develop an intelligent document retrieval system that allows researchers to request for patient cases based on document content. Thus we wish to retrieve patient cases from electronic information archives that could include a combined specification of patient demographics, low level radiologic findings (size, shape, number), intermediate-level radiologic findings (e.g., atelectasis, infiltrates, etc.) and/or high-level pathology constraints (e.g., well-differentiated small cell carcinoma). The cases could be distributed among multiple heterogeneous databases such as PACS, RIS, and HIS. Content- based retrieval systems go beyond the capabilities of simple key-word or string-based retrieval matching systems. These systems require a knowledge base to comprehend the generality/specificity of a concept (thus knowing the subclasses or related concepts to a given concept) and knowledge of the various string representations for each concept (i.e., synonyms, lexical variants, etc.). We have previously reported on a data integration mediation layer that allows transparent access to multiple heterogeneous distributed medical databases (HIS, RIS, and PACS). The data access layer of our architecture currently has limited query processing capabilities. Given a patient hospital identification number, the access mediation layer collects all documents in RIS and HIS and returns this information to a specified workstation location. In this paper we report on our efforts to extend the query processing capabilities of the system by creation of custom query interfaces, an intelligent query processing engine, and a document-content index that can be generated automatically (i.e., no manual authoring or changes to the normal clinical protocols).

Applying Advances in GPM Radiometer Intercalibration and Algorithm Development to a Long-Term TRMM/GPM Global Precipitation Dataset

NASA Astrophysics Data System (ADS)

Berg, W. K.

2016-12-01

The Global Precipitation Mission (GPM) Core Observatory, which was launched in February of 2014, provides a number of advances for satellite monitoring of precipitation including a dual-frequency radar, high frequency channels on the GPM Microwave Imager (GMI), and coverage over middle and high latitudes. The GPM concept, however, is about producing unified precipitation retrievals from a constellation of microwave radiometers to provide approximately 3-hourly global sampling. This involves intercalibration of the input brightness temperatures from the constellation radiometers, development of an apriori precipitation database using observations from the state-of-the-art GPM radiometer and radars, and accounting for sensor differences in the retrieval algorithm in a physically-consistent way. Efforts by the GPM inter-satellite calibration working group, or XCAL team, and the radiometer algorithm team to create unified precipitation retrievals from the GPM radiometer constellation were fully implemented into the current version 4 GPM precipitation products. These include precipitation estimates from a total of seven conical-scanning and six cross-track scanning radiometers as well as high spatial and temporal resolution global level 3 gridded products. Work is now underway to extend this unified constellation-based approach to the combined TRMM/GPM data record starting in late 1997. The goal is to create a long-term global precipitation dataset employing these state-of-the-art calibration and retrieval algorithm approaches. This new long-term global precipitation dataset will incorporate the physics provided by the combined GPM GMI and DPR sensors into the apriori database, extend prior TRMM constellation observations to high latitudes, and expand the available TRMM precipitation data to the full constellation of available conical and cross-track scanning radiometers. This combined TRMM/GPM precipitation data record will thus provide a high-quality high-temporal resolution global dataset for use in a wide variety of weather and climate research applications.

Accounting for the Effects of Surface BRDF on Satellite Cloud and Trace-Gas Retrievals: A New Approach Based on Geometry-Dependent Lambertian-Equivalent Reflectivity Applied to OMI Algorithms

NASA Technical Reports Server (NTRS)

Vasilkov, Alexander; Qin, Wenhan; Krotkov, Nickolay; Lamsal, Lok; Spurr, Robert; Haffner, David; Joiner, Joanna; Yang, Eun-Su; Marchenko, Sergey

2017-01-01

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50% in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Improvement of Aerosol Optical Depth Retrieval from MODIS Spectral Reflectance over the Global Ocean Using New Aerosol Models Archived from AERONET Inversion Data and Tri-axial Ellipsoidal Dust Database

NASA Technical Reports Server (NTRS)

Lee, J.; Kim, J.; Yang, P.; Hsu, N. C.

2012-01-01

New over-ocean aerosol models are developed by integrating the inversion data from the Aerosol Robotic Network (AERONET) sun/sky radiometers with a database for the optical properties of tri-axial ellipsoid particles. The new aerosol models allow more accurate retrieval of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the case of high AOD (AOD greater than 0.3). The aerosol models are categorized by using the fine-mode fraction (FMF) at 550 nm and the singlescattering albedo (SSA) at 440 nm from the AERONET inversion data to include a variety of aerosol types found around the globe. For each aerosol model, the changes in the aerosol optical properties (AOPs) as functions of AOD are considered to better represent aerosol characteristics. Comparisons of AODs between AERONET and MODIS for the period from 2003 to 2010 show that the use of the new aerosol models enhances the AOD accuracy with a Pearson coefficient of 0.93 and a regression slope of 0.99 compared to 0.92 and 0.85 calculated using the MODIS Collection 5 data. Moreover, the percentage of data within an expected error of +/-(0.03 + 0.05xAOD) is increased from 62 percent to 64 percent for overall data and from 39 percent to 51 percent for AOD greater than 0.3. Errors in the retrieved AOD are further characterized with respect to the Angstrom exponent (AE), scattering angle, SSA, and air mass factor (AMF). Due to more realistic AOPs assumptions, the new algorithm generally reduces systematic errors in the retrieved AODs compared with the current operational algorithm. In particular, the underestimation of fine-dominated AOD and the scattering angle dependence of dust-dominated AOD are significantly mitigated as results of the new algorithm's improved treatment of aerosol size distribution and dust particle nonsphericity.

Measuring and Predicting Tag Importance for Image Retrieval.

