Storing and Viewing Electronic Documents.

ERIC Educational Resources Information Center

Falk, Howard

1999-01-01

Discusses the conversion of fragile library materials to computer storage and retrieval to extend the life of the items and to improve accessibility through the World Wide Web. Highlights include entering the images, including scanning; optical character recognition; full text and manual indexing; and available document- and image-management…

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

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.

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.

Indexing the medical open access literature for textual and content-based visual retrieval.

PubMed

Eggel, Ivan; Müller, Henning

2010-01-01

Over the past few years an increasing amount of scientific journals have been created in an open access format. Particularly in the medical field the number of openly accessible journals is enormous making a wide body of knowledge available for analysis and retrieval. Part of the trend towards open access publications can be linked to funding bodies such as the NIH¹ (National Institutes of Health) and the Swiss National Science Foundation (SNF²) requiring funded projects to make all articles of funded research available publicly. This article describes an approach to make part of the knowledge of open access journals available for retrieval including the textual information but also the images contained in the articles. For this goal all articles of 24 journals related to medical informatics and medical imaging were crawled from the web pages of BioMed Central. Text and images of the PDF (Portable Document Format) files were indexed separately and a web-based retrieval interface allows for searching via keyword queries or by visual similarity queries. Starting point for a visual similarity query can be an image on the local hard disk that is uploaded or any image found via the textual search. Search for similar documents is also possible.

26 CFR 1.1471-1 - Scope of chapter 4 and definitions.

Code of Federal Regulations, 2013 CFR

2013-04-01

... an image retrieval system (such as portable document format (.pdf) or scanned documents). (35) Entity..., custodial institution, or specified insurance company. (124) TIN. The term TIN means the tax identifying...

26 CFR 1.1471-1 - Scope of chapter 4 and definitions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... an image retrieval system (such as portable document format (.pdf) or scanned documents). (39) Entity..., custodial institution, or specified insurance company. (133) TIN. The term TIN means the tax identifying...

An automatic indexing method for medical documents.

PubMed Central

Wagner, M. M.

1991-01-01

This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported. PMID:1807564

Experiments with a novel content-based image retrieval software: can we eliminate classification systems in adolescent idiopathic scoliosis?

PubMed

Menon, K Venugopal; Kumar, Dinesh; Thomas, Tessamma

2014-02-01

Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the newly developed CBIR database. Five new cases of different curve patterns were used as query images. The images were fed into the CBIR database that retrieved similar images from the existing cases. These were analyzed by a senior surgeon for conformity to the query image. Results Within the limits of variability set for the query system, all the resultant images conformed to the query image. One case had no similar match in the series. The other four retrieved several images that were matching with the query. No matching case was left out in the series. The postoperative images were then analyzed to check for surgical strategies. Broad guidelines for treatment could be derived from the results. More precise query settings, inclusion of bending films, and a larger database will enhance accurate retrieval and better decision making. Conclusion The CBIR system is an effective tool for accurate documentation and retrieval of scoliosis images. Broad guidelines for surgical strategies can be made from the postoperative images of the existing cases without adhering to any classification scheme.

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.

Millimeter-wave Imaging Radiometer (MIR) data processing and development of water vapor retrieval algorithms

NASA Technical Reports Server (NTRS)

Chang, L. Aron

1995-01-01

This document describes the progress of the task of the Millimeter-wave Imaging Radiometer (MIR) data processing and the development of water vapor retrieval algorithms, for the second six-month performing period. Aircraft MIR data from two 1995 field experiments were collected and processed with a revised data processing software. Two revised versions of water vapor retrieval algorithm were developed, one for the execution of retrieval on a supercomputer platform, and one for using pressure as the vertical coordinate. Two implementations of incorporating products from other sensors into the water vapor retrieval system, one from the Special Sensor Microwave Imager (SSM/I), the other from the High-resolution Interferometer Sounder (HIS). Water vapor retrievals were performed for both airborne MIR data and spaceborne SSM/T-2 data, during field experiments of TOGA/COARE, CAMEX-1, and CAMEX-2. The climatology of water vapor during TOGA/COARE was examined by SSM/T-2 soundings and conventional rawinsonde.

Commercial applications for optical data storage

NASA Astrophysics Data System (ADS)

Tas, Jeroen

1991-03-01

Optical data storage has spurred the market for document imaging systems. These systems are increasingly being used to electronically manage the processing, storage and retrieval of documents. Applications range from straightforward archives to sophisticated workflow management systems. The technology is developing rapidly and within a few years optical imaging facilities will be incorporated in most of the office information systems. This paper gives an overview of the status of the market, the applications and the trends of optical imaging systems.

Indexing and retrieving DICOM data in disperse and unstructured archives.

PubMed

Costa, Carlos; Freitas, Filipe; Pereira, Marco; Silva, Augusto; Oliveira, José L

2009-01-01

This paper proposes an indexing and retrieval solution to gather information from distributed DICOM documents by allowing searches and access to the virtual data repository using a Google-like process. The medical imaging modalities are becoming more powerful and less expensive. The result is the proliferation of equipment acquisition by imaging centers, including the small ones. With this dispersion of data, it is not easy to take advantage of all the information that can be retrieved from these studies. Furthermore, many of these small centers do not have large enough requirements to justify the acquisition of a traditional PACS. A peer-to-peer PACS platform to index and query DICOM files over a set of distributed repositories that are logically viewed as a single federated unit. The solution is based on a public domain document-indexing engine and extends traditional PACS query and retrieval mechanisms. This proposal deals well with complex searching requirements, from a single desktop environment to distributed scenarios. The solution performance and robustness were demonstrated in trials. The characteristics of presented PACS platform make it particularly important for small institutions, including educational and research groups.

Video Information Communication and Retrieval/Image Based Information System (VICAR/IBIS)

NASA Technical Reports Server (NTRS)

Wherry, D. B.

1981-01-01

The acquisition, operation, and planning stages of installing a VICAR/IBIS system are described. The system operates in an IBM mainframe environment, and provides image processing of raster data. System support problems with software and documentation are discussed.

Fusion of Deep Learning and Compressed Domain features for Content Based Image Retrieval.

PubMed

Liu, Peizhong; Guo, Jing-Ming; Wu, Chi-Yi; Cai, Danlin

2017-08-29

This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate (APR) and average recall rate (ARR), are employed to examine various datasets. As documented in the experimental results, the proposed schemes can achieve superior performance compared to the state-of-the-art methods with either low- or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

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.

XDS-I Gateway Development for HIE Connectivity with Legacy PACS at Gil Hospital.

PubMed

Simalango, Mikael Fernandus; Kim, Youngchul; Seo, Young Tae; Choi, Young Hwan; Cho, Yong Kyun

2013-12-01

The ability to support healthcare document sharing is imperative in a health information exchange (HIE). Sharing imaging documents or images, however, can be challenging, especially when they are stored in a picture archiving and communication system (PACS) archive that does not support document sharing via standard HIE protocols. This research proposes a standard-compliant imaging gateway that enables connectivity between a legacy PACS and the entire HIE. Investigation of the PACS solutions used at Gil Hospital was conducted. An imaging gateway application was then developed using a Java technology stack. Imaging document sharing capability enabled by the gateway was tested by integrating it into Gil Hospital's order communication system and its HIE infrastructure. The gateway can acquire radiology images from a PACS storage system, provide and register the images to Gil Hospital's HIE for document sharing purposes, and make the images retrievable by a cross-enterprise document sharing document viewer. Development of an imaging gateway that mediates communication between a PACS and an HIE can be considered a viable option when the PACS does not support the standard protocol for cross-enterprise document sharing for imaging. Furthermore, the availability of common HIE standards expedites the development and integration of the imaging gateway with an HIE.

XDS-I Gateway Development for HIE Connectivity with Legacy PACS at Gil Hospital

PubMed Central

Simalango, Mikael Fernandus; Kim, Youngchul; Seo, Young Tae; Cho, Yong Kyun

2013-01-01

Objectives The ability to support healthcare document sharing is imperative in a health information exchange (HIE). Sharing imaging documents or images, however, can be challenging, especially when they are stored in a picture archiving and communication system (PACS) archive that does not support document sharing via standard HIE protocols. This research proposes a standard-compliant imaging gateway that enables connectivity between a legacy PACS and the entire HIE. Methods Investigation of the PACS solutions used at Gil Hospital was conducted. An imaging gateway application was then developed using a Java technology stack. Imaging document sharing capability enabled by the gateway was tested by integrating it into Gil Hospital's order communication system and its HIE infrastructure. Results The gateway can acquire radiology images from a PACS storage system, provide and register the images to Gil Hospital's HIE for document sharing purposes, and make the images retrievable by a cross-enterprise document sharing document viewer. Conclusions Development of an imaging gateway that mediates communication between a PACS and an HIE can be considered a viable option when the PACS does not support the standard protocol for cross-enterprise document sharing for imaging. Furthermore, the availability of common HIE standards expedites the development and integration of the imaging gateway with an HIE. PMID:24523994

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

Electronic Document Management Systems: Where Are They Today?

ERIC Educational Resources Information Center

Koulopoulos, Thomas M.; Frappaolo, Carl

1993-01-01

Discusses developments in document management systems based on a survey of over 400 corporations and government agencies. Text retrieval and imaging markets, architecture and integration, purchasing plans, and vendor market leaders are covered. Five graphs present data on user preferences for improvements. A sidebar article reviews the development…

Clustering document fragments using background color and texture information

NASA Astrophysics Data System (ADS)

Chanda, Sukalpa; Franke, Katrin; Pal, Umapada

2012-01-01

Forensic analysis of questioned documents sometimes can be extensively data intensive. A forensic expert might need to analyze a heap of document fragments and in such cases to ensure reliability he/she should focus only on relevant evidences hidden in those document fragments. Relevant document retrieval needs finding of similar document fragments. One notion of obtaining such similar documents could be by using document fragment's physical characteristics like color, texture, etc. In this article we propose an automatic scheme to retrieve similar document fragments based on visual appearance of document paper and texture. Multispectral color characteristics using biologically inspired color differentiation techniques are implemented here. This is done by projecting document color characteristics to Lab color space. Gabor filter-based texture analysis is used to identify document texture. It is desired that document fragments from same source will have similar color and texture. For clustering similar document fragments of our test dataset we use a Self Organizing Map (SOM) of dimension 5×5, where the document color and texture information are used as features. We obtained an encouraging accuracy of 97.17% from 1063 test images.

A simple procedure for retrieval of a cement-retained implant-supported crown: a case report.

PubMed

Buzayan, Muaiyed Mahmoud; Mahmood, Wan Adida; Yunus, Norsiah Binti

2014-02-01

Retrieval of cement-retained implant prostheses can be more demanding than retrieval of screw-retained prostheses. This case report describes a simple and predictable procedure to locate the abutment screw access openings of cementretained implant-supported crowns in cases of fractured ceramic veneer. A conventional periapical radiography image was captured using a digital camera, transferred to a computer, and manipulated using Microsoft Word document software to estimate the location of the abutment screw access.

Classification of document page images based on visual similarity of layout structures

NASA Astrophysics Data System (ADS)

Shin, Christian K.; Doermann, David S.

1999-12-01

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.

Unified modeling language and design of a case-based retrieval system in medical imaging.

PubMed Central

LeBozec, C.; Jaulent, M. C.; Zapletal, E.; Degoulet, P.

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users. Images Figure 6 Figure 7 PMID:9929346

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

The present status and problems in document retrieval system : document input type retrieval system

NASA Astrophysics Data System (ADS)

Inagaki, Hirohito

The office-automation (OA) made many changes. Many documents were begun to maintained in an electronic filing system. Therefore, it is needed to establish efficient document retrieval system to extract useful information. Current document retrieval systems are using simple word-matching, syntactic-matching, semantic-matching to obtain high retrieval efficiency. On the other hand, the document retrieval systems using special hardware devices, such as ISSP, were developed for aiming high speed retrieval. Since these systems can accept a single sentence or keywords as input, it is difficult to explain searcher's request. We demonstrated document input type retrieval system, which can directly accept document as an input, and can search similar documents from document data-base.

AVIRIS Reflectance Retrievals: UCSB Users Manual. Appendix 1

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Prentiss, Dylan

2001-01-01

The following write-up is designed to help students and researchers take Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance data and retrieve surface reflectance. In the event that the software is not available, but a user has access to a reflectance product, this document is designed to provide a better understanding of how AVIRIS reflectance was retrieved. This guide assumes that the reader has both a basic understanding of the UNIX computing environment, and that of spectroscopy. Knowledge of the Interactive Data Language (IDL) and the Environment for Visualizing Images (ENVI) is helpful. This is a working document, and many of the fine details described in the following pages have been previously undocumented. After having read this document the reader should be able to process AVIRIS to reflectance, provided access to all of the code is possible. The AVIRIS radiance data itself is pre-processed at the Jet Propulsion Laboratory (JPL) in Pasadena, California. The first section of this paper describes how to read data from tape and byte-swap the data. Section 2 describes the procedure in preparing support files before running the 'h2o' suite of programs. Section 3 describes the four programs used in the process, h2olut9.f, h2ospl9.f, vlsfit9.f and rfl9.f.

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.

Implementation of Imaging Technology for Recordkeeping at the World Bank.

ERIC Educational Resources Information Center

Smith, Clive D.

1997-01-01

Describes the evolution of an electronic document management system for the World Bank, including record-keeping components, and how the Pittsburgh requirements for evidence in record keeping were used to evaluate it. Discusses imaging technology for scanning paper records, metadata for retrieval and record keeping, and extending the system to…

Font adaptive word indexing of modern printed documents.

PubMed

Marinai, Simone; Marino, Emanuele; Soda, Giovanni

2006-08-01

We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.

Out of sight, out of mind: racial retrieval cues increase the accessibility of social justice concepts.

PubMed

Salter, Phia S; Kelley, Nicholas J; Molina, Ludwin E; Thai, Luyen T

2017-09-01

Photographs provide critical retrieval cues for personal remembering, but few studies have considered this phenomenon at the collective level. In this research, we examined the psychological consequences of visual attention to the presence (or absence) of racially charged retrieval cues within American racial segregation photographs. We hypothesised that attention to racial retrieval cues embedded in historical photographs would increase social justice concept accessibility. In Study 1, we recorded gaze patterns with an eye-tracker among participants viewing images that contained racial retrieval cues or were digitally manipulated to remove them. In Study 2, we manipulated participants' gaze behaviour by either directing visual attention toward racial retrieval cues, away from racial retrieval cues, or directing attention within photographs where racial retrieval cues were missing. Across Studies 1 and 2, visual attention to racial retrieval cues in photographs documenting historical segregation predicted social justice concept accessibility.

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.

Unified modeling language and design of a case-based retrieval system in medical imaging.

PubMed

LeBozec, C; Jaulent, M C; Zapletal, E; Degoulet, P

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users.

Videofile for Law Enforcement

NASA Technical Reports Server (NTRS)

1977-01-01

Components of a videotape storage and retrieval system originally developed for NASA have been adapted as a tool for law enforcement agencies. Ampex Corp., Redwood City, Cal., built a unique system for NASA-Marshall. The first application of professional broadcast technology to computerized record-keeping, it incorporates new equipment for transporting tapes within the system. After completing the NASA system, Ampex continued development, primarily to improve image resolution. The resulting advanced system, known as the Ampex Videofile, offers advantages over microfilm for filing, storing, retrieving, and distributing large volumes of information. The system's computer stores information in digital code rather than in pictorial form. While microfilm allows visual storage of whole documents, it requires a step before usage--developing the film. With Videofile, the actual document is recorded, complete with photos and graphic material, and a picture of the document is available instantly.

Information Retrieval: A Sequential Learning Process.

ERIC Educational Resources Information Center

Bookstein, Abraham

1983-01-01

Presents decision-theoretic models which intrinsically include retrieval of multiple documents whereby system responds to request by presenting documents to patron in sequence, gathering feedback, and using information to modify future retrievals. Document independence model, set retrieval model, sequential retrieval model, learning model,…

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.

Analyzing Document Retrievability in Patent Retrieval Settings

NASA Astrophysics Data System (ADS)

Bashir, Shariq; Rauber, Andreas

Most information retrieval settings, such as web search, are typically precision-oriented, i.e. they focus on retrieving a small number of highly relevant documents. However, in specific domains, such as patent retrieval or law, recall becomes more relevant than precision: in these cases the goal is to find all relevant documents, requiring algorithms to be tuned more towards recall at the cost of precision. This raises important questions with respect to retrievability and search engine bias: depending on how the similarity between a query and documents is measured, certain documents may be more or less retrievable in certain systems, up to some documents not being retrievable at all within common threshold settings. Biases may be oriented towards popularity of documents (increasing weight of references), towards length of documents, favour the use of rare or common words; rely on structural information such as metadata or headings, etc. Existing accessibility measurement techniques are limited as they measure retrievability with respect to all possible queries. In this paper, we improve accessibility measurement by considering sets of relevant and irrelevant queries for each document. This simulates how recall oriented users create their queries when searching for relevant information. We evaluate retrievability scores using a corpus of patents from US Patent and Trademark Office.

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

INFORMATION STORAGE AND RETRIEVAL, REPORTS ON EVALUATION PROCEDURES AND RESULTS 1965-1967.

ERIC Educational Resources Information Center

SALTON, GERALD

A DETAILED ANALYSIS OF THE RETRIEVAL EVALUATION RESULTS OBTAINED WITH THE AUTOMATIC SMART DOCUMENT RETRIEVAL SYSTEM FOR DOCUMENT COLLECTIONS IN THE FIELDS OF AERODYNAMICS, COMPUTER SCIENCE, AND DOCUMENTATION IS GIVEN IN THIS REPORT. THE VARIOUS COMPONENTS OF FULLY AUTOMATIC DOCUMENT RETRIEVAL SYSTEMS ARE DISCUSSED IN DETAIL, INCLUDING THE FORMS OF…

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

PubMed

Welter, Petra; Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis

PubMed Central

Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno (né Lehmann), Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process. PMID:21672913

Mobile medical image retrieval

NASA Astrophysics Data System (ADS)

Duc, Samuel; Depeursinge, Adrien; Eggel, Ivan; Müller, Henning

2011-03-01

Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in the text. Problems with the many, often incompatible mobile platforms were discovered and are listed in the text. Mobile information access is a quickly growing domain and the constraints of mobile access also need to be taken into account for image retrieval. The demonstrated access to the medical literature is most relevant as the medical literature and their images are clearly the largest knowledge source in the medical field.

An Intelligent System for Document Retrieval in Distributed Office Environments.

ERIC Educational Resources Information Center

Mukhopadhyay, Uttam; And Others

1986-01-01

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns and user interests and preferences during system usage, it customizes document retrievals for…

A Re-Unification of Two Competing Models for Document Retrieval.

ERIC Educational Resources Information Center

Bodoff, David

1999-01-01

Examines query-oriented versus document-oriented information retrieval and feedback learning. Highlights include a reunification of the two approaches for probabilistic document retrieval and for vector space model (VSM) retrieval; learning in VSM and in probabilistic models; multi-dimensional scaling; and ongoing field studies. (LRW)

The Ecological Approach to Text Visualization.

ERIC Educational Resources Information Center

Wise, James A.

1999-01-01

Presents both theoretical and technical bases on which to build a "science of text visualization." The Spatial Paradigm for Information Retrieval and Exploration (SPIRE) text-visualization system, which images information from free-text documents as natural terrains, serves as an example of the "ecological approach" in its visual metaphor, its…

Implementation of a thesaurus in an electronic photograph imaging system

NASA Astrophysics Data System (ADS)

Partlow, Denise

1995-11-01

A photograph imaging system presents a unique set of requirements for indexing and retrieving images, unlike a standard imaging system for written documents. This paper presents the requirements, technical design, and development results for a hierarchical ANSI standard thesaurus embedded into a photograph archival system. The thesaurus design incorporates storage reduction techniques, permits fast searches, and contains flexible indexing methods. It can be extended to many applications other than the retrieval of photographs. When photographic images are indexed into an electronic system, they are subject to a variety of indexing problems based on what the indexer `sees.' For instance, the indexer may categorize an image as a boat when others might refer to it as a ship, sailboat, or raft. The thesaurus will allow a user to locate images containing any synonym for boat, regardless of how the image was actually indexed. In addition to indexing problems, photos may need to be retrieved based on a broad category, for instance, flowers. The thesaurus allows a search for `flowers' to locate all images containing a rose, hibiscus, or daisy, yet still allow a specific search for an image containing only a rose. The technical design and method of implementation for such a thesaurus is presented. The thesaurus is implemented using an SQL relational data base management system that supports blobs, binary large objects. The design incorporates unique compression methods for storing the thesaurus words. Words are indexed to photographs using the compressed word and allow for very rapid searches, eliminating lengthy string matches.

Transcript mapping for handwritten English documents

NASA Astrophysics Data System (ADS)

Jose, Damien; Bharadwaj, Anurag; Govindaraju, Venu

2008-01-01

Transcript mapping or text alignment with handwritten documents is the automatic alignment of words in a text file with word images in a handwritten document. Such a mapping has several applications in fields ranging from machine learning where large quantities of truth data are required for evaluating handwriting recognition algorithms, to data mining where word image indexes are used in ranked retrieval of scanned documents in a digital library. The alignment also aids "writer identity" verification algorithms. Interfaces which display scanned handwritten documents may use this alignment to highlight manuscript tokens when a person examines the corresponding transcript word. We propose an adaptation of the True DTW dynamic programming algorithm for English handwritten documents. The integration of the dissimilarity scores from a word-model word recognizer and Levenshtein distance between the recognized word and lexicon word, as a cost metric in the DTW algorithm leading to a fast and accurate alignment, is our primary contribution. Results provided, confirm the effectiveness of our approach.