PubMed

Li, Shangwen; Purushotham, Sanjay; Chen, Chen; Ren, Yuzhuo; Kuo, C-C Jay

2017-12-01

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

Classifying magnetic resonance image modalities with convolutional neural networks

NASA Astrophysics Data System (ADS)

Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

2018-02-01

Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

Comparison of k-means related clustering methods for nuclear medicine images segmentation

NASA Astrophysics Data System (ADS)

Borys, Damian; Bzowski, Pawel; Danch-Wierzchowska, Marta; Psiuk-Maksymowicz, Krzysztof

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Is More Always Better?

ERIC Educational Resources Information Center

Bell, Steven J.

2003-01-01

Discusses full-text databases and whether existing aggregator databases are meeting user needs. Topics include the need for better search interfaces; concepts of quality research and information retrieval; information overload; full text in electronic journal collections versus aggregator databases; underrepresentation of certain disciplines; and…

An evaluation of information retrieval accuracy with simulated OCR output

DOE Office of Scientific and Technical Information (OSTI.GOV)

Croft, W.B.; Harding, S.M.; Taghva, K.

Optical Character Recognition (OCR) is a critical part of many text-based applications. Although some commercial systems use the output from OCR devices to index documents without editing, there is very little quantitative data on the impact of OCR errors on the accuracy of a text retrieval system. Because of the difficulty of constructing test collections to obtain this data, we have carried out evaluation using simulated OCR output on a variety of databases. The results show that high quality OCR devices have little effect on the accuracy of retrieval, but low quality devices used with databases of short documents canmore » result in significant degradation.« less

Fast perceptual image hash based on cascade algorithm

NASA Astrophysics Data System (ADS)

Ruchay, Alexey; Kober, Vitaly; Yavtushenko, Evgeniya

2017-09-01

In this paper, we propose a perceptual image hash algorithm based on cascade algorithm, which can be applied in image authentication, retrieval, and indexing. Image perceptual hash uses for image retrieval in sense of human perception against distortions caused by compression, noise, common signal processing and geometrical modifications. The main disadvantage of perceptual hash is high time expenses. In the proposed cascade algorithm of image retrieval initializes with short hashes, and then a full hash is applied to the processed results. Computer simulation results show that the proposed hash algorithm yields a good performance in terms of robustness, discriminability, and time expenses.

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

PubMed Central

Ali, Nouman; Bajwa, Khalid Bashir; Sablatnig, Robert; Chatzichristofis, Savvas A.; Iqbal, Zeshan; Rashid, Muhammad; Habib, Hafiz Adnan

2016-01-01

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration. PMID:27315101

Incomplete evidence: the inadequacy of databases in tracing published adverse drug reactions in clinical trials

PubMed Central

Derry, Sheena; Kong Loke, Yoon; Aronson, Jeffrey K

2001-01-01

Background We would expect information on adverse drug reactions in randomised clinical trials to be easily retrievable from specific searches of electronic databases. However, complete retrieval of such information may not be straightforward, for two reasons. First, not all clinical drug trials provide data on the frequency of adverse effects. Secondly, not all electronic records of trials include terms in the abstract or indexing fields that enable us to select those with adverse effects data. We have determined how often automated search methods, using indexing terms and/or textwords in the title or abstract, would fail to retrieve trials with adverse effects data. Methods We used a sample set of 107 trials known to report frequencies of adverse drug effects, and measured the proportion that (i) were not assigned the appropriate adverse effects indexing terms in the electronic databases, and (ii) did not contain identifiable adverse effects textwords in the title or abstract. Results Of the 81 trials with records on both MEDLINE and EMBASE, 25 were not indexed for adverse effects in either database. Twenty-six trials were indexed in one database but not the other. Only 66 of the 107 trials reporting adverse effects data mentioned this in the abstract or title of the paper. Simultaneous use of textword and indexing terms retrieved only 82/107 (77%) papers. Conclusions Specific search strategies based on adverse effects textwords and indexing terms will fail to identify nearly a quarter of trials that report on the rate of drug adverse effects. PMID:11591220

A Holistic, Similarity-Based Approach for Personalized Ranking in Web Databases

ERIC Educational Resources Information Center

Telang, Aditya

2011-01-01

With the advent of the Web, the notion of "information retrieval" has acquired a completely new connotation and currently encompasses several disciplines ranging from traditional forms of text and data retrieval in unstructured and structured repositories to retrieval of static and dynamic information from the contents of the surface and deep Web.…

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval.

PubMed

Rahman, Md Mahmudur; Antani, Sameer K; Demner-Fushman, Dina; Thoma, George R

2015-10-01

This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term "concept" refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature.

Biomedical image representation approach using visualness and spatial information in a concept feature space for interactive region-of-interest-based retrieval

PubMed Central

Rahman, Md. Mahmudur; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-01-01

Abstract. This article presents an approach to biomedical image retrieval by mapping image regions to local concepts where images are represented in a weighted entropy-based concept feature space. The term “concept” refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as the Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist the user in interactively selecting a region-of-interest (ROI) and searching for similar image ROIs. Further, a spatial verification step is used as a postprocessing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval is validated through experiments on two different data sets, which are collected from open access biomedical literature. PMID:26730398

Secure annotation for medical images based on reversible watermarking in the Integer Fibonacci-Haar transform domain

NASA Astrophysics Data System (ADS)

Battisti, F.; Carli, M.; Neri, A.

2011-03-01

The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet transform, the Integer Fibonacci-Haar transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.

Effects of Internal and External Vividness on Hippocampal Connectivity during Memory Retrieval

PubMed Central

Ford, Jaclyn H.; Kensinger, Elizabeth A.

2016-01-01

Successful memory for an image can be supported by retrieval of one’s personal reaction to the image (i.e., internal vividness), as well as retrieval of the specific details of the image itself (i.e., external vividness). Prior research suggests that memory vividness relies on regions within the medial temporal lobe, particularly the hippocampus, but it is unclear whether internal and external vividness are supported by the hippocampus in a similar way. To address this open question, the current study examined hippocampal connectivity associated with enhanced internal and external vividness ratings during retrieval. Participants encoded complex visual images paired with verbal titles. During a scanned retrieval session, they were presented with the titles and asked whether each had been seen with an image during encoding. Following retrieval of each image, participants were asked to rate internal and external vividness. Increased hippocampal activity was associated with higher vividness ratings for both scales, supporting prior evidence implicating the hippocampus in retrieval of memory detail. However, different patterns of hippocampal connectivity related to enhanced external and internal vividness. Further, hippocampal connectivity with medial prefrontal regions was associated with increased ratings of internal vividness, but with decreased ratings of external vividness. These findings suggest that the hippocampus may contribute to increased internal and external vividness via distinct mechanisms and that external and internal vividness of memories should be considered as separable measures. PMID:26778653

Validation of Cloud Properties From Multiple Satellites Using CALIOP Data

NASA Technical Reports Server (NTRS)

Yost, Christopher R.; Minnis, Patrick; Bedka, Kristopher M.; Heck, Patrick W.; Palikonda, Rabindra; Sun-Mack, Sunny; Trepte, Qing

2016-01-01

The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

Improving data management and dissemination in web based information systems by semantic enrichment of descriptive data aspects

NASA Astrophysics Data System (ADS)

Gebhardt, Steffen; Wehrmann, Thilo; Klinger, Verena; Schettler, Ingo; Huth, Juliane; Künzer, Claudia; Dech, Stefan

2010-10-01

The German-Vietnamese water-related information system for the Mekong Delta (WISDOM) project supports business processes in Integrated Water Resources Management in Vietnam. Multiple disciplines bring together earth and ground based observation themes, such as environmental monitoring, water management, demographics, economy, information technology, and infrastructural systems. This paper introduces the components of the web-based WISDOM system including data, logic and presentation tier. It focuses on the data models upon which the database management system is built, including techniques for tagging or linking metadata with the stored information. The model also uses ordered groupings of spatial, thematic and temporal reference objects to semantically tag datasets to enable fast data retrieval, such as finding all data in a specific administrative unit belonging to a specific theme. A spatial database extension is employed by the PostgreSQL database. This object-oriented database was chosen over a relational database to tag spatial objects to tabular data, improving the retrieval of census and observational data at regional, provincial, and local areas. While the spatial database hinders processing raster data, a "work-around" was built into WISDOM to permit efficient management of both raster and vector data. The data model also incorporates styling aspects of the spatial datasets through styled layer descriptions (SLD) and web mapping service (WMS) layer specifications, allowing retrieval of rendered maps. Metadata elements of the spatial data are based on the ISO19115 standard. XML structured information of the SLD and metadata are stored in an XML database. The data models and the data management system are robust for managing the large quantity of spatial objects, sensor observations, census and document data. The operational WISDOM information system prototype contains modules for data management, automatic data integration, and web services for data retrieval, analysis, and distribution. The graphical user interfaces facilitate metadata cataloguing, data warehousing, web sensor data analysis and thematic mapping.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Indexing and retrieving point and region objects

NASA Astrophysics Data System (ADS)

Ibrahim, Azzam T.; Fotouhi, Farshad A.

1996-03-01

R-tree and its variants are examples of spatial data structures for paged-secondary memory. To process a query, these structures require multiple path traversals. In this paper, we present a new image access method, SB+-tree which requires a single path traversal to process a query. Also, SB+-tree will allow commercial databases an access method for spatial objects without a major change, since most commercial databases already support B+-tree as an access method for text data. The SB+-tree can be used for zero and non-zero size data objects. Non-zero size objects are approximated by their minimum bounding rectangles (MBRs). The number of SB+-trees generated is dependent upon the number of dimensions of the approximation of the object. The structure supports efficient spatial operations such as regions-overlap, distance and direction. In this paper, we experimentally and analytically demonstrate the superiority of SB+-tree over R-tree.

A Strategy for Reusing the Data of Electronic Medical Record Systems for Clinical Research.

PubMed

Matsumura, Yasushi; Hattori, Atsushi; Manabe, Shiro; Tsuda, Tsutomu; Takeda, Toshihiro; Okada, Katsuki; Murata, Taizo; Mihara, Naoki

2016-01-01

There is a great need to reuse data stored in electronic medical records (EMR) databases for clinical research. We previously reported the development of a system in which progress notes and case report forms (CRFs) were simultaneously recorded using a template in the EMR in order to exclude redundant data entry. To make the data collection process more efficient, we are developing a system in which the data originally stored in the EMR database can be populated within a frame in a template. We developed interface plugin modules that retrieve data from the databases of other EMR applications. A universal keyword written in a template master is converted to a local code using a data conversion table, then the objective data is retrieved from the corresponding database. The template element data, which are entered by a template, are stored in the template element database. To retrieve the data entered by other templates, the objective data is designated by the template element code with the template code, or by the concept code if it is written for the element. When the application systems in the EMR generate documents, they also generate a PDF file and a corresponding document profile XML, which includes important data, and send them to the document archive server and the data sharing saver, respectively. In the data sharing server, the data are represented by an item with an item code with a document class code and its value. By linking a concept code to an item identifier, an objective data can be retrieved by designating a concept code. We employed a flexible strategy in which a unique identifier for a hospital is initially attached to all of the data that the hospital generates. The identifier is secondarily linked with concept codes. The data that are not linked with a concept code can also be retrieved using the unique identifier of the hospital. This strategy makes it possible to reuse any of a hospital's data.