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.

MorphoSaurus--design and evaluation of an interlingua-based, cross-language document retrieval engine for the medical domain.

PubMed

Markó, K; Schulz, S; Hahn, U

2005-01-01

We propose an interlingua-based indexing approach to account for the particular challenges that arise in the design and implementation of cross-language document retrieval systems for the medical domain. Documents, as well as queries, are mapped to a language-independent conceptual layer on which retrieval operations are performed. We contrast this approach with the direct translation of German queries to English ones which, subsequently, are matched against English documents. We evaluate both approaches, interlingua-based and direct translation, on a large medical document collection, the OHSUMED corpus. A substantial benefit for interlingua-based document retrieval using German queries on English texts is found, which amounts to 93% of the (monolingual) English baseline. Most state-of-the-art cross-language information retrieval systems translate user queries to the language(s) of the target documents. In contra-distinction to this approach, translating both documents and user queries into a language-independent, concept-like representation format is more beneficial to enhance cross-language retrieval performance.

Text-image alignment for historical handwritten documents

NASA Astrophysics Data System (ADS)

Zinger, S.; Nerbonne, J.; Schomaker, L.

2009-01-01

We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set - images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69% of words from 100 lines, or 90% of partially correct and correct alignments combined.

Query Expansion for Noisy Legal Documents

DTIC Science & Technology

2008-11-01

9] G. Salton (ed). The SMART retrieval system experiments in automatic document processing. 1971. [10] H. Schutze and J . Pedersen. A cooccurrence...Language Modeling and Information Retrieval. http://www.lemurproject.org. [2] J . Baron, D. Lewis, and D. Oard. TREC 2006 legal track overview. In...Retrieval, 1993. [8] J . Rocchio. Relevance feedback in information retrieval. In The SMART retrieval system experiments in automatic document processing, 1971

Cognitive Process as a Basis for Intelligent Retrieval Systems Design.

ERIC Educational Resources Information Center

Chen, Hsinchun; Dhar, Vasant

1991-01-01

Two studies of the cognitive processes involved in online document-based information retrieval were conducted. These studies led to the development of five computational models of online document retrieval which were incorporated into the design of an "intelligent" document-based retrieval system. Both the system and the broader implications of…

An XML Data Model for Inverted Image Indexing

NASA Astrophysics Data System (ADS)

So, Simon W.; Leung, Clement H. C.; Tse, Philip K. C.

2003-01-01

The Internet world makes increasing use of XML-based technologies. In multimedia data indexing and retrieval, the MPEG-7 standard for Multimedia Description Scheme is specified using XML. The flexibility of XML allows users to define other markup semantics for special contexts, construct data-centric XML documents, exchange standardized data between computer systems, and present data in different applications. In this paper, the Inverted Image Indexing paradigm is presented and modeled using XML Schema.

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.

Using Induction to Refine Information Retrieval Strategies

NASA Technical Reports Server (NTRS)

Baudin, Catherine; Pell, Barney; Kedar, Smadar

1994-01-01

Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

TORPEDO: Networked Access to Full-Text and Page-Image Representations of Physics Journals and Technical Reports.

ERIC Educational Resources Information Center

Atkinson, Roderick D.; Stackpole, Laurie E.

1995-01-01

The Naval Research Laboratory (NRL) Library and the American Physical Society (APS) are experimenting with electronically disseminating journals and reports in a project called TORPEDO (The Optical Retrieval Project: Electronic Documents Online). Scanned journals and reports are converted to ASCII, then attached to bibliographic information, and…

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.…

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

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

Choo, Jaegul; Kim, Hannah; Clarkson, Edward

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents,more » VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.« less

VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

DOE PAGES

Choo, Jaegul; Kim, Hannah; Clarkson, Edward; ...

2018-01-31

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents,more » VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.« less

An overview of selected information storage and retrieval issues in computerized document processing

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Ihebuzor, Valentine U.

1984-01-01

The rapid development of computerized information storage and retrieval techniques has introduced the possibility of extending the word processing concept to document processing. A major advantage of computerized document processing is the relief of the tedious task of manual editing and composition usually encountered by traditional publishers through the immense speed and storage capacity of computers. Furthermore, computerized document processing provides an author with centralized control, the lack of which is a handicap of the traditional publishing operation. A survey of some computerized document processing techniques is presented with emphasis on related information storage and retrieval issues. String matching algorithms are considered central to document information storage and retrieval and are also discussed.

75 FR 72829 - Los Alamos Historical Document Retrieval and Assessment (LAHDRA) Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-26

... Historical Document Retrieval and Assessment (LAHDRA) Project The Centers for Disease Control and Prevention... release of the Final Report of the Los Alamos Historical Document Retrieval and Assessment (LAHDRA)Project... information about historical chemical or radionuclide releases from facilities at the Los Alamos National...

Predicting Document Retrieval System Performance: An Expected Precision Measure.

ERIC Educational Resources Information Center

Losee, Robert M., Jr.

1987-01-01

Describes an expected precision (EP) measure designed to predict document retrieval performance. Highlights include decision theoretic models; precision and recall as measures of system performance; EP graphs; relevance feedback; and computing the retrieval status value of a document for two models, the Binary Independent Model and the Two Poisson…

Assessment and application of AirMSPI high-resolution multiangle imaging photo-polarimetric observations for atmospheric correction

NASA Astrophysics Data System (ADS)

Kalashnikova, O. V.; Xu, F.; Garay, M. J.; Seidel, F. C.; Diner, D. J.

2016-02-01

Water-leaving radiance comprises less than 10% of the signal measured from space, making correction for absorption and scattering by the intervening atmosphere imperative. Modern improvements have been developed in ocean color retrieval algorithms to handle absorbing aerosols such as urban particulates in coastal areas and transported desert dust over the open ocean. In addition, imperfect knowledge of the absorbing aerosol optical properties or their height distribution results in well-documented sources of error in the retrieved water leaving radiance. Multi-angle spectro-polarimetric measurements have been advocated as an additional tool to better understand and retrieve the aerosol properties needed for atmospheric correction for ocean color retrievals. The Airborne Multiangle SpectroPolarimetric Imager-1 (AirMSPI-1) has been flying aboard the NASA ER-2 high altitude aircraft since October 2010. AirMSPI typically acquires observations of a target area at 9 view angles between ±67° at 10 m resolution. AirMSPI spectral channels are centered at 355, 380, 445, 470, 555, 660, and 865 nm, with 470, 660, and 865 reporting linear polarization. We have developed a retrieval code that employs a coupled Markov Chain (MC) and adding/doubling radiative transfer method for joint retrieval of aerosol properties and water leaving radiance from AirMSPI polarimetric observations. We tested prototype retrievals by comparing the retrieved aerosol concentration, size distribution, water-leaving radiance, and chlorophyll concentrations to values reported by the USC SeaPRISM AERONET-OC site off the coast of California. The retrieval then was applied to a variety of costal regions in California to evaluate variability in the water-leaving radiance under different atmospheric conditions. We will present results, and will discuss algorithm sensitivity and potential applications for future space-borne coastal monitoring.

On-Line Retrieval System Design; Part V of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

On-line retrieval system design is discussed in the two papers which make up Part Five of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Prototype On-Line Document Retrieval System" by D. Williamson and R. Williamson outlines a design for a SMART on-line document retrieval system…

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).

IHE profiles applied to regional PACS.

PubMed

Fernandez-Bayó, Josep

2011-05-01

PACS has been widely adopted as an image storage solution that perfectly fits the radiology department workflow and that can be easily extended to other hospital departments. Integrations with other hospital systems, like the Radiology Information System, the Hospital Information System and the Electronic Patient Record are fully achieved but still challenging aims. PACS also creates the perfect environment for teleradiology and teleworking setups. One step further is the regional PACS concept where different hospitals or health care enterprises share the images in an integrated Electronic Patient Record. Among the different solutions available to share images between different hospitals IHE (Integrating the Healthcare Enterprise) organization presents the Cross Enterprise Document Sharing profile (XDS) which allows sharing images from different hospitals even if they have different PACS vendors. Adopting XDS has multiple advantages, images do not need to be duplicated in a central archive to be shared among the different healthcare enterprises, they only need to be indexed and published in a central document registry. In the XDS profile IHE defines the mechanisms to publish and index the images in the central document registry. It also defines the mechanisms that each hospital will use to retrieve those images regardless on the Hospital PACS they are stored. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

Full-field optical coherence tomography used for security and document identity

NASA Astrophysics Data System (ADS)

Chang, Shoude; Mao, Youxin; Sherif, Sherif; Flueraru, Costel

2006-09-01

The optical coherence tomography (OCT) is an emerging technology for high-resolution cross-sectional imaging of 3D structures. In the past years, OCT systems have been used mainly for medical, especially ophthalmological diagnostics. Concerning the nature of OCT system being capable to explore the internal features of an object, we apply the OCT technology to directly retrieve the 2D information pre-stored in a multiple-layer information carrier. The standard depth-resolution of an OCT system is at micrometer level. If a 20mm by 20mm sampling area with a 1024 x 1024 CCD array is used in the OCT system having 10 μm, an information carrier having a volume of 20mm x 20mm x 2mm could contain 200 Mega-pixel images. Because of its tiny size and large information volume, the information carrier, with its OCT retrieving system, will have potential applications in documents security and object identification. In addition, as the information carrier can be made by low-scattering transparent material, the signal/noise ratio will be improved dramatically. As a consequence, the specific hardware and complicated software can also be greatly simplified. Owing to non-scanning along X-Y axis, the full-field OCT could be the simplest and most economic imaging system for extracting information from such a multilayer information carrier. In this paper, deign and implementation of a full-field OCT system is described and the related algorithms are introduced. In our experiments, a four layers information carrier is used, which contains 4 layers of image pattern, two text images and two fingerprint images. The extracted tomography images of each layer are also provided.

Multispectral Photogrammetric Data Acquisition and Processing Forwall Paintings Studies

NASA Astrophysics Data System (ADS)

Pamart, A.; Guillon, O.; Faraci, S.; Gattet, E.; Genevois, M.; Vallet, J. M.; De Luca, L.

2017-02-01

In the field of wall paintings studies different imaging techniques are commonly used for the documentation and the decision making in term of conservation and restoration. There is nowadays some challenging issues to merge scientific imaging techniques in a multimodal context (i.e. multi-sensors, multi-dimensions, multi-spectral and multi-temporal approaches). For decades those CH objects has been widely documented with Technical Photography (TP) which gives precious information to understand or retrieve the painting layouts and history. More recently there is an increasing demand of the use of digital photogrammetry in order to provide, as one of the possible output, an orthophotomosaic which brings a possibility for metrical quantification of conservators/restorators observations and actions planning. This paper presents some ongoing experimentations of the LabCom MAP-CICRP relying on the assumption that those techniques can be merged through a common pipeline to share their own benefits and create a more complete documentation.

Autocorrelation and Regularization of Query-Based Information Retrieval Scores

DTIC Science & Technology

2008-02-01

of the most general information retrieval models [ Salton , 1968]. By treating a query as a very short document, documents and queries can be rep... Salton , 1971]. In the context of single link hierarchical clustering, Jardine and van Rijsbergen showed that ranking all k clusters and retrieving a...a document about “dogs”, then the system will always miss this document when a user queries “dog”. Salton recognized that a document’s representation

Cross-modal learning to rank via latent joint representation.

PubMed

Wu, Fei; Jiang, Xinyang; Li, Xi; Tang, Siliang; Lu, Weiming; Zhang, Zhongfei; Zhuang, Yueting

2015-05-01

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.

Semi-Automated Methods for Refining a Domain-Specific Terminology Base

DTIC Science & Technology

2011-02-01

only as a resource for written and oral translation, but also for Natural Language Processing ( NLP ) applications, text retrieval, document indexing...Natural Language Processing ( NLP ) applications, text retrieval, document indexing, and other knowledge management tasks. The objective of this...also for Natural Language Processing ( NLP ) applications, text retrieval (1), document indexing, and other knowledge management tasks. The National

Computer-Assisted Search Of Large Textual Data Bases

NASA Technical Reports Server (NTRS)

Driscoll, James R.

1995-01-01

"QA" denotes high-speed computer system for searching diverse collections of documents including (but not limited to) technical reference manuals, legal documents, medical documents, news releases, and patents. Incorporates previously available and emerging information-retrieval technology to help user intelligently and rapidly locate information found in large textual data bases. Technology includes provision for inquiries in natural language; statistical ranking of retrieved information; artificial-intelligence implementation of semantics, in which "surface level" knowledge found in text used to improve ranking of retrieved information; and relevance feedback, in which user's judgements of relevance of some retrieved documents used automatically to modify search for further information.

Scaling Up High-Value Retrieval to Medium-Volume Data

NASA Astrophysics Data System (ADS)

Cunningham, Hamish; Hanbury, Allan; Rüger, Stefan

We summarise the scientific work presented at the first Information Retrieval Facility Conference [3] and argue that high-value retrieval with medium-volume data, exemplified by patent search, is a thriving topic in a multidisciplinary area that sits between Information Retrieval, Natural Language Processing and Semantic Web Technologies. We analyse the parameters that condition choices of retrieval technology for different sizes and values of document space, and we present the patent document space and some of its characteristics for retrieval work.

Current Research into Chemical and Textual Information Retrieval at the Department of Information Studies, University of Sheffield.

ERIC Educational Resources Information Center

Lynch, Michael F.; Willett, Peter

1987-01-01

Discusses research into chemical information and document retrieval systems at the University of Sheffield. Highlights include the use of cluster analysis methods for document retrieval and drug design, representation and searching of files of generic chemical structures, and the application of parallel computer hardware to information retrieval.…

Bag-of-features based medical image retrieval via multiple assignment and visual words weighting.

PubMed

Wang, Jingyan; Li, Yongping; Zhang, Ying; Wang, Chao; Xie, Honglan; Chen, Guoling; Gao, Xin

2011-11-01

Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights.

Information retrieval for a document writing assistance program

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

Corral, M.L.; Simon, A.; Julien, C.

This paper presents an Information Retrieval mechanism to facilitate the writing of technical documents in the space domain. To address the need for document exchange between partners in a given project, documents are standardized. The writing of a new document requires the re-use of existing documents or parts thereof. These parts can be identified by {open_quotes}tagging{close_quotes} the logical structure of documents and restored by means of a purpose-built Information Retrieval System (I.R.S.). The I.R.S. implemented in our writing assistance tool uses natural language queries and is based on a statistical linguistic approach which is enhanced by the use of documentmore » structure module.« less

Revised description of index of Florida water data collection active stations and a user's guide for station or site information retrieval computer program FINDEX H578

USGS Publications Warehouse

Geiger, Linda H.

1983-01-01

The report is an update of U.S. Geological Survey Open-File Report 77-703, which described a retrieval program for administrative index of active data-collection sites in Florida. Extensive changes to the Findex system have been made since 1977 , making the previous report obsolete. A description of the data base and computer programs that are available in the Findex system are documented in this report. This system serves a vital need in the administration of the many and diverse water-data collection activities. District offices with extensive data-collection activities will benefit from the documentation of the system. Largely descriptive, the report tells how a file of computer card images has been established which contains entries for all sites in Florida at which there is currently a water-data collection activity. Entries include information such as identification number, station name, location, type of site, county, frequency of data collection, funding, and other pertinent details. The computer program FINDEX selectively retrieves entries and lists them in a format suitable for publication. The index is updated routinely. (USGS)

Multimedia platform for authoring and presentation of clinical rounds in cardiology

NASA Astrophysics Data System (ADS)

Ratib, Osman M.; Allada, Vivekanand; Dahlbom, Magdalena; Lapstra, Lorelle

2003-05-01

We developed a multimedia presentation platform that allows retrieving data from any digital and analog modalities and to prepare a script of a clinical presentation in an XML format. This system was designed for cardiac multi-disciplinary conferences involving different cardiology specialists as well as cardiovascular surgeons. A typical presentation requires preparation of summary reports of data obtained from the different investigations and imaging techniques. An XML-based scripting methodology was developed to allow for preparation of clinical presentations. The image display program uses the generated script for the sequential presentation of different images that are displayed on pre-determined presentation settings. The ability to prepare and present clinical conferences electronically is more efficient and less time consuming than conventional settings using analog and digital documents, films and videotapes. The script of a given presentation can further be saved as part of the patient record for subsequent review of the documents and images that supported a given medical or therapeutic decision. This also constitutes a perfect documentation method for surgeons and physicians responsible of therapeutic procedures that were decided upon during the clinical conference. It allows them to review the relevant data that supported a given therapeutic decision.

Recent Experiments with INQUERY

DTIC Science & Technology

1995-11-01

were conducted with version of the INQUERY information retrieval system INQUERY is based on the Bayesian inference network retrieval model It is...corpus based query expansion For TREC a subset of of the adhoc document set was used to build the InFinder database None of the...experiments that showed signi cant improvements in retrieval eectiveness when document rankings based on the entire document text are combined with

Document Level Assessment of Document Retrieval Systems in a Pairwise System Evaluation

ERIC Educational Resources Information Center

Rajagopal, Prabha; Ravana, Sri Devi

2017-01-01

Introduction: The use of averaged topic-level scores can result in the loss of valuable data and can cause misinterpretation of the effectiveness of system performance. This study aims to use the scores of each document to evaluate document retrieval systems in a pairwise system evaluation. Method: The chosen evaluation metrics are document-level…

Embedding Term Similarity and Inverse Document Frequency into a Logical Model of Information Retrieval.

ERIC Educational Resources Information Center

Losada, David E.; Barreiro, Alvaro

2003-01-01

Proposes an approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. Highlights include document representation and matching; incorporating term similarity into the measure of distance; new algorithms for implementation; inverse document frequency; and logical versus classical models of…

Techniques of Document Management: A Review of Text Retrieval and Related Technologies.

ERIC Educational Resources Information Center

Veal, D. C.

2001-01-01

Reviews present and possible future developments in the techniques of electronic document management, the major ones being text retrieval and scanning and OCR (optical character recognition). Also addresses document acquisition, indexing and thesauri, publishing and dissemination standards, impact of the Internet, and the document management…

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

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.

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…

Categorizing document by fuzzy C-Means and K-nearest neighbors approach

NASA Astrophysics Data System (ADS)

Priandini, Novita; Zaman, Badrus; Purwanti, Endah

2017-08-01

Increasing of technology had made categorizing documents become important. It caused by increasing of number of documents itself. Managing some documents by categorizing is one of Information Retrieval application, because it involve text mining on its process. Whereas, categorization technique could be done both Fuzzy C-Means (FCM) and K-Nearest Neighbors (KNN) method. This experiment would consolidate both methods. The aim of the experiment is increasing performance of document categorize. First, FCM is in order to clustering training documents. Second, KNN is in order to categorize testing document until the output of categorization is shown. Result of the experiment is 14 testing documents retrieve relevantly to its category. Meanwhile 6 of 20 testing documents retrieve irrelevant to its category. Result of system evaluation shows that both precision and recall are 0,7.

Extraction and labeling high-resolution images from PDF documents

NASA Astrophysics Data System (ADS)

Chachra, Suchet K.; Xue, Zhiyun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Accuracy of content-based image retrieval is affected by image resolution among other factors. Higher resolution images enable extraction of image features that more accurately represent the image content. In order to improve the relevance of search results for our biomedical image search engine, Open-I, we have developed techniques to extract and label high-resolution versions of figures from biomedical articles supplied in the PDF format. Open-I uses the open-access subset of biomedical articles from the PubMed Central repository hosted by the National Library of Medicine. Articles are available in XML and in publisher supplied PDF formats. As these PDF documents contain little or no meta-data to identify the embedded images, the task includes labeling images according to their figure number in the article after they have been successfully extracted. For this purpose we use the labeled small size images provided with the XML web version of the article. This paper describes the image extraction process and two alternative approaches to perform image labeling that measure the similarity between two images based upon the image intensity projection on the coordinate axes and similarity based upon the normalized cross-correlation between the intensities of two images. Using image identification based on image intensity projection, we were able to achieve a precision of 92.84% and a recall of 82.18% in labeling of the extracted images.

Topology of Document Retrieval Systems.

ERIC Educational Resources Information Center

Everett, Daniel M.; Cater, Steven C.

1992-01-01

Explains the use of a topological structure to examine the closeness between documents in retrieval systems and analyzes the topological structure of a vector-space model, a fuzzy-set model, an extended Boolean model, a probabilistic model, and a TIRS (Topological Information Retrieval System) model. Proofs for the results are appended. (17…

Acquisition plan for Digital Document Storage (DDS) prototype system

NASA Technical Reports Server (NTRS)

1990-01-01

NASA Headquarters maintains a continuing interest in and commitment to exploring the use of new technology to support productivity improvements in meeting service requirements tasked to the NASA Scientific and Technical Information (STI) Facility, and to support cost effective approaches to the development and delivery of enhanced levels of service provided by the STI Facility. The DDS project has been pursued with this interest and commitment in mind. It is believed that DDS will provide improved archival blowback quality and service for ad hoc requests for paper copies of documents archived and serviced centrally at the STI Facility. It will also develop an operating capability to scan, digitize, store, and reproduce paper copies of 5000 NASA technical reports archived annually at the STI Facility and serviced to the user community. Additionally, it will provide NASA Headquarters and field installations with on-demand, remote, electronic retrieval of digitized, bilevel, bit mapped report images along with branched, nonsequential retrieval of report subparts.

An Overview of the Project on the Imaging and Full-Text Retrieval of the Ava Helen and Linus Pauling Papers at the Oregon State University Libraries.

ERIC Educational Resources Information Center

Krishnamurthy, Ramesh S.; Mead, Clifford S.

1995-01-01

Presents plan of Oregon State University Libraries to convert all paper documents from the Ava Helen and Linus Pauling archives to digital format. The scope, goals, tasks and objectives set by the project coordinators are outlined, and issues such as protection of equipment, access, copyright and management are discussed. (JKP)

Passive microwave algorithm development and evaluation

NASA Technical Reports Server (NTRS)

Petty, Grant W.

1995-01-01

The scientific objectives of this grant are: (1) thoroughly evaluate, both theoretically and empirically, all available Special Sensor Microwave Imager (SSM/I) retrieval algorithms for column water vapor, column liquid water, and surface wind speed; (2) where both appropriate and feasible, develop, validate, and document satellite passive microwave retrieval algorithms that offer significantly improved performance compared with currently available algorithms; and (3) refine and validate a novel physical inversion scheme for retrieving rain rate over the ocean. This report summarizes work accomplished or in progress during the first year of a three year grant. The emphasis during the first year has been on the validation and refinement of the rain rate algorithm published by Petty and on the analysis of independent data sets that can be used to help evaluate the performance of rain rate algorithms over remote areas of the ocean. Two articles in the area of global oceanic precipitation are attached.

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

PubMed

Kropf, Stefan; Krücken, Peter; Mueller, Wolf; Denecke, Kerstin

2017-05-18

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

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.

Regional information guidance system based on hypermedia concept

NASA Astrophysics Data System (ADS)

Matoba, Hiroshi; Hara, Yoshinori; Kasahara, Yutako

1990-08-01

A regional information guidance system has been developed on an image workstation. Two main features of this system are hypermedia data structure and friendly visual interface realized by the full-color frame memory system. As the hypermedia data structure manages regional information such as maps, pictures and explanations of points of interest, users can retrieve those information one by one, next to next according to their interest change. For example, users can retrieve explanation of a picture through the link between pictures and text explanations. Users can also traverse from one document to another by using keywords as cross reference indices. The second feature is to utilize a full-color, high resolution and wide space frame memory for visual interface design. This frame memory system enables real-time operation of image data and natural scene representation. The system also provides half tone representing function which enables fade-in/out presentations. This fade-in/out functions used in displaying and erasing menu and image data, makes visual interface soft for human eyes. The system we have developed is a typical example of multimedia applications. We expect the image workstation will play an important role as a platform for multimedia applications.

Strong Similarity Measures for Ordered Sets of Documents in Information Retrieval.

ERIC Educational Resources Information Center

Egghe, L.; Michel, Christine

2002-01-01

Presents a general method to construct ordered similarity measures in information retrieval based on classical similarity measures for ordinary sets. Describes a test of some of these measures in an information retrieval system that extracted ranked document sets and discuses the practical usability of the ordered similarity measures. (Author/LRW)

Internet printing

NASA Astrophysics Data System (ADS)

Rahgozar, M. Armon; Hastings, Tom; McCue, Daniel L.

1997-04-01

The Internet is rapidly changing the traditional means of creation, distribution and retrieval of information. Today, information publishers leverage the capabilities provided by Internet technologies to rapidly communicate information to a much wider audience in unique customized ways. As a result, the volume of published content has been astronomically increasing. This, in addition to the ease of distribution afforded by the Internet has resulted in more and more documents being printed. This paper introduces several axes along which Internet printing may be examined and addresses some of the technological challenges that lay ahead. Some of these axes include: (1) submission--the use of the Internet protocols for selecting printers and submitting documents for print, (2) administration--the management and monitoring of printing engines and other print resources via Web pages, and (3) formats--printing document formats whose spectrum now includes HTML documents with simple text, layout-enhanced documents with Style Sheets, documents that contain audio, graphics and other active objects as well as the existing desktop and PDL formats. The format axis of the Internet Printing becomes even more exciting when one considers that the Web documents are inherently compound and the traversal into the various pieces may uncover various formats. The paper also examines some imaging specific issues that are paramount to Internet Printing. These include formats and structures for representing raster documents and images, compression, fonts rendering and color spaces.

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.

Information Retrieval and Graph Analysis Approaches for Book Recommendation.

PubMed

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments.

Information Retrieval and Graph Analysis Approaches for Book Recommendation

PubMed Central

Benkoussas, Chahinez; Bellot, Patrice

2015-01-01

A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track. A series of reranking experiments demonstrate that combining retrieval models yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments. PMID:26504899

The Drizzling Cookbook

NASA Astrophysics Data System (ADS)

Gonzaga, S.; Biretta, J.; Wiggs, M. S.; Hsu, J. C.; Smith, T. E.; Bergeron, L.

1998-12-01

The drizzle software combines dithered images while preserving photometric accuracy, enhancing resolution, and removing geometric distortion. A recent upgrade also allows removal of cosmic rays from single images at each dither pointing. This document gives detailed examples illustrating drizzling procedures for six cases: WFPC2 observations of a deep field, a crowded field, a large galaxy, a planetary nebula, STIS/CCD observations of a HDF-North field, and NICMOS/NIC2 observations of the Egg Nebula. Command scripts and input images for each example are available on the WFPC2 WWW website. Users are encouraged to retrieve the data for the case that most closely resembles their own data and then practice and experiment drizzling the example.

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.

Robust keyword retrieval method for OCRed text

NASA Astrophysics Data System (ADS)

Fujii, Yusaku; Takebe, Hiroaki; Tanaka, Hiroshi; Hotta, Yoshinobu

2011-01-01

Document management systems have become important because of the growing popularity of electronic filing of documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the electronic documents for document retrieval. Since texts generated by OCR generally include character recognition errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher than that of the conventional method. However, the precision rate was 64% lower.

Information Storage and Retrieval. Reports on Analysis, Search, and Iterative Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard

As the fourteenth report in a series describing research in automatic information storage and retrieval, this document covers work carried out on the SMART project for approximately one year (summer 1967 to summer 1968). The document is divided into four main parts: (1) SMART systems design, (2) analysis and search experiments, (3) user feedback…

Computer program and user documentation medical data tape retrieval system

NASA Technical Reports Server (NTRS)

Anderson, J.

1971-01-01

This volume provides several levels of documentation for the program module of the NASA medical directorate mini-computer storage and retrieval system. A biomedical information system overview describes some of the reasons for the development of the mini-computer storage and retrieval system. It briefly outlines all of the program modules which constitute the system.

Document retrieval on repetitive string collections.

PubMed

Gagie, Travis; Hartikainen, Aleksi; Karhu, Kalle; Kärkkäinen, Juha; Navarro, Gonzalo; Puglisi, Simon J; Sirén, Jouni

2017-01-01

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists , that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top- k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple [Formula: see text] model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.

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

Mining knowledge from corpora: an application to retrieval and indexing.

PubMed

Soualmia, Lina F; Dahamna, Badisse; Darmoni, Stéfan

2008-01-01

The present work aims at discovering new associations between medical concepts to be exploited as input in retrieval and indexing. Association rules method is applied to documents. The process is carried out on three major document categories referring to e-health information consumers: health professionals, students and lay people. Association rules evaluation is founded on statistical measures combined with domain knowledge. Association rules represent existing relations between medical concepts (60.62%) and new knowledge (54.21%). Based on observations, 463 expert rules are defined by medical librarians for retrieval and indexing. Association rules bear out existing relations, produce new knowledge and support users and indexers in document retrieval and indexing.

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).

Evaluating Combinations of Ranked Lists and Visualizations of Inter-Document Similarity.

ERIC Educational Resources Information Center

Allan, James; Leuski, Anton; Swan, Russell; Byrd, Donald

2001-01-01

Considers how ideas from document clustering can be used to improve retrieval accuracy of ranked lists in interactive systems and how to evaluate system effectiveness. Describes a TREC (Text Retrieval Conference) study that constructed and evaluated systems that present the user with ranked lists and a visualization of inter-document similarities.…

75 FR 1793 - Study Team for the Los Alamos Historical Document Retrieval and Assessment (LAHDRA) Project

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-13

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention Study Team for the Los Alamos Historical Document Retrieval and Assessment (LAHDRA) Project The Centers for Disease... the following meeting. Name: Public Meeting of the Study Team for the Los Alamos Historical Document...

Computer program CDCID: an automated quality control program using CDC update

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

Singer, G.L.; Aguilar, F.

1984-04-01

A computer program, CDCID, has been developed in coordination with a quality control program to provide a highly automated method of documenting changes to computer codes at EG and G Idaho, Inc. The method uses the standard CDC UPDATE program in such a manner that updates and their associated documentation are easily made and retrieved in various formats. The method allows each card image of a source program to point to the document which describes it, who created the card, and when it was created. The method described is applicable to the quality control of computer programs in general. Themore » computer program described is executable only on CDC computing systems, but the program could be modified and applied to any computing system with an adequate updating program.« less

A semantic medical multimedia retrieval approach using ontology information hiding.

PubMed

Guo, Kehua; Zhang, Shigeng

2013-01-01

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users' query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

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.

AP-102/104 Retrieval control system qualification test procedure

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

RIECK, C.A.

1999-05-18

This Qualification Test Procedure documents the results of the qualification testing that was performed on the Project W-211, ''Initial Tank Retrieval Systems,'' retrieval control system (RCS) for tanks 241-AP-102 and 241-AP-104. The results confirm that the RCS has been programmed correctly and that the two related hardware enclosures have been assembled in accordance with the design documents.

Deeply learnt hashing forests for content based image retrieval in prostate MR images

NASA Astrophysics Data System (ADS)

Shah, Amit; Conjeti, Sailesh; Navab, Nassir; Katouzian, Amin

2016-03-01

Deluge in the size and heterogeneity of medical image databases necessitates the need for content based retrieval systems for their efficient organization. In this paper, we propose such a system to retrieve prostate MR images which share similarities in appearance and content with a query image. We introduce deeply learnt hashing forests (DL-HF) for this image retrieval task. DL-HF effectively leverages the semantic descriptiveness of deep learnt Convolutional Neural Networks. This is used in conjunction with hashing forests which are unsupervised random forests. DL-HF hierarchically parses the deep-learnt feature space to encode subspaces with compact binary code words. We propose a similarity preserving feature descriptor called Parts Histogram which is derived from DL-HF. Correlation defined on this descriptor is used as a similarity metric for retrieval from the database. Validations on publicly available multi-center prostate MR image database established the validity of the proposed approach. The proposed method is fully-automated without any user-interaction and is not dependent on any external image standardization like image normalization and registration. This image retrieval method is generalizable and is well-suited for retrieval in heterogeneous databases other imaging modalities and anatomies.

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.

PubMed

Ma, Ling; Liu, Xiabi; Gao, Yan; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2017-02-01

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. Copyright © 2017 Elsevier Inc. All rights reserved.

Topological Aspects of Information Retrieval.

ERIC Educational Resources Information Center

Egghe, Leo; Rousseau, Ronald

1998-01-01

Discusses topological aspects of theoretical information retrieval, including retrieval topology; similarity topology; pseudo-metric topology; document spaces as topological spaces; Boolean information retrieval as a subsystem of any topological system; and proofs of theorems. (LRW)

Support Vector Machines: Relevance Feedback and Information Retrieval.

ERIC Educational Resources Information Center

Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

2002-01-01

Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

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.

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.

Kingfisher: a system for remote sensing image database management

NASA Astrophysics Data System (ADS)

Bruzzo, Michele; Giordano, Ferdinando; Dellepiane, Silvana G.

2003-04-01

At present retrieval methods in remote sensing image database are mainly based on spatial-temporal information. The increasing amount of images to be collected by the ground station of earth observing systems emphasizes the need for database management with intelligent data retrieval capabilities. The purpose of the proposed method is to realize a new content based retrieval system for remote sensing images database with an innovative search tool based on image similarity. This methodology is quite innovative for this application, at present many systems exist for photographic images, as for example QBIC and IKONA, but they are not able to extract and describe properly remote image content. The target database is set by an archive of images originated from an X-SAR sensor (spaceborne mission, 1994). The best content descriptors, mainly texture parameters, guarantees high retrieval performances and can be extracted without losses independently of image resolution. The latter property allows DBMS (Database Management System) to process low amount of information, as in the case of quick-look images, improving time performance and memory access without reducing retrieval accuracy. The matching technique has been designed to enable image management (database population and retrieval) independently of dimensions (width and height). Local and global content descriptors are compared, during retrieval phase, with the query image and results seem to be very encouraging.

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)

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.

A Semantic Medical Multimedia Retrieval Approach Using Ontology Information Hiding

PubMed Central

Guo, Kehua; Zhang, Shigeng

2013-01-01

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users' query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches. PMID:24082915

Final Report of Research Conducted For DE-AI02-08ER64546

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

Patrick Minnis

2012-03-28

Research was conducted for 3-4 years to use ARM data to validate satellite cloud retrievals and help the development of improved techniques for remotely sensing clouds and radiative fluxes from space to complement the ARM surface measurement program. This final report summarizes the results and publications during the last 2 years of the studies. Since our last report covering the 2009 period, we published four papers that were accepted during the previous reporting period and revised and published a fifth one. Our efforts to intercalibrate selected channels on several polar orbiting and geostationary satellite imagers, which are funded in partmore » by ASR, resulted in methods that were accepted as part of the international Global Space-based Intercalibration System (GSICS) calibration algorithms. We developed a new empirical method for correcting the spectral differences between comparable channels on various imagers that will be used to correct the calibrations of the satellite data used for ARM. We documented our cloud retrievals for the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-Rex; ARM participated with an AAF contribution) in context of the entire experiment. We used our VOCALS satellite data along with the aircraft measurements to better understand the relationships between aerosols and liquid water path in marine stratus clouds. We continued or efforts to validate and improve the satellite cloud retrievals for ARM and using ARM data to validate retrievals for other purposes.« less

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.

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 tutorial on information retrieval: basic terms and concepts

PubMed Central

Zhou, Wei; Smalheiser, Neil R; Yu, Clement

2006-01-01

This informal tutorial is intended for investigators and students who would like to understand the workings of information retrieval systems, including the most frequently used search engines: PubMed and Google. Having a basic knowledge of the terms and concepts of information retrieval should improve the efficiency and productivity of searches. As well, this knowledge is needed in order to follow current research efforts in biomedical information retrieval and text mining that are developing new systems not only for finding documents on a given topic, but extracting and integrating knowledge across documents. PMID:16722601

Exemplary design of a DICOM structured report template for CBIR integration into radiological routine

NASA Astrophysics Data System (ADS)

Welter, Petra; Deserno, Thomas M.; Gülpers, Ralph; Wein, Berthold B.; Grouls, Christoph; Günther, Rolf W.

2010-03-01

The large and continuously growing amount of medical image data demands access methods with regards to content rather than simple text-based queries. The potential benefits of content-based image retrieval (CBIR) systems for computer-aided diagnosis (CAD) are evident and have been approved. Still, CBIR is not a well-established part of daily routine of radiologists. We have already presented a concept of CBIR integration for the radiology workflow in accordance with the Integrating the Healthcare Enterprise (IHE) framework. The retrieval result is composed as a Digital Imaging and Communication in Medicine (DICOM) Structured Reporting (SR) document. The use of DICOM SR provides interchange with PACS archive and image viewer. It offers the possibility of further data mining and automatic interpretation of CBIR results. However, existing standard templates do not address the domain of CBIR. We present a design of a SR template customized for CBIR. Our approach is based on the DICOM standard templates and makes use of the mammography and chest CAD SR templates. Reuse of approved SR sub-trees promises a reliable design which is further adopted to the CBIR domain. We analyze the special CBIR requirements and integrate the new concept of similar images into our template. Our approach also includes the new concept of a set of selected images for defining the processed images for CBIR. A commonly accepted pre-defined template for the presentation and exchange of results in a standardized format promotes the widespread application of CBIR in radiological routine.

NoSQL: collection document and cloud by using a dynamic web query form

NASA Astrophysics Data System (ADS)

Abdalla, Hemn B.; Lin, Jinzhao; Li, Guoquan

2015-07-01

Mongo-DB (from "humongous") is an open-source document database and the leading NoSQL database. A NoSQL (Not Only SQL, next generation databases, being non-relational, deal, open-source and horizontally scalable) presenting a mechanism for storage and retrieval of documents. Previously, we stored and retrieved the data using the SQL queries. Here, we use the MonogoDB that means we are not utilizing the MySQL and SQL queries. Directly importing the documents into our Drives, retrieving the documents on that drive by not applying the SQL queries, using the IO BufferReader and Writer, BufferReader for importing our type of document files to my folder (Drive). For retrieving the document files, the usage is BufferWriter from the particular folder (or) Drive. In this sense, providing the security for those storing files for what purpose means if we store the documents in our local folder means all or views that file and modified that file. So preventing that file, we are furnishing the security. The original document files will be changed to another format like in this paper; Binary format is used. Our documents will be converting to the binary format after that direct storing in one of our folder, that time the storage space will provide the private key for accessing that file. Wherever any user tries to discover the Document files means that file data are in the binary format, the document's file owner simply views that original format using that personal key from receive the secret key from the cloud.

Combining approaches to on-line handwriting information retrieval

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Viard-Gaudin, Christian; Morin, Emmanuel

2010-01-01

In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

The "Generality" Effect and the Retrieval Evaluation for Large Collections

ERIC Educational Resources Information Center

Salton, Gerard

1972-01-01

The role of the generality effect in retrieval system evaluation is assessed, and evaluation results are given for the comparison of several document collections of distinct size and generality in the areas of documentation and aerodynamics. (14 references) (Author)

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.

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.

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

Substance use disorders in Arab countries: research activity and bibliometric analysis

PubMed Central

2014-01-01

Background Substance use disorders, which include substance abuse and substance dependence, are present in all regions of the world including Middle Eastern Arab countries. Bibliometric analysis is an increasingly used tool for research assessment. The main objective of this study was to assess research productivity in the field of substance use disorders in Arab countries using bibliometric indicators. Methodology Original or review research articles authored or co-authored by investigators from Arab countries about substance use disorders during the period 1900 – 2013 were retrieved using the ISI Web of Science database. Research activity was assessed by analyzing the annual research productivity, contribution of each Arab country, names of journals, citations, and types of abused substances. Results Four hundred and thirteen documents in substance use disorders were retrieved. Annual research productivity was low but showed a significant increase in the last few years. In terms of quantity, Kingdom of Saudi Arabia (83 documents) ranked first in research about substance use disorders while Lebanon (17.4 documents per million) ranked first in terms of number of documents published per million inhabitants. Retrieved documents were found in different journal titles and categories, mostly in Drug and Alcohol Dependence Journal. Authors from USA appeared in 117 documents published by investigators from Arab countries. Citation analysis of retrieved documents showed that the average citation per document was 10.76 and the h - index was 35. The majority of retrieved documents were about tobacco and smoking (175 documents) field while alcohol consumption and abuse research was the least with 69 documents. Conclusion The results obtained suggest that research in this field was largely neglected in the past. However, recent research interest was observed. Research output on tobacco and smoking was relatively high compared to other substances of abuse like illicit drugs and medicinal agents. Governmental funding for academics and mental health graduate programs to do research in the field of substance use disorders is highly recommended. PMID:25148888

Multiple Object Retrieval in Image Databases Using Hierarchical Segmentation Tree

ERIC Educational Resources Information Center

Chen, Wei-Bang

2012-01-01

The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The…

Development of the Defense Documentation Center Remote On-Line Retrieval System - Past, Present and Future.

ERIC Educational Resources Information Center

Bennertz, Richard K.

The document highlights in nontechnical language the development of the Defense Documentation Center (DDC) Remote On-Line Retrieval System from its inception in 1967 to what is planned. It describes in detail the current operating system, equipment configuration and associated costs, user training and system evaluation and may be of value to other…

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.

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

Guyer, H.B.; McChesney, C.A.

The overall primary Objective of HDAR is to create a repository of historical personnel security documents and provide the functionality needed for archival and retrieval use by other software modules and application users of the DISS/ET system. The software product to be produced from this specification is the Historical Document Archival and Retrieval Subsystem The product will provide the functionality to capture, retrieve and manage documents currently contained in the personnel security folders in DOE Operations Offices vaults at various locations across the United States. The long-term plan for DISS/ET includes the requirement to allow for capture and storage ofmore » arbitrary, currently undefined, clearance-related documents that fall outside the scope of the ``cradle-to-grave`` electronic processing provided by DISS/ET. However, this requirement is not within the scope of the requirements specified in this document.« less

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

NASA Astrophysics Data System (ADS)

Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

2013-02-01

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

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

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.