Diversification of visual media retrieval results using saliency detection

NASA Astrophysics Data System (ADS)

Muratov, Oleg; Boato, Giulia; De Natale, Franesco G. B.

2013-03-01

Diversification of retrieval results allows for better and faster search. Recently there has been proposed different methods for diversification of image retrieval results mainly utilizing text information and techniques imported from natural language processing domain. However, images contain visual information that is impossible to describe in text and the use of visual features is inevitable. Visual saliency is information about the main object of an image implicitly included by humans while creating visual content. For this reason it is naturally to exploit this information for the task of diversification of the content. In this work we study whether visual saliency can be used for the task of diversification and propose a method for re-ranking image retrieval results using saliency. The evaluation has shown that the use of saliency information results in higher diversity of retrieval results.

The Cystic Fibrosis Database: Content and Research Opportunities.

ERIC Educational Resources Information Center

Shaw, William M., Jr.; And Others

1991-01-01

Describes the files contained in the Cystic Fibrosis (CF) database and discusses educational and research opportunities using this database. Topics discussed include queries, evaluating the relevance of items retrieved, and use of the database in an online searching course in the School of Information and Library Science at the University of North…

Scientific Communication of Geochemical Data and the Use of Computer Databases.

ERIC Educational Resources Information Center

Le Bas, M. J.; Durham, J.

1989-01-01

Describes a scheme in the United Kingdom that coordinates geochemistry publications with a computerized geochemistry database. The database comprises not only data published in the journals but also the remainder of the pertinent data set. The discussion covers the database design; collection, storage and retrieval of data; and plans for future…

Selecting Data-Base Management Software for Microcomputers in Libraries and Information Units.

ERIC Educational Resources Information Center

Pieska, K. A. O.

1986-01-01

Presents a model for the evaluation of database management systems software from the viewpoint of librarians and information specialists. The properties of data management systems, database management systems, and text retrieval systems are outlined and compared. (10 references) (CLB)

Knowledge Discovery in Databases.

ERIC Educational Resources Information Center

Norton, M. Jay

1999-01-01

Knowledge discovery in databases (KDD) revolves around the investigation and creation of knowledge, processes, algorithms, and mechanisms for retrieving knowledge from data collections. The article is an introductory overview of KDD. The rationale and environment of its development and applications are discussed. Issues related to database design…

Sagace: A web-based search engine for biomedical databases in Japan

PubMed Central

2012-01-01

Background In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. Findings We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. Conclusions Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/. PMID:23110816

Citation searching: a systematic review case study of multiple risk behaviour interventions

PubMed Central

2014-01-01

Background The value of citation searches as part of the systematic review process is currently unknown. While the major guides to conducting systematic reviews state that citation searching should be carried out in addition to searching bibliographic databases there are still few studies in the literature that support this view. Rather than using a predefined search strategy to retrieve studies, citation searching uses known relevant papers to identify further papers. Methods We describe a case study about the effectiveness of using the citation sources Google Scholar, Scopus, Web of Science and OVIDSP MEDLINE to identify records for inclusion in a systematic review. We used the 40 included studies identified by traditional database searches from one systematic review of interventions for multiple risk behaviours. We searched for each of the included studies in the four citation sources to retrieve the details of all papers that have cited these studies. We carried out two analyses; the first was to examine the overlap between the four citation sources to identify which citation tool was the most useful; the second was to investigate whether the citation searches identified any relevant records in addition to those retrieved by the original database searches. Results The highest number of citations was retrieved from Google Scholar (1680), followed by Scopus (1173), then Web of Science (1095) and lastly OVIDSP (213). To retrieve all the records identified by the citation tracking searching all four resources was required. Google Scholar identified the highest number of unique citations. The citation tracking identified 9 studies that met the review’s inclusion criteria. Eight of these had already been identified by the traditional databases searches and identified in the screening process while the ninth was not available in any of the databases when the original searches were carried out. It would, however, have been identified by two of the database search strategies if searches had been carried out later. Conclusions Based on the results from this investigation, citation searching as a supplementary search method for systematic reviews may not be the best use of valuable time and resources. It would be useful to verify these findings in other reviews. PMID:24893958

Quantitative approach for optimizing e-beam condition of photoresist inspection and measurement

NASA Astrophysics Data System (ADS)

Lin, Chia-Jen; Teng, Chia-Hao; Cheng, Po-Chung; Sato, Yoshishige; Huang, Shang-Chieh; Chen, Chu-En; Maruyama, Kotaro; Yamazaki, Yuichiro

2018-03-01

Severe process margin in advanced technology node of semiconductor device is controlled by e-beam metrology system and e-beam inspection system with scanning electron microscopy (SEM) image. By using SEM, larger area image with higher image quality is required to collect massive amount of data for metrology and to detect defect in a large area for inspection. Although photoresist is the one of the critical process in semiconductor device manufacturing, observing photoresist pattern by SEM image is crucial and troublesome especially in the case of large image. The charging effect by e-beam irradiation on photoresist pattern causes deterioration of image quality, and it affect CD variation on metrology system and causes difficulties to continue defect inspection in a long time for a large area. In this study, we established a quantitative approach for optimizing e-beam condition with "Die to Database" algorithm of NGR3500 on photoresist pattern to minimize charging effect. And we enhanced the performance of measurement and inspection on photoresist pattern by using optimized e-beam condition. NGR3500 is the geometry verification system based on "Die to Database" algorithm which compares SEM image with design data [1]. By comparing SEM image and design data, key performance indicator (KPI) of SEM image such as "Sharpness", "S/N", "Gray level variation in FOV", "Image shift" can be retrieved. These KPIs were analyzed with different e-beam conditions which consist of "Landing Energy", "Probe Current", "Scanning Speed" and "Scanning Method", and the best e-beam condition could be achieved with maximum image quality, maximum scanning speed and minimum image shift. On this quantitative approach of optimizing e-beam condition, we could observe dependency of SEM condition on photoresist charging. By using optimized e-beam condition, measurement could be continued on photoresist pattern over 24 hours stably. KPIs of SEM image proved image quality during measurement and inspection was stabled enough.

ASIST 2001. Information in a Networked World: Harnessing the Flow. Part III: Poster Presentations.

ERIC Educational Resources Information Center

Proceedings of the ASIST Annual Meeting, 2001

2001-01-01

Topics of Poster Presentations include: electronic preprints; intranets; poster session abstracts; metadata; information retrieval; watermark images; video games; distributed information retrieval; subject domain knowledge; data mining; information theory; course development; historians' use of pictorial images; information retrieval software;…

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C.

2013-01-01

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

X-Ray Phase Imaging for Breast Cancer Detection

DTIC Science & Technology

2010-09-01

regularization seeks the minimum- norm , least squares solution for phase retrieval. The retrieval result with Tikhonov regularization is still unsatisfactory...of norm , that can effectively reflect the accuracy of the retrieved data as an image, if ‖δ Ik+1−δ Ik‖ is less than a predefined threshold value β...pointed out that the proper norm for images is the total variation (TV) norm , which is the L1 norm of the gradient of the image function, and not the

Maintaining Multimedia Data in a Geospatial Database

DTIC Science & Technology

2012-09-01

at PostgreSQL and MySQL as spatial databases was offered. Given their results, as each database produced result sets from zero to 100,000, it was...excelled given multiple conditions. A different look at PostgreSQL and MySQL as spatial databases was offered. Given their results, as each database... MySQL ................................................................................................14  B.  BENCHMARKING DATA RETRIEVED FROM TABLE

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

PubMed

Zheng, Liang; Yang, Yi; Tian, Qi

2018-05-01

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

Distribution to the Astronomy Community of the Compressed Digitized Sky Survey

NASA Astrophysics Data System (ADS)

Postman, Marc

1996-03-01

The Space Telescope Science Institute has compressed an all-sky collection of ground-based images and has printed the data on a two volume, 102 CD-ROM disc set. The first part of the survey (containing images of the southern sky) was published in May 1994. The second volume (containing images of the northern sky) was published in January 1995. Software which manages the image retrieval is included with each volume. The Astronomical Society of the Pacific (ASP) is handling the distribution of the lOx compressed data and has sold 310 sets as of October 1996. ASP is also handling the distribution of the recently published 100x version of the northern sky survey which is publicly available at a low cost. The target markets for the 100x compressed data set are the amateur astronomy community, educational institutions, and the general public. During the next year, we plan to publish the first version of a photometric calibration database which will allow users of the compressed sky survey to determine the brightness of stars in the images.

Distribution to the Astronomy Community of the Compressed Digitized Sky Survey

NASA Technical Reports Server (NTRS)

Postman, Marc

1996-01-01

The Space Telescope Science Institute has compressed an all-sky collection of ground-based images and has printed the data on a two volume, 102 CD-ROM disc set. The first part of the survey (containing images of the southern sky) was published in May 1994. The second volume (containing images of the northern sky) was published in January 1995. Software which manages the image retrieval is included with each volume. The Astronomical Society of the Pacific (ASP) is handling the distribution of the lOx compressed data and has sold 310 sets as of October 1996. ASP is also handling the distribution of the recently published 100x version of the northern sky survey which is publicly available at a low cost. The target markets for the 100x compressed data set are the amateur astronomy community, educational institutions, and the general public. During the next year, we plan to publish the first version of a photometric calibration database which will allow users of the compressed sky survey to determine the brightness of stars in the images.

Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms.

PubMed

Geradts, Z J; Bijhold, J; Hermsen, R; Murtagh, F

2001-06-01

On the market several systems exist for collecting spent ammunition data for forensic investigation. These databases store images of cartridge cases and the marks on them. Image matching is used to create hit lists that show which marks on a cartridge case are most similar to another cartridge case. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for optimizing the results of image matching. The images are acquired by side light images for the breech face marks and by ring light for the firing pin impression. For these images a standard way of digitizing the images used. For the side light images and ring light images this means that the user has to position the cartridge case in the same position according to a protocol. The positioning is important for the sidelight, since the image that is obtained of a striation mark depends heavily on the angle of incidence of the light. In practice, it appears that the user positions the cartridge case with +/-10 degrees accuracy. We tested our algorithms using 49 cartridge cases of 19 different firearms, where the examiner determined that they were shot with the same firearm. For testing, these images were mixed with a database consisting of approximately 4900 images that were available from the Drugfire database of different calibers.In cases where the registration and the light conditions among those matching pairs was good, a simple computation of the standard deviation of the subtracted gray levels, delivered the best-matched images. For images that were rotated and shifted, we have implemented a "brute force" way of registration. The images are translated and rotated until the minimum of the standard deviation of the difference is found. This method did not result in all relevant matches in the top position. This is caused by the effect that shadows and highlights are compared in intensity. Since the angle of incidence of the light will give a different intensity profile, this method is not optimal. For this reason a preprocessing of the images was required. It appeared that the third scale of the "à trous" wavelet transform gives the best results in combination with brute force. Matching the contents of the images is less sensitive to the variation of the lighting. The problem with the brute force method is however that the time for calculation for 49 cartridge cases to compare between them, takes over 1 month of computing time on a Pentium II-computer with 333MHz. For this reason a faster approach is implemented: correlation in log polar coordinates. This gave similar results as the brute force calculation, however it was computed in 24h for a complete database with 4900 images.A fast pre-selection method based on signatures is carried out that is based on the Kanade Lucas Tomasi (KLT) equation. The positions of the points computed with this method are compared. In this way, 11 of the 49 images were in the top position in combination with the third scale of the à trous equation. It depends however on the light conditions and the prominence of the marks if correct matches are found in the top ranked position. All images were retrieved in the top 5% of the database. This method takes only a few minutes for the complete database if, and can be optimized for comparison in seconds if the location of points are stored in files. For further improvement, it is useful to have the refinement in which the user selects the areas that are relevant on the cartridge case for their marks. This is necessary if this cartridge case is damaged and other marks that are not from the firearm appear on it.