Information Retrieval in Biomedical Research: From Articles to Datasets

ERIC Educational Resources Information Center

Wei, Wei

2017-01-01

Information retrieval techniques have been applied to biomedical research for a variety of purposes, such as textual document retrieval and molecular data retrieval. As biomedical research evolves over time, information retrieval is also constantly facing new challenges, including the growing number of available data, the emerging new data types,…

Mobile object retrieval in server-based image databases

NASA Astrophysics Data System (ADS)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval

DTIC Science & Technology

2006-01-01

The Effect of Bilingual Term List Size on Dictionary -Based Cross-Language Information Retrieval Dina Demner-Fushman Department of Computer Science... dictionary -based Cross-Language Information Retrieval (CLIR), in which the goal is to find documents written in one natural language based on queries that...in which the documents are written. In dictionary -based CLIR techniques, the princi- pal source of translation knowledge is a translation lexicon

Development of a full-text information retrieval system

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

Keizo Oyama; AKira Miyazawa, Atsuhiro Takasu; Kouji Shibano

The authors have executed a project to realize a full-text information retrieval system. The system is designed to deal with a document database comprising full text of a large number of documents such as academic papers. The document structures are utilized in searching and extracting appropriate information. The concept of structure handling and the configuration of the system are described in this paper.

Third Annual Symposium on Document Analysis and Information Retrieval

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

Not Available

This document presents papers of the Third Annual Symposium on Document Analysis and Information Retrieval at the Information Science Research-l Institute at the University of Nevada, Las Vegas (UNLV/ISRI). Of the 60 papers submitted, 25 were accepted for oral presentation and 9 as poster papers. Both oral presentations and poster papers are included in these Proceedings. The individual papers have been cataloged separately.

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 mathematical model of neuro-fuzzy approximation in image classification

NASA Astrophysics Data System (ADS)

Gopalan, Sasi; Pinto, Linu; Sheela, C.; Arun Kumar M., N.

2016-06-01

Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.

Challenges and methodology for indexing the computerized patient record.

PubMed

Ehrler, Frédéric; Ruch, Patrick; Geissbuhler, Antoine; Lovis, Christian

2007-01-01

Patient records contain most crucial documents for managing the treatments and healthcare of patients in the hospital. Retrieving information from these records in an easy, quick and safe way helps care providers to save time and find important facts about their patient's health. This paper presents the scalability issues induced by the indexing and the retrieval of the information contained in the patient records. For this study, EasyIR, an information retrieval tool performing full text queries and retrieving the related documents has been used. An evaluation of the performance reveals that the indexing process suffers from overhead consequence of the particular structure of the patient records. Most IR tools are designed to manage very large numbers of documents in a single index whereas in our hypothesis, one index per record, which usually implies few documents, has been imposed. As the number of modifications and creations of patient records are significant in a day, using a specialized and efficient indexation tool is required.

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 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.

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.

Comparing features sets for content-based image retrieval in a medical-case database

NASA Astrophysics Data System (ADS)

Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

2004-04-01

Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the texture analysis also leads to improved results but response time is going up equally due to the larger feature space. CBIRSs can be of great use in managing large medical image databases. They allow to find images that might otherwise be lost for research and publications. They also give students students the possibility to navigate within large image repositories. In the future, CBIR might also become more important in case-based reasoning and evidence-based medicine to support the diagnostics because first studies show good results.

A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-04-01

Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system. Passage retrieval in open-domain QA is a longstanding challenge widely studied over the last decades. However, it still requires further efforts in biomedical QA. In this paper, we present a new biomedical passage retrieval method based on Stanford CoreNLP sentence/passage length, probabilistic information retrieval (IR) model and UMLS concepts. In the proposed method, we first use our document retrieval system based on PubMed search engine and UMLS similarity to retrieve relevant documents to a given biomedical question. We then take the abstracts from the retrieved documents and use Stanford CoreNLP for sentence splitter to make a set of sentences, i.e., candidate passages. Using stemmed words and UMLS concepts as features for the BM25 model, we finally compute the similarity scores between the biomedical question and each of the candidate passages and keep the N top-ranked ones. Experimental evaluations performed on large standard datasets, provided by the BioASQ challenge, show that the proposed method achieves good performances compared with the current state-of-the-art methods. The proposed method significantly outperforms the current state-of-the-art methods by an average of 6.84% in terms of mean average precision (MAP). We have proposed an efficient passage retrieval method which can be used to retrieve relevant passages in biomedical QA systems with high mean average precision. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

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

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.

An Approach to a Digital Library of Newspapers.

ERIC Educational Resources Information Center

Arambura Cabo, Maria Jose; Berlanga Llavori, Rafael

1997-01-01

Presents a new application for retrieving news from a large electronic bank of newspapers that is intended to manage past issues of newspapers. Highlights include a data model for newspapers, including metadata and metaclasses; document definition language; document retrieval language; and memory organization and indexes. (Author/LRW)

The JPL Library information retrieval system

NASA Technical Reports Server (NTRS)

Walsh, J.

1975-01-01

The development, capabilities, and products of the computer-based retrieval system of the Jet Propulsion Laboratory Library are described. The system handles books and documents, produces a book catalog, and provides a machine search capability. Programs and documentation are available to the public through NASA's computer software dissemination program.

Querying and Ranking XML Documents.

ERIC Educational Resources Information Center

Schlieder, Torsten; Meuss, Holger

2002-01-01

Discussion of XML, information retrieval, precision, and recall focuses on a retrieval technique that adopts the similarity measure of the vector space model, incorporates the document structure, and supports structured queries. Topics include a query model based on tree matching; structured queries and term-based ranking; and term frequency and…

Documents Similarity Measurement Using Field Association Terms.

ERIC Educational Resources Information Center

Atlam, El-Sayed; Fuketa, M.; Morita, K.; Aoe, Jun-ichi

2003-01-01

Discussion of text analysis and information retrieval and measurement of document similarity focuses on a new text manipulation system called FA (field association)-Sim that is useful for retrieving information in large heterogeneous texts and for recognizing content similarity in text excerpts. Discusses recall and precision, automatic indexing…

Enhanced optical security by using information carrier digital screening

NASA Astrophysics Data System (ADS)

Koltai, Ferenc; Adam, Bence

2004-06-01

Jura has developed different security features based on Information Carrier Digital Screening. Substance of such features is that a non-visible secondary image is encoded in a visible primary image. The encoded image will be visible only by using a decoding device. One of such developments is JURA's Invisible Personal Information (IPI) is widely used in high security documents, where personal data of the document holder are encoded in the screen of the document holder's photography and they can be decoded by using an optical decoding device. In order to make document verification fully automated, enhance security and eliminate human factors, digital version of IPI, the D-IPI was developed. A special 2D-barcode structure was designed, which contains sufficient quantity of encoded digital information and can be embedded into the photo. Other part of Digital-IPI is the reading software, that is able to retrieve the encoded information with high reliability. The reading software developed with a specific 2D structure is providing the possibility of a forensic analysis. Such analysis will discover all kind of manipulations -- globally, if the photography was simply changed and selectively, if only part of the photography was manipulated. Digital IPI is a good example how benefits of digital technology can be exploited by using optical security and how technology for optical security can be converted into digital technology. The D-IPI process is compatible with all current personalization printers and materials (polycarbonate, PVC, security papers, Teslin-foils, etc.) and can provide any document with enhanced security and tamper-resistance.

COM3/369: Knowledge-based Information Systems: A new approach for the representation and retrieval of medical information

PubMed Central

Mann, G; Birkmann, C; Schmidt, T; Schaeffler, V

1999-01-01

Introduction Present solutions for the representation and retrieval of medical information from online sources are not very satisfying. Either the retrieval process lacks of precision and completeness the representation does not support the update and maintenance of the represented information. Most efforts are currently put into improving the combination of search engines and HTML based documents. However, due to the current shortcomings of methods for natural language understanding there are clear limitations to this approach. Furthermore, this approach does not solve the maintenance problem. At least medical information exceeding a certain complexity seems to afford approaches that rely on structured knowledge representation and corresponding retrieval mechanisms. Methods Knowledge-based information systems are based on the following fundamental ideas. The representation of information is based on ontologies that define the structure of the domain's concepts and their relations. Views on domain models are defined and represented as retrieval schemata. Retrieval schemata can be interpreted as canonical query types focussing on specific aspects of the provided information (e.g. diagnosis or therapy centred views). Based on these retrieval schemata it can be decided which parts of the information in the domain model must be represented explicitly and formalised to support the retrieval process. As representation language propositional logic is used. All other information can be represented in a structured but informal way using text, images etc. Layout schemata are used to assign layout information to retrieved domain concepts. Depending on the target environment HTML or XML can be used. Results Based on this approach two knowledge-based information systems have been developed. The 'Ophthalmologic Knowledge-based Information System for Diabetic Retinopathy' (OKIS-DR) provides information on diagnoses, findings, examinations, guidelines, and reference images related to diabetic retinopathy. OKIS-DR uses combinations of findings to specify the information that must be retrieved. The second system focuses on nutrition related allergies and intolerances. Information on allergies and intolerances of a patient are used to retrieve general information on the specified combination of allergies and intolerances. As a special feature the system generates tables showing food types and products that are tolerated or not tolerated by patients. Evaluation by external experts and user groups showed that the described approach of knowledge-based information systems increases the precision and completeness of knowledge retrieval. Due to the structured and non-redundant representation of information the maintenance and update of the information can be simplified. Both systems are available as WWW based online knowledge bases and CD-ROMs (cf. http://mta.gsf.de topic: products).

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.

Content-based image retrieval on mobile devices

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Abdullah, Shafaq; Kiranyaz, Serkan; Gabbouj, Moncef

2005-03-01

Content-based image retrieval area possesses a tremendous potential for exploration and utilization equally for researchers and people in industry due to its promising results. Expeditious retrieval of desired images requires indexing of the content in large-scale databases along with extraction of low-level features based on the content of these images. With the recent advances in wireless communication technology and availability of multimedia capable phones it has become vital to enable query operation in image databases and retrieve results based on the image content. In this paper we present a content-based image retrieval system for mobile platforms, providing the capability of content-based query to any mobile device that supports Java platform. The system consists of light-weight client application running on a Java enabled device and a server containing a servlet running inside a Java enabled web server. The server responds to image query using efficient native code from selected image database. The client application, running on a mobile phone, is able to initiate a query request, which is handled by a servlet in the server for finding closest match to the queried image. The retrieved results are transmitted over mobile network and images are displayed on the mobile phone. We conclude that such system serves as a basis of content-based information retrieval on wireless devices and needs to cope up with factors such as constraints on hand-held devices and reduced network bandwidth available in mobile environments.

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.

Automation of the CAS Document Delivery Service.

ERIC Educational Resources Information Center

Steensland, M. C.; Soukup, K. M.

1986-01-01

The automation of online order retrieval for Chemical Abstracts Service Document Delivery Service was accomplished by shifting to an order retrieval/dispatch process linked to a Unix network. The Unix-based environment, its terminal emulation, page-break, and user-friendly interface software, and later enhancements are reviewed. Resultant increase…

A Vector Space Model for Automatic Indexing.

ERIC Educational Resources Information Center

Salton, G.; And Others

In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other, or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; that is, retrieval performance correlates inversely…

Document Storage and Retrieval in the Electronic Office.

ERIC Educational Resources Information Center

Ashford, John

1985-01-01

Proposals are made for practical approaches to the design of electronic office systems to provide for the effective storage and retrieval of the documents that they generate. Problems of records management and requirements to be met by the designer of an electronic office system are highlighted. Nineteen references are cited. (EJS)

Information Retrieval Using UMLS-based Structured Queries

PubMed Central

Fagan, Lawrence M.; Berrios, Daniel C.; Chan, Albert; Cucina, Russell; Datta, Anupam; Shah, Maulik; Surendran, Sujith

2001-01-01

During the last three years, we have developed and described components of ELBook, a semantically based information-retrieval system [1-4]. Using these components, domain experts can specify a query model, indexers can use the query model to index documents, and end-users can search these documents for instances of indexed queries.

ASSOCIATIVE ADJUSTMENTS TO REDUCE ERRORS IN DOCUMENT SEARCHING.

ERIC Educational Resources Information Center

BRYANT, EDWARD C.; AND OTHERS

ASSOCIATIVE ADJUSTMENTS TO A DOCUMENT FILE ARE CONSIDERED AS A MEANS FOR IMPROVING RETRIEVAL. A THEORETICAL INVESTIGATION OF THE STATISTICAL PROPERTIES OF A GENERALIZED MISMATCH MEASURE WAS CARRIED OUT AND IMPROVEMENTS IN RETRIEVAL RESULTING FROM PERFORMING ASSOCIATIVE REGRESSION ADJUSTMENTS ON DATA FILE WERE EXAMINED BOTH FROM THE THEORETICAL AND…

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.

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…

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

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

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.

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.

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.

Sampling criteria in multicollection searching.

NASA Astrophysics Data System (ADS)

Gilio, A.; Scozzafava, R.; Marchetti, P. G.

In the first stage of the document retrieval process, no information concerning relevance of a particular document is available. On the other hand, computer implementation requires that the analysis be made only for a sample of retrieved documents. This paper addresses the significance and suitability of two different sampling criteria for a multicollection online search facility. The inevitability of resorting to a logarithmic criterion in order to achieve a "spread of representativeness" from the multicollection is demonstrated.

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.

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.

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.

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.

36 CFR 1238.12 - What documentation is required for microfilmed records?

Code of Federal Regulations, 2011 CFR

2011-07-01

... microforms capture all information contained on the source documents and that they can be used for the... retrieval and use. Agencies must: (a) Arrange, describe, and index the filmed records to permit retrieval of... titling target or header. For fiche, place the titling information in the first frame if the information...

36 CFR § 1238.12 - What documentation is required for microfilmed records?

Code of Federal Regulations, 2013 CFR

2013-07-01

... microforms capture all information contained on the source documents and that they can be used for the... retrieval and use. Agencies must: (a) Arrange, describe, and index the filmed records to permit retrieval of... titling target or header. For fiche, place the titling information in the first frame if the information...

36 CFR 1238.12 - What documentation is required for microfilmed records?

Code of Federal Regulations, 2014 CFR

2014-07-01

... microforms capture all information contained on the source documents and that they can be used for the... retrieval and use. Agencies must: (a) Arrange, describe, and index the filmed records to permit retrieval of... titling target or header. For fiche, place the titling information in the first frame if the information...

36 CFR 1238.12 - What documentation is required for microfilmed records?

Code of Federal Regulations, 2012 CFR

2012-07-01

... microforms capture all information contained on the source documents and that they can be used for the... retrieval and use. Agencies must: (a) Arrange, describe, and index the filmed records to permit retrieval of... titling target or header. For fiche, place the titling information in the first frame if the information...

36 CFR 1238.12 - What documentation is required for microfilmed records?

Code of Federal Regulations, 2010 CFR

2010-07-01

... microforms capture all information contained on the source documents and that they can be used for the... retrieval and use. Agencies must: (a) Arrange, describe, and index the filmed records to permit retrieval of... titling target or header. For fiche, place the titling information in the first frame if the information...

Information Storage and Retrieval, Scientific Report No. ISR-15.

ERIC Educational Resources Information Center

Salton, Gerard

Several algorithms were investigated which would allow a user to interact with an automatic document retrieval system by requesting relevance judgments on selected sets of documents. Two viewpoints were taken in evaluation. One measured the movement of queries toward the optimum query as defined by Rocchio; the other measured the retrieval…

The Future of Amphibious Operations: Shaping the Expeditionary Strike Group to Fight in the Joint Task Force

DTIC Science & Technology

2010-02-01

1 Charles E. Wilhelm, Expeditionary Warfare.marine corps gazette, 79(6), 28-30. Retrieved October 15, 2009, from Career and Technical Education . (Document...Expeditionary warfare.marine corps gazette, 79(6), 28- 30. Retrieved October 15, 2009, from Career and Technical Education . (Document ID: 4455650

On the Delusiveness of Adopting a Common Space for Modeling IR Objects: Are Queries Documents?

ERIC Educational Resources Information Center

Bollmann-Sdorra, Peter; Raghavan, Vjay V.

1993-01-01

Proposes that document space and query space have different structures in information retrieval and discusses similarity measures, term independence, and linear structure. Examples are given using the retrieval functions of dot-product, the cosine measure, the coefficient of Jaccard, and the overlap function. (Contains 28 references.) (LRW)

Change detection on UGV patrols with respect to a reference tour using VIS imagery

NASA Astrophysics Data System (ADS)

Müller, Thomas

2015-05-01

Autonomous driving robots (UGVs, Unmanned Ground Vehicles) equipped with visual-optical (VIS) cameras offer a high potential to automatically detect suspicious occurrences and dangerous or threatening situations on patrol. In order to explore this potential, the scene of interest is recorded first on a reference tour representing the 'everything okay' situation. On further patrols changes are detected with respect to the reference in a two step processing scheme. In the first step, an image retrieval is done to find the reference images that are closest to the current camera image on patrol. This is done efficiently based on precalculated image-to-image registrations of the reference by optimizing image overlap in a local reference search (after a global search when that is needed). In the second step, a robust spatio-temporal change detection is performed that widely compensates 3-D parallax according to variations of the camera position. Various results document the performance of the presented approach.

Word Spotting and Recognition with Embedded Attributes.

PubMed

Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

2014-12-01

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

PACS and electronic health records

NASA Astrophysics Data System (ADS)

Cohen, Simona; Gilboa, Flora; Shani, Uri

2002-05-01

Electronic Health Record (EHR) is a major component of the health informatics domain. An important part of the EHR is the medical images obtained over a patient's lifetime and stored in diverse PACS. The vision presented in this paper is that future medical information systems will convert data from various medical sources -- including diverse modalities, PACS, HIS, CIS, RIS, and proprietary systems -- to HL7 standard XML documents. Then, the various documents are indexed and compiled to EHRs, upon which complex queries can be posed. We describe the conversion of data retrieved from PACS systems through DICOM to HL7 standard XML documents. This enables the EHR system to answer queries such as 'Get all chest images of patients at the age of 20-30, that have blood type 'A' and are allergic to pine trees', which a single PACS cannot answer. The integration of data from multiple sources makes our approach capable of delivering such answers. It enables the correlation of medical, demographic, clinical, and even genetic information. In addition, by fully indexing all the tagged data in DICOM objects, it becomes possible to offer access to huge amounts of valuable data, which can be better exploited in the specific radiology domain.

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.

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.

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.

iSMART: Ontology-based Semantic Query of CDA Documents

PubMed Central

Liu, Shengping; Ni, Yuan; Mei, Jing; Li, Hanyu; Xie, Guotong; Hu, Gang; Liu, Haifeng; Hou, Xueqiao; Pan, Yue

2009-01-01

The Health Level 7 Clinical Document Architecture (CDA) is widely accepted as the format for electronic clinical document. With the rich ontological references in CDA documents, the ontology-based semantic query could be performed to retrieve CDA documents. In this paper, we present iSMART (interactive Semantic MedicAl Record reTrieval), a prototype system designed for ontology-based semantic query of CDA documents. The clinical information in CDA documents will be extracted into RDF triples by a declarative XML to RDF transformer. An ontology reasoner is developed to infer additional information by combining the background knowledge from SNOMED CT ontology. Then an RDF query engine is leveraged to enable the semantic queries. This system has been evaluated using the real clinical documents collected from a large hospital in southern China. PMID:20351883

Concept Based Tie-breaking and Maximal Marginal Relevance Retrieval in Microblog Retrieval

DTIC Science & Technology

2014-11-01

the same score, another singal will be used to rank these documents to break the ties , but the relative orders of other documents against these...documents remain the same. The tie- breaking step above is repeatedly applied to further break ties until all candidate signals are applied and the ranking...searched it on the Yahoo! search engine, which returned some query sug- gestions for the query. The original queries as well as their query suggestions

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.

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.

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.

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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.

Improving biomedical information retrieval by linear combinations of different query expansion techniques.

PubMed

Abdulla, Ahmed AbdoAziz Ahmed; Lin, Hongfei; Xu, Bo; Banbhrani, Santosh Kumar

2016-07-25

Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user's needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user's information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries. The retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %. We propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher-quality search results.

The Effect of Indexing Exhaustivity on Retrieval Performance.

ERIC Educational Resources Information Center

Burgin, Robert

1991-01-01

Describes results of a study that investigated the effect of variations in indexing exhaustivity on retrieval performance in a vector space retrieval system. The test collection of documents in the National Library of Medicine's Medline file indexed under cystic fibrosis is described, and use of the SMART information retrieval system is discussed.…

Finding Information on the World Wide Web: The Retrieval Effectiveness of Search Engines.

ERIC Educational Resources Information Center

Pathak, Praveen; Gordon, Michael

1999-01-01

Describes a study that examined the effectiveness of eight search engines for the World Wide Web. Calculated traditional information-retrieval measures of recall and precision at varying numbers of retrieved documents to use as the bases for statistical comparisons of retrieval effectiveness. Also examined the overlap between search engines.…

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

STATISTICAL DATA ON CHEMICAL COMPOUNDS.

DTIC Science & Technology

DATA STORAGE SYSTEMS, FEASIBILITY STUDIES, COMPUTERS, STATISTICAL DATA , DOCUMENTS, ARMY...CHEMICAL COMPOUNDS, INFORMATION RETRIEVAL), (*INFORMATION RETRIEVAL, CHEMICAL COMPOUNDS), MOLECULAR STRUCTURE, BIBLIOGRAPHIES, DATA PROCESSING

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.

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.

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.

The Effectiveness of Stemming for Natural-Language Access to Slovene Textual Data.

ERIC Educational Resources Information Center

Popovic, Mirko; Willett, Peter

1992-01-01

Reports on the use of stemming for Slovene language documents and queries in free-text retrieval systems and demonstrates that an appropriate stemming algorithm results in an increase in retrieval effectiveness when compared with nonstemming processing. A comparison is made with stemming of English versions of the same documents and queries. (24…

The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems.