Estimation of Land Surface Energy Balance Using Satellite Data of Spatial Reduced Resolution

NASA Astrophysics Data System (ADS)

Vintila, Ruxandra; Radnea, Cristina; Savin, Elena; Poenaru, Violeta

2010-12-01

The paper presents preliminary results concerning the monitoring at national level of several geo-biophysical variables retrieved by remote sensing, in particular those related to drought or aridisation. The study, which is in progress, represents also an exercise for to the implementation of a Land Monitoring Core Service for Romania, according to the Kopernikus Program and in compliance with the INSPIRE Directive. The SEBS model has been used to retrieve land surface energy balance variables, such as turbulent heat fluxes, evaporative fraction and daily evaporation, based on three information types: (1) surface albedo, emissivity, temperature, fraction of vegetation cover (fCover), leaf area index (LAI) and vegetation height; (2) air pressure, temperature, humidity and wind speed at the planetary boundary layer (PBL) height; (3) downward solar radiation and downward longwave radiation. AATSR and MERIS archived reprocessed images have provided several types of information. Thus, surface albedo, emissivity, and land surface temperature have been retrieved from AATSR, while LAI and fCover have been estimated from MERIS. The vegetation height has been derived from CORINE Land Cover and PELCOM Land Use databases, while the meteorological information at the height of PBL have been estimated from the measurements provided by the national weather station network. Other sources of data used during this study have been the GETASSE30 digital elevation model with 30" spatial resolution, used for satellite image orthorectification, and the SIGSTAR-200 geographical information system of soil resources of Romania, used for water deficit characterisation. The study will continue by processing other AATSR and MERIS archived images, complemented by the validation of SEBS results with ground data collected on the most important biomes for Romania at various phenological stages, and the transformation of evaporation / evapotranspiration into a drought index using the soil texture data. It is also foreseen to develop procedures for processing near-real time AATSR and MERIS images from the rolling archives, as well as procedures for dealing with SENTINEL 3 images in the future, for timely delivery of reliable information to authorities and planning for drought to reduce its effects on citizens.

Two-dimensional thermography image retrieval from zig-zag scanned data with TZ-SCAN

NASA Astrophysics Data System (ADS)

Okumura, Hiroshi; Yamasaki, Ryohei; Arai, Kohei

2008-10-01

TZ-SCAN is a simple and low cost thermal imaging device which consists of a single point radiation thermometer on a tripod with a pan-tilt rotator, a DC motor controller board with a USB interface, and a laptop computer for rotator control, data acquisition, and data processing. TZ-SCAN acquires a series of zig-zag scanned data and stores the data as CSV file. A 2-D thermal distribution image can be retrieved by using the second quefrency peak calculated from TZ-SCAN data. An experiment is conducted to confirm the validity of the thermal retrieval algorithm. The experimental result shows efficient accuracy for 2-D thermal distribution image retrieval.

The Influence of Spatial Resolutions on the Retrieval Accuracy of Sea Surface Wind Speed with Cross-polarized C-band SAR images

NASA Astrophysics Data System (ADS)

Zhang, K.; Han, B.; Mansaray, L. R.; Xu, X.; Guo, Q.; Jingfeng, H.

2017-12-01

Synthetic aperture radar (SAR) instruments on board satellites are valuable for high-resolution wind field mapping, especially for coastal studies. Since the launch of Sentinel-1A on April 3, 2014, followed by Sentinel-1B on April 25, 2016, large amount of C-band SAR data have been added to a growing accumulation of SAR datasets (ERS-1/2, RADARSAT-1/2, ENVISAT). These new developments are of great significance for a wide range of applications in coastal sea areas, especially for high spatial resolution wind resource assessment, in which the accuracy of retrieved wind fields is extremely crucial. Recently, it is reported that wind speeds can also be retrieved from C-band cross-polarized SAR images, which is an important complement to wind speed retrieval from co-polarization. However, there is no consensus on the optimal resolution for wind speed retrieval from cross-polarized SAR images. This paper presents a comparison strategy for investigating the influence of spatial resolutions on sea surface wind speed retrieval accuracy with cross-polarized SAR images. Firstly, for wind speeds retrieved from VV-polarized images, the optimal geophysical C-band model (CMOD) function was selected among four CMOD functions. Secondly, the most suitable C-band cross-polarized ocean (C-2PO) model was selected between two C-2POs for the VH-polarized image dataset. Then, the VH-wind speeds retrieved by the selected C-2PO were compared with the VV-polarized sea surface wind speeds retrieved using the optimal CMOD, which served as reference, at different spatial resolutions. Results show that the VH-polarized wind speed retrieval accuracy increases rapidly with the decrease in spatial resolutions from 100 m to 1000 m, with a drop in RMSE of 42%. However, the improvement in wind speed retrieval accuracy levels off with spatial resolutions decreasing from 1000 m to 5000 m. This demonstrates that the pixel spacing of 1 km may be the compromising choice for the tradeoff between the spatial resolution and wind speed retrieval accuracy with cross-polarized images obtained from RADASAT-2 fine quad polarization mode. Figs. 1 illustrate the variation of the following statistical parameters: Bias, Corr, R2, RMSE and STD as a function of spatial resolution.