ERIC Educational Resources Information Center

Peat, Helen J.; Willett, Peter

1991-01-01

Identifies limitations in the use of term co-occurrence data as a basis for automatic query expansion in natural language document retrieval systems. The use of similarity coefficients to calculate the degree of similarity between pairs of terms is explained, and frequency and discriminatory characteristics for nearest neighbors of query terms are…

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

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

Cigarette package design: opportunities for disease prevention.

PubMed

Difranza, J R; Clark, D M; Pollay, R W

2002-06-15

To learn how cigarette packages are designed and to determine to what extent cigarette packages are designed to target children. A computer search was made of all Internet websites that post tobacco industry documents using the search terms: packaging, package design, package study, box design, logo, trademark and design study. All documents were retrieved electronically and analyzed by the first author for recurrent themes. Cigarette manufacturers devote a great deal of attention and expense to package design because it is central to their efforts to create brand images. Colors, graphic elements, proportioning, texture, materials and typography are tested and used in various combinations to create the desired product and user images. Designs help to create the perceived product attributes and project a personality image of the user with the intent of fulfilling the psychological needs of the targeted type of smoker. The communication of these images and attributes is conducted through conscious and subliminal processes. Extensive testing is conducted using a variety of qualitative and quantitative research techniques. The promotion of tobacco products through appealing imagery cannot be stopped without regulating the package design. The same marketing research techniques used by the tobacco companies can be used to design generic packaging and more effective warning labels targeted at specific consumers.

Cigarette package design: opportunities for disease prevention

PubMed Central

DiFranza, JR; Clark, DM; Pollay, RW

2003-01-01

Objective To learn how cigarette packages are designed and to determine to what extent cigarette packages are designed to target children. Methods A computer search was made of all Internet websites that post tobacco industry documents using the search terms: packaging, package design, package study, box design, logo, trademark and design study. All documents were retrieved electronically and analyzed by the first author for recurrent themes. Data Synthesis Cigarette manufacturers devote a great deal of attention and expense to package design because it is central to their efforts to create brand images. Colors, graphic elements, proportioning, texture, materials and typography are tested and used in various combinations to create the desired product and user images. Designs help to create the perceived product attributes and project a personality image of the user with the intent of fulfilling the psychological needs of the targeted type of smoker. The communication of these images and attributes is conducted through conscious and subliminal processes. Extensive testing is conducted using a variety of qualitative and quantitative research techniques. Conclusion The promotion of tobacco products through appealing imagery cannot be stopped without regulating the package design. The same marketing research techniques used by the tobacco companies can be used to design generic packaging and more effective warning labels targeted at specific consumers. PMID:19570250

Cigarette package design: opportunities for disease prevention

PubMed Central

DiFranza, JR; Clark, DM; Pollay, RW

2003-01-01

Objective To learn how cigarette packages are designed and to determine to what extent cigarette packages are designed to target children. Methods A computer search was made of all Internet websites that post tobacco industry documents using the search terms: packaging, package design, package study, box design, logo, trademark and design study. All documents were retrieved electronically and analyzed by the first author for recurrent themes. Data Synthesis Cigarette manufacturers devote a great deal of attention and expense to package design because it is central to their efforts to create brand images. Colors, graphic elements, proportioning, texture, materials and typography are tested and used in various combinations to create the desired product and user images. Designs help to create the perceived product attributes and project a personality image of the user with the intent of fulfilling the psychological needs of the targeted type of smoker. The communication of these images and attributes is conducted through conscious and subliminal processes. Extensive testing is conducted using a variety of qualitative and quantitative research techniques. Conclusion The promotion of tobacco products through appealing imagery cannot be stopped without regulating the package design. The same marketing research techniques used by the tobacco companies can be used to design generic packaging and more effective warning labels targeted at specific consumers.

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 comparison of Boolean-based retrieval to the WAIS system for retrieval of aeronautical information

NASA Technical Reports Server (NTRS)

Marchionini, Gary; Barlow, Diane

1994-01-01

An evaluation of an information retrieval system using a Boolean-based retrieval engine and inverted file architecture and WAIS, which uses a vector-based engine, was conducted. Four research questions in aeronautical engineering were used to retrieve sets of citations from the NASA Aerospace Database which was mounted on a WAIS server and available through Dialog File 108 which served as the Boolean-based system (BBS). High recall and high precision searches were done in the BBS and terse and verbose queries were used in the WAIS condition. Precision values for the WAIS searches were consistently above the precision values for high recall BBS searches and consistently below the precision values for high precision BBS searches. Terse WAIS queries gave somewhat better precision performance than verbose WAIS queries. In every case, a small number of relevant documents retrieved by one system were not retrieved by the other, indicating the incomplete nature of the results from either retrieval system. Relevant documents in the WAIS searches were found to be randomly distributed in the retrieved sets rather than distributed by ranks. Advantages and limitations of both types of systems are discussed.

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

PubMed

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

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.

Calibration and Vegetation Field Spectra Collection for the 2000 AVIRIS Hawaii Deployment

NASA Technical Reports Server (NTRS)

Dennison, Philip E.; Gardner, Margaret E.; Roberts, Dar A.; Green, Robert O.

2001-01-01

As part of the April 2000 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Hawaii deployment, two researchers from the University of California, Santa Barbara, were sent to Hawaii to collect supporting field data. The primary goal of the fieldwork was to obtain spectra of bright targets to be used for retrieving surface reflectance from AVIRIS imagery. Secondary goals included recording the spectra of dominant vegetation, marking the position of homogeneous land cover for use as potential image endmembers (PIEs), and recording firsthand impressions of cover types. Primary and secondary goals were met. Spectra were recorded for 12 calibration targets on 5 islands and spectra were obtained for 61 vegetation species. Twenty PIEs were located, and video was used to document cover at 56 locations.

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.

Profiling of Atmospheric Water Vapor from the SSM/T-2 Radiometric Measurements

NASA Technical Reports Server (NTRS)

Wang, J. R.

2000-01-01

An advantage of using the millimeter-wave measurements for water vapor profiling is the ability to probe beyond a moderate cloud cover. Such a capability has been demonstrated from an airborne MIR (Millimeter-wave Imaging Radiometer) flight over the Pacific Ocean during an intense observation period of TOGA/COARE (Tropical Ocean Global Atmosphere/ Couple Ocean Atmospheric Response Experiment) in early 1993. A Cloud Lidar System (CLS) and MODIS Airborne Simulator (MAS) were on board the same aircraft to identify the presence of clouds and cloud type. The retrieval algorithm not only provides output of a water vapor profile, but also the cloud liquid water and approximate cloud altitude required to satisfy convergence of the retrieval. The validity of these cloud parameters has not been verified previously. In this document, these cloud parameters are compared with those derived from concurrent measurements from the CLS and AMPR (Advanced Microwave Precipitation Radiometer).

Natural language information retrieval in digital libraries

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

Strzalkowski, T.; Perez-Carballo, J.; Marinescu, M.

In this paper we report on some recent developments in joint NYU and GE natural language information retrieval system. The main characteristic of this system is the use of advanced natural language processing to enhance the effectiveness of term-based document retrieval. The system is designed around a traditional statistical backbone consisting of the indexer module, which builds inverted index files from pre-processed documents, and a retrieval engine which searches and ranks the documents in response to user queries. Natural language processing is used to (1) preprocess the documents in order to extract content-carrying terms, (2) discover inter-term dependencies and buildmore » a conceptual hierarchy specific to the database domain, and (3) process user`s natural language requests into effective search queries. This system has been used in NIST-sponsored Text Retrieval Conferences (TREC), where we worked with approximately 3.3 GBytes of text articles including material from the Wall Street Journal, the Associated Press newswire, the Federal Register, Ziff Communications`s Computer Library, Department of Energy abstracts, U.S. Patents and the San Jose Mercury News, totaling more than 500 million words of English. The system have been designed to facilitate its scalability to deal with ever increasing amounts of data. In particular, a randomized index-splitting mechanism has been installed which allows the system to create a number of smaller indexes that can be independently and efficiently searched.« less

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.

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.

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.

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.

A vision system planner for increasing the autonomy of the Extravehicular Activity Helper/Retriever

NASA Technical Reports Server (NTRS)

Magee, Michael

1993-01-01

The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools and equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This paper documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios are discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, and complementary usage of the sensors and redundant capabilities.

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.

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.

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

Crain, Steven P.; Yang, Shuang-Hong; Zha, Hongyuan

Access to health information by consumers is ham- pered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, com- mon and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations us- ing consumermore » questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.« less

Indexing and Retrieval for the Web.

ERIC Educational Resources Information Center

Rasmussen, Edie M.

2003-01-01

Explores current research on indexing and ranking as retrieval functions of search engines on the Web. Highlights include measuring search engine stability; evaluation of Web indexing and retrieval; Web crawlers; hyperlinks for indexing and ranking; ranking for metasearch; document structure; citation indexing; relevance; query evaluation;…

Theoretical and Philosophical Aspects of Knowledge Management (SIG KM).

ERIC Educational Resources Information Center

Day, Ronald E.

2000-01-01

This session abstract discusses the history, philosophy, and theories of knowledge management to better understand its social and organizational potentials and limitations. Topics include determinacy of sense, information retrieval, and the Data Retrieval Model versus the Document Retrieval Model; discussions about knowledge; and surplus…

Advanced Feedback Methods in Information Retrieval.

ERIC Educational Resources Information Center

Salton, G.; And Others

1985-01-01

In this study, automatic feedback techniques are applied to Boolean query statements in online information retrieval to generate improved query statements based on information contained in previously retrieved documents. Feedback operations are carried out using conventional Boolean logic and extended logic. Experimental output is included to…

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

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.

Initial retrieval sequence and blending strategy

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

Pemwell, D.L.; Grenard, C.E.

1996-09-01

This report documents the initial retrieval sequence and the methodology used to select it. Waste retrieval, storage, pretreatment and vitrification were modeled for candidate single-shell tank retrieval sequences. Performance of the sequences was measured by a set of metrics (for example,high-level waste glass volume, relative risk and schedule).Computer models were used to evaluate estimated glass volumes,process rates, retrieval dates, and blending strategy effects.The models were based on estimates of component inventories and concentrations, sludge wash factors and timing, retrieval annex limitations, etc.

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

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

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.

Technique for information retrieval using enhanced latent semantic analysis generating rank approximation matrix by factorizing the weighted morpheme-by-document matrix

DOEpatents

Chew, Peter A; Bader, Brett W

2012-10-16

A technique for information retrieval includes parsing a corpus to identify a number of wordform instances within each document of the corpus. A weighted morpheme-by-document matrix is generated based at least in part on the number of wordform instances within each document of the corpus and based at least in part on a weighting function. The weighted morpheme-by-document matrix separately enumerates instances of stems and affixes. Additionally or alternatively, a term-by-term alignment matrix may be generated based at least in part on the number of wordform instances within each document of the corpus. At least one lower rank approximation matrix is generated by factorizing the weighted morpheme-by-document matrix and/or the term-by-term alignment matrix.

Words, concepts, or both: optimal indexing units for automated information retrieval.

PubMed Central

Hersh, W. R.; Hickam, D. H.; Leone, T. J.

1992-01-01

What is the best way to represent the content of documents in an information retrieval system? This study compares the retrieval effectiveness of five different methods for automated (machine-assigned) indexing using three test collections. The consistently best methods are those that use indexing based on the words that occur in the available text of each document. Methods used to map text into concepts from a controlled vocabulary showed no advantage over the word-based methods. This study also looked at an approach to relevance feedback which showed benefit for both word-based and concept-based methods. PMID:1482951

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

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.

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.

Assessment of OMI Near-UV Aerosol Optical Depth over Land

NASA Technical Reports Server (NTRS)

Ahn, Changwoo; Torres, Omar; Jethva, Hiren

2014-01-01

This is the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the near-UV observations by the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. The OMI-retrieved AOD by the ultraviolet (UV) aerosol algorithm (OMAERUV version 1.4.2) was evaluated using collocated Aerosol Robotic Network (AERONET) level 2.0 direct Sun AOD measurements over 8 years (2005-2012). A time series analysis of collocated satellite and ground-based AOD observations over 8 years shows no discernible drift in OMI's calibration. A rigorous validation analysis over 4 years (2005-2008) was carried out at 44 globally distributed AERONET land sites. The chosen locations are representative of major aerosol types such as smoke from biomass burning or wildfires, desert mineral dust, and urban/industrial pollutants. Correlation coefficient (p) values of 0.75 or better were obtained at 50 percent of the sites with about 33 percent of the sites in the analysis reporting regression line slope values larger than 0.70 but always less than unity. The combined AERONET-OMAERUV analysis of the 44 sites yielded a p of 0.81, slope of 0.79, Y intercept of 0.10, and 65 percent OMAERUV AOD falling within the expected uncertainty range (largest of 30 percent or 0.1) at 440 nanometers. The most accurate OMAERUV retrievals are reported over northern Africa locations where the predominant aerosol type is desert dust and cloud presence is less frequent. Reliable retrievals were documented at many sites characterized by urban-type aerosols with low to moderate AOD values, concentrated in the boundary layer. These results confirm that the near-ultraviolet observations are sensitive to the entire aerosol column. A simultaneous comparison of OMAERUV, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue, and Multiangle Imaging Spectroradiometer (MISR) AOD retrievals to AERONET measurements was also carried out to evaluate the OMAERUV accuracy in relation to those of the standard aerosol satellite products. The outcome of the comparison indicates that OMAERUV, MODIS Deep Blue, and MISR retrieval accuracies in arid and semiarid environments are statistically comparable.

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;…

Learned Vector-Space Models for Document Retrieval.

ERIC Educational Resources Information Center

Caid, William R.; And Others

1995-01-01

The Latent Semantic Indexing and MatchPlus systems examine similar contexts in which words appear and create representational models that capture the similarity of meaning of terms and then use the representation for retrieval. Text Retrieval Conference experiments using these systems demonstrate the computational feasibility of using…

QCS : a system for querying, clustering, and summarizing documents.

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

Dunlavy, Daniel M.

2006-08-01

Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test setsmore » from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence ''trimming'', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.« less

QCS: a system for querying, clustering and summarizing documents.

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

Dunlavy, Daniel M.; Schlesinger, Judith D.; O'Leary, Dianne P.

2006-10-01

Information retrieval systems consist of many complicated components. Research and development of such systems is often hampered by the difficulty in evaluating how each particular component would behave across multiple systems. We present a novel hybrid information retrieval system--the Query, Cluster, Summarize (QCS) system--which is portable, modular, and permits experimentation with different instantiations of each of the constituent text analysis components. Most importantly, the combination of the three types of components in the QCS design improves retrievals by providing users more focused information organized by topic. We demonstrate the improved performance by a series of experiments using standard test setsmore » from the Document Understanding Conferences (DUC) along with the best known automatic metric for summarization system evaluation, ROUGE. Although the DUC data and evaluations were originally designed to test multidocument summarization, we developed a framework to extend it to the task of evaluation for each of the three components: query, clustering, and summarization. Under this framework, we then demonstrate that the QCS system (end-to-end) achieves performance as good as or better than the best summarization engines. Given a query, QCS retrieves relevant documents, separates the retrieved documents into topic clusters, and creates a single summary for each cluster. In the current implementation, Latent Semantic Indexing is used for retrieval, generalized spherical k-means is used for the document clustering, and a method coupling sentence 'trimming', and a hidden Markov model, followed by a pivoted QR decomposition, is used to create a single extract summary for each cluster. The user interface is designed to provide access to detailed information in a compact and useful format. Our system demonstrates the feasibility of assembling an effective IR system from existing software libraries, the usefulness of the modularity of the design, and the value of this particular combination of modules.« less

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

Seminal nanotechnology literature: a review.

PubMed

Kostoff, Ronald N; Koytcheff, Raymond G; Lau, Clifford G Y

2009-11-01

This paper uses complementary text mining techniques to identify and retrieve the high impact (seminal) nanotechnology literature over a span of time. Following a brief scientometric analysis of the seminal articles retrieved, these seminal articles are then used as a basis for a comprehensive literature survey of nanoscience and nanotechnology. The paper ends with a global analysis of the relation of seminal nanotechnology document production to total nanotechnology document production.

Automated storage and retrieval of data obtained in the Interkosmos project

NASA Technical Reports Server (NTRS)

Ziolkovski, K.; Pakholski, V.

1975-01-01

The formation of a data bank and information retrieval system for scientific data is described. The stored data can be digital or documentation data. Data classification methods are discussed along with definition and compilation of the dictionary utilized, definition of the indexing scheme, and definition of the principles used in constructing a file for documents, data blocks, and tapes. Operating principles are also presented.

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.

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.

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.

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-...

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.

Cross-language information retrieval using PARAFAC2.

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

Bader, Brett William; Chew, Peter; Abdelali, Ahmed

A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents 'language-independently', so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar tomore » other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the 'concepts' in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval.« less

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.

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

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.

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…

Experiments in Multi-Lingual Information Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard

A comparison was made of the performance in an automatic information retrieval environment of user queries and document abstracts available in natural language form in both English and French. The results obtained indicate that the automatic indexing and retrieval techniques actually used appear equally effective in handling the query and document…

40 CFR 792.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2012 CFR

2012-07-01

....190 Storage and retrieval of records and data. (a) All raw data, documentation, records, protocols... 40 Protection of Environment 33 2012-07-01 2012-07-01 false Storage and retrieval of records and data. 792.190 Section 792.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED...

40 CFR 792.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2011 CFR

2011-07-01

....190 Storage and retrieval of records and data. (a) All raw data, documentation, records, protocols... 40 Protection of Environment 32 2011-07-01 2011-07-01 false Storage and retrieval of records and data. 792.190 Section 792.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED...

40 CFR 792.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2013 CFR

2013-07-01

....190 Storage and retrieval of records and data. (a) All raw data, documentation, records, protocols... 40 Protection of Environment 33 2013-07-01 2013-07-01 false Storage and retrieval of records and data. 792.190 Section 792.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED...

40 CFR 792.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2014 CFR

2014-07-01

....190 Storage and retrieval of records and data. (a) All raw data, documentation, records, protocols... 40 Protection of Environment 32 2014-07-01 2014-07-01 false Storage and retrieval of records and data. 792.190 Section 792.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED...

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.

Combination of image descriptors for the exploration of cultural photographic collections

NASA Astrophysics Data System (ADS)

Bhowmik, Neelanjan; Gouet-Brunet, Valérie; Bloch, Gabriel; Besson, Sylvain

2017-01-01

The rapid growth of image digitization and collections in recent years makes it challenging and burdensome to organize, categorize, and retrieve similar images from voluminous collections. Content-based image retrieval (CBIR) is immensely convenient in this context. A considerable number of local feature detectors and descriptors are present in the literature of CBIR. We propose a model to anticipate the best feature combinations for image retrieval-related applications. Several spatial complementarity criteria of local feature detectors are analyzed and then engaged in a regression framework to find the optimal combination of detectors for a given dataset and are better adapted for each given image; the proposed model is also useful to optimally fix some other parameters, such as the k in k-nearest neighbor retrieval. Three public datasets of various contents and sizes are employed to evaluate the proposal, which is legitimized by improving the quality of retrieval notably facing classical approaches. Finally, the proposed image search engine is applied to the cultural photographic collections of a French museum, where it demonstrates its added value for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex situ.

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images

PubMed Central

Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro; Aoki, Hiroshi; Takeuchi, Ken; Suzuki, Yasuo

2017-01-01

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy. PMID:28255295

Image Retrieval Method for Multiscale Objects from Optical Colonoscopy Images.

PubMed

Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro; Aoki, Hiroshi; Takeuchi, Ken; Suzuki, Yasuo

2017-01-01

Optical colonoscopy is the most common approach to diagnosing bowel diseases through direct colon and rectum inspections. Periodic optical colonoscopy examinations are particularly important for detecting cancers at early stages while still treatable. However, diagnostic accuracy is highly dependent on both the experience and knowledge of the medical doctor. Moreover, it is extremely difficult, even for specialist doctors, to detect the early stages of cancer when obscured by inflammations of the colonic mucosa due to intractable inflammatory bowel diseases, such as ulcerative colitis. Thus, to assist the UC diagnosis, it is necessary to develop a new technology that can retrieve similar cases of diagnostic target image from cases in the past that stored the diagnosed images with various symptoms of colonic mucosa. In order to assist diagnoses with optical colonoscopy, this paper proposes a retrieval method for colonoscopy images that can cope with multiscale objects. The proposed method can retrieve similar colonoscopy images despite varying visible sizes of the target objects. Through three experiments conducted with real clinical colonoscopy images, we demonstrate that the method is able to retrieve objects of any visible size and any location at a high level of accuracy.

Phase retrieval by coherent modulation imaging

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

Zhang, Fucai; Chen, Bo; Morrison, Graeme R.

Phase retrieval is a long-standing problem in imaging when only the intensity of the wavefield can be recorded. Coherent diffraction imaging (CDI) 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 (CMI) method removes inherent ambiguities of CDI and uses a reliable, rapidly converging iterative algorithm involving three planes. It works formore » extended samples, does not require tight support for convergence, and relaxes dynamic range requirements on the detector. CMI 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 laser.« less

Phase retrieval by coherent modulation imaging

DOE PAGES

Zhang, Fucai; Chen, Bo; Morrison, Graeme R.; ...