An Abstraction-Based Data Model for Information Retrieval

NASA Astrophysics Data System (ADS)

McAllister, Richard A.; Angryk, Rafal A.

Language ontologies provide an avenue for automated lexical analysis that may be used to supplement existing information retrieval methods. This paper presents a method of information retrieval that takes advantage of WordNet, a lexical database, to generate paths of abstraction, and uses them as the basis for an inverted index structure to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research on using ontologies to perform word-sense disambiguation and improve the precision of existing information retrieval techniques.

Generating PubMed Chemical Queries for Consumer Health Literature

PubMed Central

Loo, Jeffery; Chang, Hua Florence; Hochstein, Colette; Sun, Ying

2005-01-01

Two popular NLM resources that provide information for consumers about chemicals and their safety are the Household Products Database and Haz-Map. Search queries to PubMed via web links were generated from these databases. The query retrieves consumer health-oriented literature about adverse effects of chemicals. The retrieval was limited to a manageable set of 20 to 60 citations, achieved by successively applying increasing limits to the search until the desired number of references was reached. PMID:16779322

Gram-Schmidt orthonormalization for retrieval of amplitude images under sinusoidal patterns of illumination

USDA-ARS?s Scientific Manuscript database

Structured illumination using sinusoidal patterns has been utilized for optical imaging of biological tissues in biomedical research and, of horticultural products. Implementation of structured-illumination imaging relies on retrieval of amplitude images, which is conventionally achieved by a phase-...

Clinical results of HIS, RIS, PACS integration using data integration CASE tools

NASA Astrophysics Data System (ADS)

Taira, Ricky K.; Chan, Hing-Ming; Breant, Claudine M.; Huang, Lu J.; Valentino, Daniel J.

1995-05-01

Current infrastructure research in PACS is dominated by the development of communication networks (local area networks, teleradiology, ATM networks, etc.), multimedia display workstations, and hierarchical image storage architectures. However, limited work has been performed on developing flexible, expansible, and intelligent information processing architectures for the vast decentralized image and text data repositories prevalent in healthcare environments. Patient information is often distributed among multiple data management systems. Current large-scale efforts to integrate medical information and knowledge sources have been costly with limited retrieval functionality. Software integration strategies to unify distributed data and knowledge sources is still lacking commercially. Systems heterogeneity (i.e., differences in hardware platforms, communication protocols, database management software, nomenclature, etc.) is at the heart of the problem and is unlikely to be standardized in the near future. In this paper, we demonstrate the use of newly available CASE (computer- aided software engineering) tools to rapidly integrate HIS, RIS, and PACS information systems. The advantages of these tools include fast development time (low-level code is generated from graphical specifications), and easy system maintenance (excellent documentation, easy to perform changes, and centralized code repository in an object-oriented database). The CASE tools are used to develop and manage the `middle-ware' in our client- mediator-serve architecture for systems integration. Our architecture is scalable and can accommodate heterogeneous database and communication protocols.

Interactive content-based image retrieval (CBIR) computer-aided diagnosis (CADx) system for ultrasound breast masses using relevance feedback

NASA Astrophysics Data System (ADS)

Cho, Hyun-chong; Hadjiiski, Lubomir; Sahiner, Berkman; Chan, Heang-Ping; Paramagul, Chintana; Helvie, Mark; Nees, Alexis V.

2012-03-01

We designed a Content-Based Image Retrieval (CBIR) Computer-Aided Diagnosis (CADx) system to assist radiologists in characterizing masses on ultrasound images. The CADx system retrieves masses that are similar to a query mass from a reference library based on computer-extracted features that describe texture, width-to-height ratio, and posterior shadowing of a mass. Retrieval is performed with k nearest neighbor (k-NN) method using Euclidean distance similarity measure and Rocchio relevance feedback algorithm (RRF). In this study, we evaluated the similarity between the query and the retrieved masses with relevance feedback using our interactive CBIR CADx system. The similarity assessment and feedback were provided by experienced radiologists' visual judgment. For training the RRF parameters, similarities of 1891 image pairs obtained from 62 masses were rated by 3 MQSA radiologists using a 9-point scale (9=most similar). A leave-one-out method was used in training. For each query mass, 5 most similar masses were retrieved from the reference library using radiologists' similarity ratings, which were then used by RRF to retrieve another 5 masses for the same query. The best RRF parameters were chosen based on three simulated observer experiments, each of which used one of the radiologists' ratings for retrieval and relevance feedback. For testing, 100 independent query masses on 100 images and 121 reference masses on 230 images were collected. Three radiologists rated the similarity between the query and the computer-retrieved masses. Average similarity ratings without and with RRF were 5.39 and 5.64 on the training set and 5.78 and 6.02 on the test set, respectively. The average Az values without and with RRF were 0.86+/-0.03 and 0.87+/-0.03 on the training set and 0.91+/-0.03 and 0.90+/-0.03 on the test set, respectively. This study demonstrated that RRF improved the similarity of the retrieved masses.