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 (CDI) 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 (CMI) method removes inherent ambiguities of CDI and uses a reliable, rapidly converging iterative algorithm involving three planes. It works formore » extended samples, does not require tight support for convergence, and relaxes dynamic range requirements on the detector. CMI 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 laser.« less

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2000-12-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2001-01-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

A fuzzy measure approach to motion frame analysis for scene detection. M.S. Thesis - Houston Univ.

NASA Technical Reports Server (NTRS)

Leigh, Albert B.; Pal, Sankar K.

1992-01-01

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

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.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Grid Integrated Distributed PV (GridPV) Version 2.

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

Reno, Matthew J.; Coogan, Kyle

2014-12-01

This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functio ns are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in th e OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included tomore » show potential uses of the toolbox functions. Each function i n the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.« less

GUI-Based Document Access via SATCOMMS: Online Electronic Document Retrieval at the European Telecommunications Satellite Organization EUTELSAT.

ERIC Educational Resources Information Center

Burton, Adrian P.

1995-01-01

Discusses accessing online electronic documents at the European Telecommunications Satellite Organization (EUTELSAT). Highlights include off-site paper document storage, the document management system, benefits, the EUTELSAT Standard IBM Access software, implementation, the development process, and future enhancements. (AEF)

Document Ranking Based upon Markov Chains.

ERIC Educational Resources Information Center

Danilowicz, Czeslaw; Balinski, Jaroslaw

2001-01-01

Considers how the order of documents in information retrieval responses are determined and introduces a method that uses a probabilistic model of a document set where documents are regarded as states of a Markov chain and where transition probabilities are directly proportional to similarities between documents. (Author/LRW)

A model for enhancing Internet medical document retrieval with "medical core metadata".

PubMed

Malet, G; Munoz, F; Appleyard, R; Hersh, W

1999-01-01

Finding documents on the World Wide Web relevant to a specific medical information need can be difficult. The goal of this work is to define a set of document content description tags, or metadata encodings, that can be used to promote disciplined search access to Internet medical documents. The authors based their approach on a proposed metadata standard, the Dublin Core Metadata Element Set, which has recently been submitted to the Internet Engineering Task Force. Their model also incorporates the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary and MEDLINE-type content descriptions. The model defines a medical core metadata set that can be used to describe the metadata for a wide variety of Internet documents. The authors propose that their medical core metadata set be used to assign metadata to medical documents to facilitate document retrieval by Internet search engines.

A Model for Enhancing Internet Medical Document Retrieval with “Medical Core Metadata”

PubMed Central

Malet, Gary; Munoz, Felix; Appleyard, Richard; Hersh, William

1999-01-01

Objective: Finding documents on the World Wide Web relevant to a specific medical information need can be difficult. The goal of this work is to define a set of document content description tags, or metadata encodings, that can be used to promote disciplined search access to Internet medical documents. Design: The authors based their approach on a proposed metadata standard, the Dublin Core Metadata Element Set, which has recently been submitted to the Internet Engineering Task Force. Their model also incorporates the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary and Medline-type content descriptions. Results: The model defines a medical core metadata set that can be used to describe the metadata for a wide variety of Internet documents. Conclusions: The authors propose that their medical core metadata set be used to assign metadata to medical documents to facilitate document retrieval by Internet search engines. PMID:10094069

Effects of internal and external vividness on hippocampal connectivity during memory retrieval.

PubMed

Ford, Jaclyn H; Kensinger, Elizabeth A

2016-10-01

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. Copyright © 2016 Elsevier Inc. All rights reserved.

Image Location Estimation by Salient Region Matching.

PubMed

Qian, Xueming; Zhao, Yisi; Han, Junwei

2015-11-01

Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

Visual content highlighting via automatic extraction of embedded captions on MPEG compressed video

NASA Astrophysics Data System (ADS)

Yeo, Boon-Lock; Liu, Bede

1996-03-01

Embedded captions in TV programs such as news broadcasts, documentaries and coverage of sports events provide important information on the underlying events. In digital video libraries, such captions represent a highly condensed form of key information on the contents of the video. In this paper we propose a scheme to automatically detect the presence of captions embedded in video frames. The proposed method operates on reduced image sequences which are efficiently reconstructed from compressed MPEG video and thus does not require full frame decompression. The detection, extraction and analysis of embedded captions help to capture the highlights of visual contents in video documents for better organization of video, to present succinctly the important messages embedded in the images, and to facilitate browsing, searching and retrieval of relevant clips.

A Framework for Integration of Heterogeneous Medical Imaging Networks

PubMed Central

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS. PMID:25279021

A framework for integration of heterogeneous medical imaging networks.

PubMed

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

Using Crowdsourced Geospatial Data to Aid in Nuclear Proliferation Monitoring

DTIC Science & Technology

2016-12-01

M. Stephens, and Ronald D. Bonnell, “DAI for Document Retrieval: The MINDS Project,” in Distributed Artificial Intelligence , ed. Michael N. Huhns...Ronald D. Bonnell. “DAI for Document Retrieval: The MINDS Project,” In Distributed Artificial Intelligence , edited by Michael N. Huhns, 249–283...was for the director of National Intelligence to explore ways that crowdsourced geospatial imagery technologies could aid existing governmental

Intelligent search and retrieval of a large multimedia knowledgebase for the Hubble Space Telescope

NASA Technical Reports Server (NTRS)

Clapis, Paul J.; Byers, William S.

1990-01-01

A document-retrieval assistant (DRA) in a microcomputer format is described which incorporates hypertext and natural language capabilities. Hypertext is used to introduce an intelligent search capability, and the natural-language interface permits access to specific data without the use of keywords. The DRA can be used to access and 'browse' the large multimedia database that is composed of project documentation from the HST.

The Electronic Documentation Project in the NASA mission control center environment

NASA Technical Reports Server (NTRS)

Wang, Lui; Leigh, Albert

1994-01-01

NASA's space programs like many other technical programs of its magnitude is supported by a large volume of technical documents. These documents are not only diverse but also abundant. Management, maintenance, and retrieval of these documents is a challenging problem by itself; but, relating and cross-referencing this wealth of information when it is all on a medium of paper is an even greater challenge. The Electronic Documentation Project (EDP) is to provide an electronic system capable of developing, distributing and controlling changes for crew/ground controller procedures and related documents. There are two primary motives for the solution. The first motive is to reduce the cost of maintaining the current paper based method of operations by replacing paper documents with electronic information storage and retrieval. And, the other is to improve the efficiency and provide enhanced flexibility in document usage. Initially, the current paper based system will be faithfully reproduced in an electronic format to be used in the document viewing system. In addition, this metaphor will have hypertext extensions. Hypertext features support basic functions such as full text searches, key word searches, data retrieval, and traversal between nodes of information as well as speeding up the data access rate. They enable related but separate documents to have relationships, and allow the user to explore information naturally through non-linear link traversals. The basic operational requirements of the document viewing system are to: provide an electronic corollary to the current method of paper based document usage; supplement and ultimately replace paper-based documents; maintain focused toward control center operations such as Flight Data File, Flight Rules and Console Handbook viewing; and be available NASA wide.

Facilitating access to information in large documents with an intelligent hypertext system

NASA Technical Reports Server (NTRS)

Mathe, Nathalie

1993-01-01

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation) and tested it on the Space Station Freedom requirement documents. The CID system enables integration of various technical documents in a hypertext framework and includes an intelligent context-sensitive indexing and retrieval mechanism. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time.

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

Structure function monitor

DOEpatents

McGraw, John T [Placitas, NM; Zimmer, Peter C [Albuquerque, NM; Ackermann, Mark R [Albuquerque, NM

2012-01-24

Methods and apparatus for a structure function monitor provide for generation of parameters characterizing a refractive medium. In an embodiment, a structure function monitor acquires images of a pupil plane and an image plane and, from these images, retrieves the phase over an aperture, unwraps the retrieved phase, and analyzes the unwrapped retrieved phase. In an embodiment, analysis yields atmospheric parameters measured at spatial scales from zero to the diameter of a telescope used to collect light from a source.

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Experimental Studies on a Compact Storage Scheme for Wavelet-based Multiresolution Subregion Retrieval

NASA Technical Reports Server (NTRS)

Poulakidas, A.; Srinivasan, A.; Egecioglu, O.; Ibarra, O.; Yang, T.

1996-01-01

Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images effectively. In order to use them for image library systems, a compact storage scheme for quantized coefficient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

PubMed Central

Das, Ravi K.; Gale, Grace; Hennessy, Vanessa; Kamboj, Sunjeev K.

2018-01-01

Maladaptive reward memories (MRMs) can become unstable following retrieval under certain conditions, allowing their modification by subsequent new learning. However, robust (well-rehearsed) and chronologically old MRMs, such as those underlying substance use disorders, do not destabilize easily when retrieved. A key determinate of memory destabilization during retrieval is prediction error (PE). We describe a retrieval procedure for alcohol MRMs in hazardous drinkers that specifically aims to maximize the generation of PE and therefore the likelihood of MRM destabilization. The procedure requires explicitly generating the expectancy of alcohol consumption and then violating this expectancy (withholding alcohol) following the presentation of a brief set of prototypical alcohol cue images (retrieval + PE). Control procedures involve presenting the same cue images, but allow alcohol to be consumed, generating minimal PE (retrieval-no PE) or generate PE without retrieval of alcohol MRMs, by presenting orange juice cues (no retrieval + PE). Subsequently, we describe a multisensory disgust-based counterconditioning procedure to probe MRM destabilization by re-writing alcohol cue-reward associations prior to reconsolidation. This procedure pairs alcohol cues with images invoking pathogen disgust and an extremely bitter-tasting solution (denatonium benzoate), generating gustatory disgust. Following retrieval + PE, but not no retrieval + PE or retrieval-no PE, counterconditioning produces evidence of MRM rewriting as indexed by lasting reductions in alcohol cue valuation, attentional capture, and alcohol craving. PMID:29364255

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

NASA Astrophysics Data System (ADS)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

Inter-Comparison of GOES-8 Imager and Sounder Skin Temperature Retrievals

NASA Technical Reports Server (NTRS)

Haines, Stephanie L.; Suggs, Ronnie J.; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2001-01-01

Skin temperature (ST) retrievals derived from geostationary satellite observations have both high temporal and spatial resolutions and are therefore useful for applications such as assimilation into mesoscale forecast models, nowcasting, and diagnostic studies. Our retrieval method uses a Physical Split Window technique requiring at least two channels within the longwave infrared window. On current GOES satellites, including GOES-11, there are two Imager channels within the required spectral interval. However, beginning with the GOES-M satellite the 12-um channel will be removed, leaving only one longwave channel. The Sounder instrument will continue to have three channels within the longwave window, and therefore ST retrievals will be derived from Sounder measurements. This research compares retrievals from the two instruments and evaluates the effects of the spatial resolution and sensor calibration differences on the retrievals. Both Imager and Sounder retrievals are compared to ground-truth data to evaluate the overall accuracy of the technique. An analysis of GOES-8 and GOES-11 intercomparisons is also presented.

Registering parameters and granules of wave observations: IMAGE RPI success story

NASA Astrophysics Data System (ADS)

Galkin, I. A.; Charisi, A.; Fung, S. F.; Benson, R. F.; Reinisch, B. W.

2015-12-01

Modern metadata systems strive to help scientists locate data relevant to their research and then retrieve them quickly. Success of this mission depends on the organization and completeness of metadata. Each relevant data resource has to be registered; each content has to be described; each data file has to be accessible. Ultimately, data discoverability is about the practical ability to describe data content and location. Correspondingly, data registration has a "Parameter" level, at which content is specified by listing available observed properties (parameters), and a "Granule" level, at which download links are given to data records (granules). Until recently, both parameter- and granule-level data registrations were accomplished at NASA Virtual System Observatory easily by listing provided parameters and building Granule documents with URLs to the datafile locations, usually those at NASA CDAWeb data warehouse. With the introduction of the Virtual Wave Observatory (VWO), however, the parameter/granule concept faced a scalability challenge. The wave phenomenon content is rich with descriptors of the wave generation, propagation, interaction with propagation media, and observation processes. Additionally, the wave phenomenon content varies from record to record, reflecting changes in the constituent processes, making it necessary to generate granule documents at sub-minute resolution. We will present the first success story of registering 234,178 records of IMAGE Radio Plasma Imager (RPI) plasmagram data and Level 2 derived data products in ESPAS (near-Earth Space Data Infrastructure for e-Science), using the VWO-inspired wave ontology. The granules are arranged in overlapping display and numerical data collections. Display data include (a) auto-prospected plasmagrams of potential interest, (b) interesting plasmagrams annotated by human analysts or software, and (c) spectacular plasmagrams annotated by analysts as publication-quality examples of the RPI science. Numerical data products include plasmagram-derived records containing signatures of local and remote signal propagation, as well as field-aligned profiles of electron density in the plasmasphere. Registered granules of RPI observations are available in ESPAS for their content-targeted search and retrieval.

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.

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

The Document Management Alliance.

ERIC Educational Resources Information Center

Fay, Chuck

1998-01-01

Describes the Document Management Alliance, a standards effort for document management systems that manages and tracks changes to electronic documents created and used by collaborative teams, provides secure access, and facilitates online information retrieval via the Internet and World Wide Web. Future directions are also discussed. (LRW)

Page layout analysis and classification for complex scanned documents

NASA Astrophysics Data System (ADS)

Erkilinc, M. Sezer; Jaber, Mustafa; Saber, Eli; Bauer, Peter; Depalov, Dejan

2011-09-01

A framework for region/zone classification in color and gray-scale scanned documents is proposed in this paper. The algorithm includes modules for extracting text, photo, and strong edge/line regions. Firstly, a text detection module which is based on wavelet analysis and Run Length Encoding (RLE) technique is employed. Local and global energy maps in high frequency bands of the wavelet domain are generated and used as initial text maps. Further analysis using RLE yields a final text map. The second module is developed to detect image/photo and pictorial regions in the input document. A block-based classifier using basis vector projections is employed to identify photo candidate regions. Then, a final photo map is obtained by applying probabilistic model based on Markov random field (MRF) based maximum a posteriori (MAP) optimization with iterated conditional mode (ICM). The final module detects lines and strong edges using Hough transform and edge-linkages analysis, respectively. The text, photo, and strong edge/line maps are combined to generate a page layout classification of the scanned target document. Experimental results and objective evaluation show that the proposed technique has a very effective performance on variety of simple and complex scanned document types obtained from MediaTeam Oulu document database. The proposed page layout classifier can be used in systems for efficient document storage, content based document retrieval, optical character recognition, mobile phone imagery, and augmented reality.

Image Retrieval by Color Semantics with Incomplete Knowledge.

ERIC Educational Resources Information Center

Corridoni, Jacopo M.; Del Bimbo, Alberto; Vicario, Enrico

1998-01-01

Presents a system which supports image retrieval by high-level chromatic contents, the sensations that color accordances generate on the observer. Surveys Itten's theory of color semantics and discusses image description and query specification. Presents examples of visual querying. (AEF)

Automatic visibility retrieval from thermal camera images

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Ott, Beat; Wellig, Peter; Wunderle, Stefan

2017-10-01

This study presents an automatic visibility retrieval of a FLIR A320 Stationary Thermal Imager installed on a measurement tower on the mountain Lagern located in the Swiss Jura Mountains. Our visibility retrieval makes use of edges that are automatically detected from thermal camera images. Predefined target regions, such as mountain silhouettes or buildings with high thermal differences to the surroundings, are used to derive the maximum visibility distance that is detectable in the image. To allow a stable, automatic processing, our procedure additionally removes noise in the image and includes automatic image alignment to correct small shifts of the camera. We present a detailed analysis of visibility derived from more than 24000 thermal images of the years 2015 and 2016 by comparing them to (1) visibility derived from a panoramic camera image (VISrange), (2) measurements of a forward-scatter visibility meter (Vaisala FD12 working in the NIR spectra), and (3) modeled visibility values using the Thermal Range Model TRM4. Atmospheric conditions, mainly water vapor from European Center for Medium Weather Forecast (ECMWF), were considered to calculate the extinction coefficients using MODTRAN. The automatic visibility retrieval based on FLIR A320 images is often in good agreement with the retrieval from the systems working in different spectral ranges. However, some significant differences were detected as well, depending on weather conditions, thermal differences of the monitored landscape, and defined target size.

Jaccard Similarity Leads to the Marczewski-Steinhaus Topology for Information Retrieval.

ERIC Educational Resources Information Center

Rousseau, Ronald

1998-01-01

Demonstrates that if the similarity function of a retrieval system leads to a (pseudo-) metric, the retrieval, similarity and Everett-Cater metric topology coincide and are different from the discrete topology; this is the case if documents are represented by lists, using the Jaccard similarity measure. The corresponding metric is the…

Historical Note: The Past Thirty Years in Information Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard

1987-01-01

Briefly reviews early work in documentation and text processing, and predictions that were made about the creative role of computers in information retrieval. An attempt is made to explain why these predictions were not fulfilled and conclusions are drawn regarding the limits of computer power in text retrieval applications. (Author/CLB)

Engineering a Multi-Purpose Test Collection for Web Retrieval Experiments.

ERIC Educational Resources Information Center

Bailey, Peter; Craswell, Nick; Hawking, David

2003-01-01

Describes a test collection that was developed as a multi-purpose testbed for experiments on the Web in distributed information retrieval, hyperlink algorithms, and conventional ad hoc retrieval. Discusses inter-server connectivity, integrity of server holdings, inclusion of documents related to a wide spread of likely queries, and distribution of…

21 CFR 58.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 1 2013-04-01 2013-04-01 false Storage and retrieval of records and data. 58.190...) There shall be archives for orderly storage and expedient retrieval of all raw data, documentation... GENERAL GOOD LABORATORY PRACTICE FOR NONCLINICAL LABORATORY STUDIES Records and Reports § 58.190 Storage...

21 CFR 58.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 1 2014-04-01 2014-04-01 false Storage and retrieval of records and data. 58.190...) There shall be archives for orderly storage and expedient retrieval of all raw data, documentation... GENERAL GOOD LABORATORY PRACTICE FOR NONCLINICAL LABORATORY STUDIES Records and Reports § 58.190 Storage...

21 CFR 58.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 1 2012-04-01 2012-04-01 false Storage and retrieval of records and data. 58.190...) There shall be archives for orderly storage and expedient retrieval of all raw data, documentation... GENERAL GOOD LABORATORY PRACTICE FOR NONCLINICAL LABORATORY STUDIES Records and Reports § 58.190 Storage...

21 CFR 58.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Storage and retrieval of records and data. 58.190...) There shall be archives for orderly storage and expedient retrieval of all raw data, documentation... GENERAL GOOD LABORATORY PRACTICE FOR NONCLINICAL LABORATORY STUDIES Records and Reports § 58.190 Storage...

21 CFR 58.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 1 2011-04-01 2011-04-01 false Storage and retrieval of records and data. 58.190...) There shall be archives for orderly storage and expedient retrieval of all raw data, documentation... GENERAL GOOD LABORATORY PRACTICE FOR NONCLINICAL LABORATORY STUDIES Records and Reports § 58.190 Storage...

Logic-Based Retrieval: Technology for Content-Oriented and Analytical Querying of Patent Data

NASA Astrophysics Data System (ADS)

Klampanos, Iraklis Angelos; Wu, Hengzhi; Roelleke, Thomas; Azzam, Hany

Patent searching is a complex retrieval task. An initial document search is only the starting point of a chain of searches and decisions that need to be made by patent searchers. Keyword-based retrieval is adequate for document searching, but it is not suitable for modelling comprehensive retrieval strategies. DB-like and logical approaches are the state-of-the-art techniques to model strategies, reasoning and decision making. In this paper we present the application of logical retrieval to patent searching. The two grand challenges are expressiveness and scalability, where high degree of expressiveness usually means a loss in scalability. In this paper we report how to maintain scalability while offering the expressiveness of logical retrieval required for solving patent search tasks. We present logical retrieval background, and how to model data-source selection and results' fusion. Moreover, we demonstrate the modelling of a retrieval strategy, a technique by which patent professionals are able to express, store and exchange their strategies and rationales when searching patents or when making decisions. An overview of the architecture and technical details complement the paper, while the evaluation reports preliminary results on how query processing times can be guaranteed, and how quality is affected by trading off responsiveness.

Semantics of User Interface for Image Retrieval: Possibility Theory and Learning Techniques.

ERIC Educational Resources Information Center

Crehange, M.; And Others

1989-01-01

Discusses the need for a rich semantics for the user interface in interactive image retrieval and presents two methods for building such interfaces: possibility theory applied to fuzzy data retrieval, and a machine learning technique applied to learning the user's deep need. Prototypes developed using videodisks and knowledge-based software are…

Collocated Dataglyphs for large-message storage and retrieval

NASA Astrophysics Data System (ADS)

Motwani, Rakhi C.; Breidenbach, Jeff A.; Black, John R.

2004-06-01

In contrast to the security and integrity of electronic files, printed documents are vulnerable to damage and forgery due to their physical nature. Researchers at Palo Alto Research Center utilize DataGlyph technology to render digital characteristics to printed documents, which provides them with the facility of tamper-proof authentication and damage resistance. This DataGlyph document is known as GlyphSeal. Limited DataGlyph carrying capacity per printed page restricted the application of this technology to a domain of graphically simple and small-sized single-paged documents. In this paper the authors design a protocol motivated by techniques from the networking domain and back-up strategies, which extends the GlyphSeal technology to larger-sized, graphically complex, multi-page documents. This protocol provides fragmentation, sequencing and data loss recovery. The Collocated DataGlyph Protocol renders large glyph messages onto multiple printed pages and recovers the glyph data from rescanned versions of the multi-page documents, even when pages are missing, reordered or damaged. The novelty of this protocol is the application of ideas from RAID to the domain of DataGlyphs. The current revision of this protocol is capable of generating at most 255 pages, if page recovery is desired and does not provide enough data density to store highly detailed images in a reasonable amount of page space.

The Digital Fish Library: Using MRI to Digitize, Database, and Document the Morphological Diversity of Fish

PubMed Central

Berquist, Rachel M.; Gledhill, Kristen M.; Peterson, Matthew W.; Doan, Allyson H.; Baxter, Gregory T.; Yopak, Kara E.; Kang, Ning; Walker, H. J.; Hastings, Philip A.; Frank, Lawrence R.

2012-01-01

Museum fish collections possess a wealth of anatomical and morphological data that are essential for documenting and understanding biodiversity. Obtaining access to specimens for research, however, is not always practical and frequently conflicts with the need to maintain the physical integrity of specimens and the collection as a whole. Non-invasive three-dimensional (3D) digital imaging therefore serves a critical role in facilitating the digitization of these specimens for anatomical and morphological analysis as well as facilitating an efficient method for online storage and sharing of this imaging data. Here we describe the development of the Digital Fish Library (DFL, http://www.digitalfishlibrary.org), an online digital archive of high-resolution, high-contrast, magnetic resonance imaging (MRI) scans of the soft tissue anatomy of an array of fishes preserved in the Marine Vertebrate Collection of Scripps Institution of Oceanography. We have imaged and uploaded MRI data for over 300 marine and freshwater species, developed a data archival and retrieval system with a web-based image analysis and visualization tool, and integrated these into the public DFL website to disseminate data and associated metadata freely over the web. We show that MRI is a rapid and powerful method for accurately depicting the in-situ soft-tissue anatomy of preserved fishes in sufficient detail for large-scale comparative digital morphology. However these 3D volumetric data require a sophisticated computational and archival infrastructure in order to be broadly accessible to researchers and educators. PMID:22493695

The Digital Fish Library: using MRI to digitize, database, and document the morphological diversity of fish.

PubMed

Berquist, Rachel M; Gledhill, Kristen M; Peterson, Matthew W; Doan, Allyson H; Baxter, Gregory T; Yopak, Kara E; Kang, Ning; Walker, H J; Hastings, Philip A; Frank, Lawrence R

2012-01-01

Museum fish collections possess a wealth of anatomical and morphological data that are essential for documenting and understanding biodiversity. Obtaining access to specimens for research, however, is not always practical and frequently conflicts with the need to maintain the physical integrity of specimens and the collection as a whole. Non-invasive three-dimensional (3D) digital imaging therefore serves a critical role in facilitating the digitization of these specimens for anatomical and morphological analysis as well as facilitating an efficient method for online storage and sharing of this imaging data. Here we describe the development of the Digital Fish Library (DFL, http://www.digitalfishlibrary.org), an online digital archive of high-resolution, high-contrast, magnetic resonance imaging (MRI) scans of the soft tissue anatomy of an array of fishes preserved in the Marine Vertebrate Collection of Scripps Institution of Oceanography. We have imaged and uploaded MRI data for over 300 marine and freshwater species, developed a data archival and retrieval system with a web-based image analysis and visualization tool, and integrated these into the public DFL website to disseminate data and associated metadata freely over the web. We show that MRI is a rapid and powerful method for accurately depicting the in-situ soft-tissue anatomy of preserved fishes in sufficient detail for large-scale comparative digital morphology. However these 3D volumetric data require a sophisticated computational and archival infrastructure in order to be broadly accessible to researchers and educators.

Retrieve polarization aberration from image degradation: a new measurement method in DUV lithography

NASA Astrophysics Data System (ADS)

Xiang, Zhongbo; Li, Yanqiu

2017-10-01

Detailed knowledge of polarization aberration (PA) of projection lens in higher-NA DUV lithographic imaging is necessary due to its impact to imaging degradations, and precise measurement of PA is conductive to computational lithography techniques such as RET and OPC. Current in situ measurement method of PA thorough the detection of degradations of aerial images need to do linear approximation and apply the assumption of 3-beam/2-beam interference condition. The former approximation neglects the coupling effect of the PA coefficients, which would significantly influence the accuracy of PA retrieving. The latter assumption restricts the feasible pitch of test masks in higher-NA system, conflicts with the Kirhhoff diffraction model of test mask used in retrieving model, and introduces 3D mask effect as a source of retrieving error. In this paper, a new in situ measurement method of PA is proposed. It establishes the analytical quadratic relation between the PA coefficients and the degradations of aerial images of one-dimensional dense lines in coherent illumination through vector aerial imaging, which does not rely on the assumption of 3-beam/2- beam interference and linear approximation. In this case, the retrieval of PA from image degradation can be convert from the nonlinear system of m-quadratic equations to a multi-objective quadratic optimization problem, and finally be solved by nonlinear least square method. Some preliminary simulation results are given to demonstrate the correctness and accuracy of the new PA retrieving model.

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

PubMed

Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo

2016-06-01

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

Rain Rate and DSD Retrievals at Kwajalein Atoll

NASA Astrophysics Data System (ADS)

Wolff, David; Marks, David; Tokay, Ali

2010-05-01

The dual-polarization weather radar on Kwajalein Atoll in the Republic of the Marshall Islands (KPOL) is one of the only full-time (24/7) operational S-band dual-polarimetric (DP) radars in the tropics. Using the DP data from KPOL, as well as data from a Joss-Waldvogel disdrometer on Kwajalein Island, algorithms for quality control, as well as calibration of reflectivity and differential reflectivity have been developed and adapted for application in a near real-time operational environment. Observations during light rain and drizzle show that KPOL measurements (since 2006) meet or exceed quality thresholds for these applications (as determined by consensus of the radar community). While the methodology for development of such applications is well documented, tuning of specific algorithms to a particular regime and observed raindrop size distributions requires a comprehensive testing and adjustment period to ensure high quality products. Upon application of these data quality techniques to the KPOL data, we have tested and compared several different rain retrieval algorithms. These include conventional Z-R, DP hybrid techniques, as well as polarimetrically-tuned Z-R described by Bringi et al. 2004. One of the major benefits of the polarimetrically tuned Z-R technique, is its ability to use the DP observations to retrieve key parameters of the drop size distribution (DSD), such as the median drop diameter, and the intercept and shape parameter of the assumed gammaDSD. We will show several such retrievals for different rain systems, as well as their distribution with height below the melting layer. From a physical validation perspective, such DSD parameter retrievals provide an important means to cross-validate microphysical parameterizations in GPM Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) retrieval algorithms.

Precise and Efficient Retrieval of Captioned Images: The MARIE Project.

ERIC Educational Resources Information Center

Rowe, Neil C.

1999-01-01

The MARIE project explores knowledge-based information retrieval of captioned images of the kind found in picture libraries and on the Internet. MARIE's five-part approach exploits the idea that images are easier to understand with context, especially descriptive text near them, but it also does image analysis. Experiments show MARIE prototypes…

KISTI at TREC 2014 Clinical Decision Support Track: Concept-based Document Re-ranking to Biomedical Information Retrieval

DTIC Science & Technology

2014-11-01

sematic type. Injury or Poisoning inpo T037 Anatomical Abnormality anab T190 Given a document D, a concept vector = {1, 2, … , ...integrating biomedical terminology . Nucleic acids research 32, Database issue (2004), 267–270. 5. Chapman, W.W., Hillert, D., Velupillai, S., et...Conference (TREC), (2011). 9. Koopman, B. and Zuccon, G. Understanding negation and family history to improve clinical information retrieval. Proceedings

An integrated information retrieval and document management system

NASA Technical Reports Server (NTRS)

Coles, L. Stephen; Alvarez, J. Fernando; Chen, James; Chen, William; Cheung, Lai-Mei; Clancy, Susan; Wong, Alexis

1993-01-01

This paper describes the requirements and prototype development for an intelligent document management and information retrieval system that will be capable of handling millions of pages of text or other data. Technologies for scanning, Optical Character Recognition (OCR), magneto-optical storage, and multiplatform retrieval using a Standard Query Language (SQL) will be discussed. The semantic ambiguity inherent in the English language is somewhat compensated-for through the use of coefficients or weighting factors for partial synonyms. Such coefficients are used both for defining structured query trees for routine queries and for establishing long-term interest profiles that can be used on a regular basis to alert individual users to the presence of relevant documents that may have just arrived from an external source, such as a news wire service. Although this attempt at evidential reasoning is limited in comparison with the latest developments in AI Expert Systems technology, it has the advantage of being commercially available.

High-speed data search

NASA Technical Reports Server (NTRS)

Driscoll, James N.

1994-01-01

The high-speed data search system developed for KSC incorporates existing and emerging information retrieval technology to help a user intelligently and rapidly locate information found in large textual databases. This technology includes: natural language input; statistical ranking of retrieved information; an artificial intelligence concept called semantics, where 'surface level' knowledge found in text is used to improve the ranking of retrieved information; and relevance feedback, where user judgements about viewed information are used to automatically modify the search for further information. Semantics and relevance feedback are features of the system which are not available commercially. The system further demonstrates focus on paragraphs of information to decide relevance; and it can be used (without modification) to intelligently search all kinds of document collections, such as collections of legal documents medical documents, news stories, patents, and so forth. The purpose of this paper is to demonstrate the usefulness of statistical ranking, our semantic improvement, and relevance feedback.

Effects of Information Access Cost and Accountability on Medical Residents' Information Retrieval Strategy and Performance During Prehandover Preparation: Evidence From Interview and Simulation Study.

PubMed

Yang, X Jessie; Wickens, Christopher D; Park, Taezoon; Fong, Liesel; Siah, Kewin T H

2015-12-01

We aimed to examine the effects of information access cost and accountability on medical residents' information retrieval strategy and performance during prehandover preparation. Prior studies observing doctors' prehandover practices witnessed the use of memory-intensive strategies when retrieving patient information. These strategies impose potential threats to patient safety as human memory is prone to errors. Of interest in this work are the underlying determinants of information retrieval strategy and the potential impacts on medical residents' information preparation performance. A two-step research approach was adopted, consisting of semistructured interviews with 21 medical residents and a simulation-based experiment with 32 medical residents. The semistructured interviews revealed that a substantial portion of medical residents (38%) relied largely on memory for preparing handover information. The simulation-based experiment showed that higher information access cost reduced information access attempts and access duration on patient documents and harmed information preparation performance. Higher accountability led to marginally longer access to patient documents. It is important to understand the underlying determinants of medical residents' information retrieval strategy and performance during prehandover preparation. We noted the criticality of easy access to patient documents in prehandover preparation. In addition, accountability marginally influenced medical residents' information retrieval strategy. Findings from this research suggested that the cost of accessing information sources should be minimized in developing handover preparation tools. © 2015, Human Factors and Ergonomics Society.

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.

Data collection and preparation of authoritative reviews on space food and nutrition research

NASA Technical Reports Server (NTRS)

1972-01-01

The collection and classification of information for a manually operated information retrieval system on the subject of space food and nutrition research are described. The system as it currently exists is designed for retrieval of documents, either in hard copy or on microfiche, from the technical files of the MSC Food and Nutrition Section by accession number, author, and/or subject. The system could readily be extended to include retrieval by affiliation, report and contract number, and sponsoring agency should the need arise. It can also be easily converted to computerized retrieval. At present the information retrieval system contains nearly 3000 documents which consist of technical papers, contractors' reports, and reprints obtained from the food and nutrition files at MSC, Technical Library, the library at the Texas Medical Center in Houston, the BMI Technical Libraries, Dr. E. B. Truitt at MBI, and the OSU Medical Libraries. Additional work was done to compile 18 selected bibliographies on subjects of immediate interest on the MSC Food and Nutrition Section.

Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning.

PubMed

Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun; Ma, Shuai; Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma; Ma, Shuai; Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun

2018-06-01

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

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.

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.

Radiology-led Follow-up System for IVC Filters: Effects on Retrieval Rates and Times

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

Lee, L.; Taylor, J.; Munneke, G.

Purpose: Successful IVC filter retrieval rates fall with time. Serious complications have been reported following attempts to remove filters after 3-18 months. Failed retrieval may be associated with adverse clinical sequelae. This study explored whether retrieval rates are improved if interventional radiologists organize patient follow-up, rather than relying on the referring clinicians. Methods: Proactive follow-up of patients who undergo filter placement was implemented in May 2008. At the time of filter placement, a report was issued to the referring consultant notifying them of the advised timeframe for filter retrieval. Clinicians were contacted to arrange retrieval within 30 days. We comparedmore » this with our practice for the preceding year. Results: The numbers of filters inserted during the two time periods was similar, as were the numbers of retrieval attempts and the time scale at which they occurred. The rate of successful retrievals increased but not significantly. The major changes were better documentation of filter types and better clinical follow-up. After the change in practice, only one patient was lost to follow-up compared with six the preceding year. Conclusions: Although there was no significant improvement in retrieval rates, the proactive, radiology-led approach improved follow-up and documentation, ensuring that a clinical decision was made about how long the filter was required and whether retrieval should be attempted and ensuring that patients were not lost to follow-up.« less

Word Spotting for Indic Documents to Facilitate Retrieval

NASA Astrophysics Data System (ADS)

Bhardwaj, Anurag; Setlur, Srirangaraj; Govindaraju, Venu

With advances in the field of digitization of printed documents and several mass digitization projects underway, information retrieval and document search have emerged as key research areas. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts has hampered information extraction from a large body of documents of cultural and historical importance. This chapter presents two relevant topics in this area. First, we describe the use of a script-specific keyword spotting for Devanagari documents that makes use of domain knowledge of the script. Second, we address the needs of a digital library to provide access to a collection of documents from multiple scripts. This requires intelligent solutions which scale across different scripts. We present a script-independent keyword spotting approach for this purpose. Experimental results illustrate the efficacy of our methods.

Multi-provider architecture for cloud outsourcing of medical imaging repositories.

PubMed

Godinho, Tiago Marques; Bastião Silva, Luís A; Costa, Carlos; Oliveira, José Luís

2014-01-01

Over the last few years, the extended usage of medical imaging procedures has raised the medical community attention towards the optimization of their workflows. More recently, the federation of multiple institutions into a seamless distribution network has brought hope of increased quality healthcare services along with more efficient resource management. As a result, medical institutions are constantly looking for the best infrastructure to deploy their imaging archives. In this scenario, public cloud infrastructures arise as major candidates, as they offer elastic storage space, optimal data availability without great requirements of maintenance costs or IT personnel, in a pay-as-you-go model. However, standard methodologies still do not take full advantage of outsourced archives, namely because their integration with other in-house solutions is troublesome. This document proposes a multi-provider architecture for integration of outsourced archives with in-house PACS resources, taking advantage of foreign providers to store medical imaging studies, without disregarding security. It enables the retrieval of images from multiple archives simultaneously, improving performance, data availability and avoiding the vendor-locking problem. Moreover it enables load balancing and cache techniques.

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.

Retrieving high-resolution images over the Internet from an anatomical image database

NASA Astrophysics Data System (ADS)

Strupp-Adams, Annette; Henderson, Earl

1999-12-01

The Visible Human Data set is an important contribution to the national collection of anatomical images. To enhance the availability of these images, the National Library of Medicine has supported the design and development of a prototype object-oriented image database which imports, stores, and distributes high resolution anatomical images in both pixel and voxel formats. One of the key database modules is its client-server Internet interface. This Web interface provides a query engine with retrieval access to high-resolution anatomical images that range in size from 100KB for browser viewable rendered images, to 1GB for anatomical structures in voxel file formats. The Web query and retrieval client-server system is composed of applet GUIs, servlets, and RMI application modules which communicate with each other to allow users to query for specific anatomical structures, and retrieve image data as well as associated anatomical images from the database. Selected images can be downloaded individually as single files via HTTP or downloaded in batch-mode over the Internet to the user's machine through an applet that uses Netscape's Object Signing mechanism. The image database uses ObjectDesign's object-oriented DBMS, ObjectStore that has a Java interface. The query and retrieval systems has been tested with a Java-CDE window system, and on the x86 architecture using Windows NT 4.0. This paper describes the Java applet client search engine that queries the database; the Java client module that enables users to view anatomical images online; the Java application server interface to the database which organizes data returned to the user, and its distribution engine that allow users to download image files individually and/or in batch-mode.

Extended Subject Access to Hypertext Online Documentation. Part III: The Document-Boundaries Problem.

ERIC Educational Resources Information Center

Girill, T. R.

1991-01-01

This article continues the description of DFT (Document, Find, Theseus), an online documentation system that provides computer-managed on-demand printing of software manuals as well as the interactive retrieval of reference passages. Document boundaries in the hypertext database are discussed, search vocabulary complexities are described, and text…

Modelling Subjectivity in Visual Perception of Orientation for Image Retrieval.

ERIC Educational Resources Information Center

Sanchez, D.; Chamorro-Martinez, J.; Vila, M. A.

2003-01-01

Discussion of multimedia libraries and the need for storage, indexing, and retrieval techniques focuses on the combination of computer vision and data mining techniques to model high-level concepts for image retrieval based on perceptual features of the human visual system. Uses fuzzy set theory to measure users' assessments and to capture users'…

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

A graph-based approach for the retrieval of multi-modality medical images.

PubMed

Kumar, Ashnil; Kim, Jinman; Wen, Lingfeng; Fulham, Michael; Feng, Dagan

2014-02-01

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects. Copyright © 2013 Elsevier B.V. All rights reserved.

Automatic Dictionary Construction; Part II of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Part Two of the eighteenth report on Salton's Magical Automatic Retriever of Texts (SMART) project is composed of three papers: The first: "The Effect of Common Words and Synonyms on Retrieval Performance" by D. Bergmark discloses that removal of common words from the query and document vectors significantly increases precision and that…

Visualizing and improving the robustness of phase retrieval algorithms

DOE PAGES

Tripathi, Ashish; Leyffer, Sven; Munson, Todd; ...

2015-06-01

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

Visualizing and improving the robustness of phase retrieval algorithms

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

Tripathi, Ashish; Leyffer, Sven; Munson, Todd

Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.

The JPL Library Information Retrieval System

ERIC Educational Resources Information Center

Walsh, Josephine

1975-01-01

The development, capabilities, and products of the computer-based retrieval system of the Jet Propulsion Laboratory Library are described. The system handles books and documents, produces a book catalog, and provides a machine search capability. (Author)

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).

Cross-Modal Retrieval With CNN Visual Features: A New Baseline.

PubMed

Wei, Yunchao; Zhao, Yao; Lu, Canyi; Wei, Shikui; Liu, Luoqi; Zhu, Zhenfeng; Yan, Shuicheng

2017-02-01

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.

Image acquisition context: procedure description attributes for clinically relevant indexing and selective retrieval of biomedical images.

PubMed

Bidgood, W D; Bray, B; Brown, N; Mori, A R; Spackman, K A; Golichowski, A; Jones, R H; Korman, L; Dove, B; Hildebrand, L; Berg, M

1999-01-01

To support clinically relevant indexing of biomedical images and image-related information based on the attributes of image acquisition procedures and the judgments (observations) expressed by observers in the process of image interpretation. The authors introduce the notion of "image acquisition context," the set of attributes that describe image acquisition procedures, and present a standards-based strategy for utilizing the attributes of image acquisition context as indexing and retrieval keys for digital image libraries. The authors' indexing strategy is based on an interdependent message/terminology architecture that combines the Digital Imaging and Communication in Medicine (DICOM) standard, the SNOMED (Systematized Nomenclature of Human and Veterinary Medicine) vocabulary, and the SNOMED DICOM microglossary. The SNOMED DICOM microglossary provides context-dependent mapping of terminology to DICOM data elements. The capability of embedding standard coded descriptors in DICOM image headers and image-interpretation reports improves the potential for selective retrieval of image-related information. This favorably affects information management in digital libraries.

Searching for Images: The Analysis of Users' Queries for Image Retrieval in American History.

ERIC Educational Resources Information Center

Choi, Youngok; Rasmussen, Edie M.