Database in Artificial Intelligence.

ERIC Educational Resources Information Center

Wilkinson, Julia

1986-01-01

Describes a specialist bibliographic database of literature in the field of artificial intelligence created by the Turing Institute (Glasgow, Scotland) using the BRS/Search information retrieval software. The subscription method for end-users--i.e., annual fee entitles user to unlimited access to database, document provision, and printed awareness…

Retrieval of land cover information under thin fog in Landsat TM image

NASA Astrophysics Data System (ADS)

Wei, Yuchun

2008-04-01

Thin fog, which often appears in remote sensing image of subtropical climate region, has resulted in the low image quantity and bad image mapping. Therefore, it is necessary to develop the image processing method to retrieve land cover information under thin fog. In this paper, the Landsat TM image near the Taihu Lake that is in the subtropical climate zone of China was used as an example, and the workflow and method used to retrieve the land cover information under thin fog have been built based on ENVI software and a single TM image. The basic step covers three parts: 1) isolating the thin fog area in image according to the spectral difference of different bands; 2) retrieving the visible band information of different land cover types under thin fog from the near-infrared bands according to the relationships between near-infrared bands and visible bands of different land cover types in the area without fog; 3) image post-process. The result showed that the method in the paper is easy and suitable, and can be used to improve the quantity of TM image mapping more effectively.

Database resources of the National Center for Biotechnology Information: 2002 update

PubMed Central

Wheeler, David L.; Church, Deanna M.; Lash, Alex E.; Leipe, Detlef D.; Madden, Thomas L.; Pontius, Joan U.; Schuler, Gregory D.; Schriml, Lynn M.; Tatusova, Tatiana A.; Wagner, Lukas; Rapp, Barbara A.

2002-01-01

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, Human¡VMouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov. PMID:11752242

Autobiographical memory specificity in response to verbal and pictorial cues in clinical depression.

PubMed

Ridout, Nathan; Dritschel, Barbara; Matthews, Keith; O'Carroll, Ronan

2016-06-01

Depressed individuals have been consistently shown to exhibit problems in accessing specific memories of events from their past and instead tend to retrieve categorical summaries of events. The majority of studies examining autobiographical memory changes associated with psychopathology have tended to use word cues, but only one study to date has used images (with PTSD patients). to determine if using images to cue autobiographical memories would reduce the memory specificity deficit exhibited by patients with depression in comparison to healthy controls. Twenty-five clinically depressed patients and twenty-five healthy controls were assessed on two versions of the autobiographical memory test; cued with emotional words and images. Depressed patients retrieved significantly fewer specific memories, and a greater number of categorical, than did the controls. Controls retrieved a greater proportion of specific memories to images compared to words, whereas depressed patients retrieved a similar proportion of specific memories to both images and words. no information about the presence and severity of past trauma was collected. results suggest that the overgeneral memory style in depression generalises from verbal to pictorial cues. This is important because retrieval to images may provide a more ecologically valid test of everyday memory experiences than word-cued retrieval.. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-view information fusion for automatic BI-RADS description of mammographic masses

NASA Astrophysics Data System (ADS)

Narvaez, Fabián; Díaz, Gloria; Romero, Eduardo

2011-03-01

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.

Representation and alignment of sung queries for music information retrieval

NASA Astrophysics Data System (ADS)

Adams, Norman H.; Wakefield, Gregory H.

2005-09-01

The pursuit of robust and rapid query-by-humming systems, which search melodic databases using sung queries, is a common theme in music information retrieval. The retrieval aspect of this database problem has received considerable attention, whereas the front-end processing of sung queries and the data structure to represent melodies has been based on musical intuition and historical momentum. The present work explores three time series representations for sung queries: a sequence of notes, a ``smooth'' pitch contour, and a sequence of pitch histograms. The performance of the three representations is compared using a collection of naturally sung queries. It is found that the most robust performance is achieved by the representation with highest dimension, the smooth pitch contour, but that this representation presents a formidable computational burden. For all three representations, it is necessary to align the query and target in order to achieve robust performance. The computational cost of the alignment is quadratic, hence it is necessary to keep the dimension small for rapid retrieval. Accordingly, iterative deepening is employed to achieve both robust performance and rapid retrieval. Finally, the conventional iterative framework is expanded to adapt the alignment constraints based on previous iterations, further expediting retrieval without degrading performance.

An effective model for store and retrieve big health data in cloud computing.

PubMed

Goli-Malekabadi, Zohreh; Sargolzaei-Javan, Morteza; Akbari, Mohammad Kazem

2016-08-01

The volume of healthcare data including different and variable text types, sounds, and images is increasing day to day. Therefore, the storage and processing of these data is a necessary and challenging issue. Generally, relational databases are used for storing health data which are not able to handle the massive and diverse nature of them. This study aimed at presenting the model based on NoSQL databases for the storage of healthcare data. Despite different types of NoSQL databases, document-based DBs were selected by a survey on the nature of health data. The presented model was implemented in the Cloud environment for accessing to the distribution properties. Then, the data were distributed on the database by applying the Shard property. The efficiency of the model was evaluated in comparison with the previous data model, Relational Database, considering query time, data preparation, flexibility, and extensibility parameters. The results showed that the presented model approximately performed the same as SQL Server for "read" query while it acted more efficiently than SQL Server for "write" query. Also, the performance of the presented model was better than SQL Server in the case of flexibility, data preparation and extensibility. Based on these observations, the proposed model was more effective than Relational Databases for handling health data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.