2003-01-01

Studied users' queries for visual information in American history to identify the image attributes important for retrieval and the characteristics of users' queries for digital images, based on queries from 38 faculty and graduate students. Results of pre- and post-test questionnaires and interviews suggest principle categories of search terms.…

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

Validation of Rain Rate Retrievals for the Airborne Hurricane Imaging Radiometer (HIRAD)

NASA Technical Reports Server (NTRS)

Jacob, Maria Marta; Salemirad, Matin; Jones, W. Linwood; Biswas, Sayak; Cecil, Daniel

2015-01-01

The NASA Hurricane and Severe Storm Sentinel (HS3) mission is an aircraft field measurements program using NASA's unmanned Global Hawk aircraft system for remote sensing and in situ observations of Atlantic and Caribbean Sea hurricanes. One of the principal microwave instruments is the Hurricane Imaging Radiometer (HIRAD), which measures surface wind speeds and rain rates. For validation of the HIRAD wind speed measurement in hurricanes, there exists a comprehensive set of comparisons with the Stepped Frequency Microwave Radiometer (SFMR) with in situ GPS dropwindsondes [1]. However, for rain rate measurements, there are only indirect correlations with rain imagery from other HS3 remote sensors (e.g., the dual-frequency Ka- & Ku-band doppler radar, HIWRAP), which is only qualitative in nature. However, this paper presents results from an unplanned rain rate measurement validation opportunity that occurred in 2013, when HIRAD flew over an intense tropical squall line that was simultaneously observed by the Tampa NEXRAD meteorological radar (Fig. 1). During this experiment, Global Hawk flying at an altitude of 18 km made 3 passes over the rapidly propagating thunderstorm, while the TAMPA NEXRAD perform volume scans on a 5-minute interval. Using the well-documented NEXRAD Z-R relationship, 2D images of rain rate (mm/hr) were obtained at two altitudes (3 km & 6 km), which serve as surface truth for the HIRAD rain rate retrievals. A preliminary comparison of HIRAD rain rate retrievals (image) for the first pass and the corresponding closest NEXRAD rain image is presented in Fig. 2 & 3. This paper describes the HIRAD instrument, which 1D synthetic-aperture thinned array radiometer (STAR) developed by NASA Marshall Space Flight Center [2]. The rain rate retrieval algorithm, developed by Amarin et al. [3], is based on the maximum likelihood estimation (MLE) technique, which compares the observed Tb's at the HIRAD operating frequencies of 4, 5, 6 and 6.6 GHz with corresponding theoretical Tb values from a forward radiative transfer model (RTM). The optimum solution is the integrated rain rate that minimizes the difference between RTM and observed values. Because the excess Tb from rain comes from the direct upwelling and the indirect reflected downwelling paths through the atmosphere, there are several assumptions made for the 2D rain distribution in the antenna incident plane (crosstrack to flight direction). The opportunity to knowing 2D rain surface truth from NEXRAD at two different altitudes will enable a comprehensive evaluation to be preformed and reported in this paper.

Image selection system. [computerized data storage and retrieval system

NASA Technical Reports Server (NTRS)

Knutson, M. A.; Hurd, D.; Hubble, L.; Kroeck, R. M.

1974-01-01

An image selection (ISS) was developed for the NASA-Ames Research Center Earth Resources Aircraft Project. The ISS is an interactive, graphics oriented, computer retrieval system for aerial imagery. An analysis of user coverage requests and retrieval strategies is presented, followed by a complete system description. Data base structure, retrieval processors, command language, interactive display options, file structures, and the system's capability to manage sets of selected imagery are described. A detailed example of an area coverage request is graphically presented.

Basic firefly algorithm for document clustering

NASA Astrophysics Data System (ADS)

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

2015-12-01

The Document clustering plays significant role in Information Retrieval (IR) where it organizes documents prior to the retrieval process. To date, various clustering algorithms have been proposed and this includes the K-means and Particle Swarm Optimization. Even though these algorithms have been widely applied in many disciplines due to its simplicity, such an approach tends to be trapped in a local minimum during its search for an optimal solution. To address the shortcoming, this paper proposes a Basic Firefly (Basic FA) algorithm to cluster text documents. The algorithm employs the Average Distance to Document Centroid (ADDC) as the objective function of the search. Experiments utilizing the proposed algorithm were conducted on the 20Newsgroups benchmark dataset. Results demonstrate that the Basic FA generates a more robust and compact clusters than the ones produced by K-means and Particle Swarm Optimization (PSO).

FBC: a flat binary code scheme for fast Manhattan hash retrieval

NASA Astrophysics Data System (ADS)

Kong, Yan; Wu, Fuzhang; Gao, Lifa; Wu, Yanjun

2018-04-01

Hash coding is a widely used technique in approximate nearest neighbor (ANN) search, especially in document search and multimedia (such as image and video) retrieval. Based on the difference of distance measurement, hash methods are generally classified into two categories: Hamming hashing and Manhattan hashing. Benefitting from better neighborhood structure preservation, Manhattan hashing methods outperform earlier methods in search effectiveness. However, due to using decimal arithmetic operations instead of bit operations, Manhattan hashing becomes a more time-consuming process, which significantly decreases the whole search efficiency. To solve this problem, we present an intuitive hash scheme which uses Flat Binary Code (FBC) to encode the data points. As a result, the decimal arithmetic used in previous Manhattan hashing can be replaced by more efficient XOR operator. The final experiments show that with a reasonable memory space growth, our FBC speeds up more than 80% averagely without any search accuracy loss when comparing to the state-of-art Manhattan hashing methods.

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.

Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

2013-01-01

Uncertainties in the retrievals of microwaveland-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors inthe retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

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).

Evaluation of contents-based image retrieval methods for a database of logos on drug tablets

NASA Astrophysics Data System (ADS)

Geradts, Zeno J.; Hardy, Huub; Poortman, Anneke; Bijhold, Jurrien

2001-02-01

In this research an evaluation has been made of the different ways of contents based image retrieval of logos of drug tablets. On a database of 432 illicitly produced tablets (mostly containing MDMA), we have compared different retrieval methods. Two of these methods were available from commercial packages, QBIC and Imatch, where the implementation of the contents based image retrieval methods are not exactly known. We compared the results for this database with the MPEG-7 shape comparison methods, which are the contour-shape, bounding box and region-based shape methods. In addition, we have tested the log polar method that is available from our own research.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

PubMed

Chen, Yan; Yin, Xiaoshi; Li, Zhoujun; Hu, Xiaohua; Huang, Jimmy Xiangji

2012-06-11

In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance.

FAPA: Faculty Appointment Policy Archive, 1998. [CD-ROM.

ERIC Educational Resources Information Center

Trower, C. Ann

This CD-ROM presents 220 documents collected in Harvard University's Faculty Appointment Policy Archive (FAPA), the ZyFIND search and retrieval system, and instructions for their use. The FAPA system and ZyFIND allow browsing through documents, inserting bookmarks in documents, attaching notes to documents without modifying them, and selecting…

Location-Driven Image Retrieval for Images Collected by a Mobile Robot

NASA Astrophysics Data System (ADS)

Tanaka, Kanji; Hirayama, Mitsuru; Okada, Nobuhiro; Kondo, Eiji

Mobile robot teleoperation is a method for a human user to interact with a mobile robot over time and distance. Successful teleoperation depends on how well images taken by the mobile robot are visualized to the user. To enhance the efficiency and flexibility of the visualization, an image retrieval system on such a robot’s image database would be very useful. The main difference of the robot’s image database from standard image databases is that various relevant images exist due to variety of viewing conditions. The main contribution of this paper is to propose an efficient retrieval approach, named location-driven approach, utilizing correlation between visual features and real world locations of images. Combining the location-driven approach with the conventional feature-driven approach, our goal can be viewed as finding an optimal classifier between relevant and irrelevant feature-location pairs. An active learning technique based on support vector machine is extended for this aim.

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.

Abstracts of SIG Sessions.

ERIC Educational Resources Information Center

Proceedings of the ASIS Annual Meeting, 1993

1993-01-01

Presents abstracts of 34 special interest group (SIG) sessions. Highlights include humanities scholars and electronic texts; information retrieval and indexing systems design; automated indexing; domain analysis; query expansion in document retrieval systems; thesauri; business intelligence; Americans with Disabilities Act; management;…

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.

Multi-instance learning based on instance consistency for image retrieval

NASA Astrophysics Data System (ADS)

Zhang, Miao; Wu, Zhize; Wan, Shouhong; Yue, Lihua; Yin, Bangjie

2017-07-01

Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags which may result in a low accuracy. In this paper, we propose a new image retrieval approach called multiple instance learning based on instance-consistency (MILIC) to mitigate such issue. First, we select potential positive instances effectively in each positive bag by ranking instance-consistency (IC) values of instances. Then, we design a feature representation scheme, which can represent the relationship among bags and instances, based on potential positive instances to convert a bag into a single instance. Finally, we can use a standard single-instance learning strategy, such as the support vector machine, for performing object-based image retrieval. Experimental results on two challenging data sets show the effectiveness of our proposal in terms of accuracy and run time.

Hypertext Image Retrieval: The Evolution of an Application.

ERIC Educational Resources Information Center

Roberts, G. Louis; Kenney, Carol E.

1991-01-01

Describes the development and implementation of a full-text image retrieval system at the Boeing Commercial Airplane Group. The conversion of card formats to a microcomputer-based system using HyperCard is described; the online system architecture is explained; and future plans are discussed, including conversion to digital images. (LRW)

Automatic indexing and retrieval of encounter-specific evidence for point-of-care support.

PubMed

O'Sullivan, Dympna M; Wilk, Szymon A; Michalowski, Wojtek J; Farion, Ken J

2010-08-01

Evidence-based medicine relies on repositories of empirical research evidence that can be used to support clinical decision making for improved patient care. However, retrieving evidence from such repositories at local sites presents many challenges. This paper describes a methodological framework for automatically indexing and retrieving empirical research evidence in the form of the systematic reviews and associated studies from The Cochrane Library, where retrieved documents are specific to a patient-physician encounter and thus can be used to support evidence-based decision making at the point of care. Such an encounter is defined by three pertinent groups of concepts - diagnosis, treatment, and patient, and the framework relies on these three groups to steer indexing and retrieval of reviews and associated studies. An evaluation of the indexing and retrieval components of the proposed framework was performed using documents relevant for the pediatric asthma domain. Precision and recall values for automatic indexing of systematic reviews and associated studies were 0.93 and 0.87, and 0.81 and 0.56, respectively. Moreover, precision and recall for the retrieval of relevant systematic reviews and associated studies were 0.89 and 0.81, and 0.92 and 0.89, respectively. With minor modifications, the proposed methodological framework can be customized for other evidence repositories. Copyright 2010 Elsevier Inc. All rights reserved.

Bibliometric analysis of global migration health research in peer-reviewed literature (2000-2016).

PubMed

Sweileh, Waleed M; Wickramage, Kolitha; Pottie, Kevin; Hui, Charles; Roberts, Bayard; Sawalha, Ansam F; Zyoud, Saed H

2018-06-20

The health of migrants has become an important issue in global health and foreign policy. Assessing the current status of research activity and identifying gaps in global migration health (GMH) is an important step in mapping the evidence-base and on advocating health needs of migrants and mobile populations. The aim of this study was to analyze globally published peer-reviewed literature in GMH. A bibliometric analysis methodology was used. The Scopus database was used to retrieve documents in peer-reviewed journals in GMH for the study period from 2000 to 2016. A group of experts in GMH developed the needed keywords and validated the final search strategy. The number of retrieved documents was 21,457. Approximately one third (6878; 32.1%) of the retrieved documents were published in the last three years of the study period. In total, 5451 (25.4%) documents were about refugees and asylum seekers, while 1328 (6.2%) were about migrant workers, 440 (2.1%) were about international students, 679 (3.2%) were about victims of human trafficking/smuggling, 26 (0.1%) were about patients' mobility across international borders, and the remaining documents were about unspecified categories of migrants. The majority of the retrieved documents (10,086; 47.0%) were in psychosocial and mental health domain, while 2945 (13.7%) documents were in infectious diseases, 6819 (31.8%) documents were in health policy and systems, 2759 (12.8%) documents were in maternal and reproductive health, and 1918 (8.9%) were in non-communicable diseases. The contribution of authors and institutions in Asian countries, Latin America, Africa, Middle East, and Eastern European countries was low. Literature in GMH represents the perspectives of high-income migrant destination countries. Our heat map of research output shows that despite the ever-growing prominence of human mobility across the globe, and Sustainable Development Goals of leaving no one behind, research output on migrants' health is not consistent with the global migration pattern. A stronger evidence base is needed to enable authorities to make evidence-informed decisions on migration health policy and practice. Research collaboration and networks should be encouraged to prioritize research in GMH.

Retrieval feedback in MEDLINE.

PubMed Central

Srinivasan, P

1996-01-01

OBJECTIVE: To investigate a new approach for query expansion based on retrieval feedback. The first objective in this study was to examine alternative query-expansion methods within the same retrieval-feedback framework. The three alternatives proposed are: expansion on the MeSH query field alone, expansion on the free-text field alone, and expansion on both the MeSH and the free-text fields. The second objective was to gain further understanding of retrieval feedback by examining possible dependencies on relevant documents during the feedback cycle. DESIGN: Comparative study of retrieval effectiveness using the original unexpanded and the alternative expanded user queries on a MEDLINE test collection of 75 queries and 2,334 MEDLINE citations. MEASUREMENTS: Retrieval effectivenesses of the original unexpanded and the alternative expanded queries were compared using 11-point-average precision scores (11-AvgP). These are averages of precision scores obtained at 11 standard recall points. RESULTS: All three expansion strategies significantly improved the original queries in terms of retrieval effectiveness. Expansion on MeSH alone was equivalent to expansion on both MeSH and the free-text fields. Expansion on the free-text field alone improved the queries significantly less than did the other two strategies. The second part of the study indicated that retrieval-feedback-based expansion yields significant performance improvements independent of the availability of relevant documents for feedback information. CONCLUSIONS: Retrieval feedback offers a robust procedure for query expansion that is most effective for MEDLINE when applied to the MeSH field. PMID:8653452

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

TRECVID: the utility of a content-based video retrieval evaluation

NASA Astrophysics Data System (ADS)

Hauptmann, Alexander G.

2006-01-01

TRECVID, an annual retrieval evaluation benchmark organized by NIST, encourages research in information retrieval from digital video. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of semantic features, and the automatic segmentation of TV news broadcasts. Evaluations done in the context of the TRECVID benchmarks show that generally, speech transcripts and annotations provide the single most important clue for successful retrieval. However, automatically finding the individual images is still a tremendous and unsolved challenge. The evaluations repeatedly found that none of the multimedia analysis and retrieval techniques provide a significant benefit over retrieval using only textual information such as from automatic speech recognition transcripts or closed captions. In interactive systems, we do find significant differences among the top systems, indicating that interfaces can make a huge difference for effective video/image search. For interactive tasks efficient interfaces require few key clicks, but display large numbers of images for visual inspection by the user. The text search finds the right context region in the video in general, but to select specific relevant images we need good interfaces to easily browse the storyboard pictures. In general, TRECVID has motivated the video retrieval community to be honest about what we don't know how to do well (sometimes through painful failures), and has focused us to work on the actual task of video retrieval, as opposed to flashy demos based on technological capabilities.

Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida shelf using neural networks and impacts of temporal variabilities

NASA Astrophysics Data System (ADS)

El-Habashi, Ahmed; Duran, Claudia M.; Lovko, Vincent; Tomlinson, Michelle C.; Stumpf, Richard P.; Ahmed, Sam

2017-07-01

We apply a neural network (NN) technique to detect/track Karenia brevis harmful algal blooms (KB HABs) plaguing West Florida shelf (WFS) coasts from Visible-Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previously KB HABs detection primarily relied on the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite, depending on its remote sensing reflectance signal at the 678-nm chlorophyll fluorescence band (Rrs678) needed for normalized fluorescence height and related red band difference retrieval algorithms. VIIRS, MODIS-A's successor, does not have a 678-nm channel. Instead, our NN uses Rrs at 486-, 551-, and 671-nm VIIRS channels to retrieve phytoplankton absorption at 443 nm (a). The retrieved a images are next filtered by applying limits, defined by (i) low Rrs551-nm backscatter and (ii) a minimum a value associated with KB HABs. The filtered residual images are then converted to show chlorophyll-a concentrations [Chla] and KB cell counts. VIIRS retrievals using our NN and five other retrieval algorithms were compared and evaluated against numerous in situ measurements made over the four-year 2012 to 2016 period, for which VIIRS data are available. These comparisons confirm the viability and higher retrieval accuracies of the NN technique, when combined with the filtering constraints, for effective detection of KB HABs. Analysis of these results as well as sequential satellite observations and recent field measurements underline the importance of short-term temporal variabilities on retrieval accuracies.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

PubMed Central

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G.; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-01-01

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency. PMID:27688597

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

PubMed

Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

2016-02-12

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Optimisation of sea surface current retrieval using a maximum cross correlation technique on modelled sea surface temperature

NASA Astrophysics Data System (ADS)

Heuzé, Céline; Eriksson, Leif; Carvajal, Gisela

2017-04-01

Using sea surface temperature from satellite images to retrieve sea surface currents is not a new idea, but so far its operational near-real time implementation has not been possible. Validation studies are too region-specific or uncertain, due to the errors induced by the images themselves. Moreover, the sensitivity of the most common retrieval method, the maximum cross correlation, to the three parameters that have to be set is unknown. Using model outputs instead of satellite images, biases induced by this method are assessed here, for four different seas of Western Europe, and the best of nine settings and eight temporal resolutions are determined. For all regions, tracking a small 5 km pattern from the first image over a large 30 km region around its original location on a second image, separated from the first image by 6 to 9 hours returned the most accurate results. Moreover, for all regions, the problem is not inaccurate results but missing results, where the velocity is too low to be picked by the retrieval. The results are consistent both with limitations caused by ocean surface current dynamics and with the available satellite technology, indicating that automated sea surface current retrieval from sea surface temperature images is feasible now, for search and rescue operations, pollution confinement or even for more energy efficient and comfortable ship navigation.

DARE: Unesco Computerized Data Retrieval System for Documentation in the Social and Human Sciences (Including an Analysis of the Present System).

ERIC Educational Resources Information Center

Vasarhelyi, Paul

The new data retrieval system for the social sciences which has recently been installed in the UNESCO Secretariat in Paris is described in this comprehensive report. The computerized system is designed to facilitate the existing storage systems in the circulation of information, data retrieval, and indexing services. Basically, this report…

Personalizing Information Retrieval Using Interaction Behaviors in Search Sessions in Different Types of Tasks

ERIC Educational Resources Information Center

Liu, Chang

2012-01-01

When using information retrieval (IR) systems, users often pose short and ambiguous query terms. It is critical for IR systems to obtain more accurate representation of users' information need, their document preferences, and the context they are working in, and then incorporate them into the design of the systems to tailor retrieval to…

SPIRES (Stanford Physics Information REtrieval System) 1969-70 Annual Report to the National Science Foundation (Office of Science Information Service).

ERIC Educational Resources Information Center

Parker, Edwin B.

The third annual report (covering the 18-month period from January 1969 to June 1970) of the Stanford Physics Information REtrieval System (SPIRES) project, which is developing an augmented bibliographic retrieval capability, is presented in this document. A first section describes the background of the project and its association with Project…

User centered and ontology based information retrieval system for life sciences.

PubMed

Sy, Mohameth-François; Ranwez, Sylvie; Montmain, Jacky; Regnault, Armelle; Crampes, Michel; Ranwez, Vincent

2012-01-25

Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

User centered and ontology based information retrieval system for life sciences

PubMed Central

2012-01-01

Background Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help. PMID:22373375

Occam's razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2005-01-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

Occam"s razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2004-12-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

Aquarius Salinity Retrieval Algorithm: Final Pre-Launch Version

NASA Technical Reports Server (NTRS)

Wentz, Frank J.; Le Vine, David M.

2011-01-01

This document provides the theoretical basis for the Aquarius salinity retrieval algorithm. The inputs to the algorithm are the Aquarius antenna temperature (T(sub A)) measurements along with a number of NCEP operational products and pre-computed tables of space radiation coming from the galaxy and sun. The output is sea-surface salinity and many intermediate variables required for the salinity calculation. This revision of the Algorithm Theoretical Basis Document (ATBD) is intended to be the final pre-launch version.

Management of technical date in Nihon Doro kodan

NASA Astrophysics Data System (ADS)

Hanada, Jun'ichi

Nihon Doro Kodan Laboratory has collected and contributed technical data (microfiches, aerial photographs, books and literature) on plans, designs, constructions and maintenance of the national expressways and the ordinary toll roads since 1968. This work is systematized on computer to retrieve and contribute data faster. Now Laboratory operates Technical Data Management System which manages all of technical data and Technical Document Management System which manages technical documents. These systems stand on users' on-line retrieval and data accumuration by microfiches and optical disks.

Comment on "An Evaluation of Query Expansion by the Addition of Clustered Terms for a Document Retrieval System"

ERIC Educational Resources Information Center

Salton, G.

1972-01-01

The author emphasized that one cannot conclude from the experiments reported upon that term clusters (or equivalently, keyword classifications or thesauruses) are not useful in retrieval. (2 references) (Author)

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

PubMed

Perez-Rey, David; Jimenez-Castellanos, Ana; Garcia-Remesal, Miguel; Crespo, Jose; Maojo, Victor

2012-04-05

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

PubMed Central

2012-01-01

Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems. PMID:22480327

Surface retrievals from Hyperion EO1 using a new, fast, 1D-Var based retrieval code

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan; Wong, Gerald

2015-05-01

We have developed a new algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space-borne, hyperspectral imagers such as Hyperion EO-1. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes. We successfully tested this new approach using hyperspectral images taken by Hyperion EO-1, an experimental pushbroom imaging spectrometer operated by NASA.

MARs Color Imager (MARCI) Daily Global Ozone Column Mapping from the Mars Reconnaissance Orbiter (MRO): A Survey of 2006-2010 Results

NASA Astrophysics Data System (ADS)

Clancy, R. T.; Wolff, M. J.; Malin, M. C.; Cantor, B. A.

2010-12-01

MARCI UV band imaging photometry within (260nm) and outside (320nm) the Hartley ozone band absorption supports daily global mapping of Mars ozone column abundances. Key retrieval issues include accurate UV radiometric calibrations, detailed specifications of surface and atmospheric background reflectance (surface albedo, atmospheric Raleigh and dust scattering/absorption), and simultaneous cloud retrievals. The implementation of accurate radiative transfer (RT) treatments of these processes has been accomplished (Wolff et al., 2010) such that daily global mapping retrievals for Mars ozone columns have been completed for the 2006-2010 period of MARCI global imaging. Ozone retrievals are most accurate for high column abundances associated with mid-to-high latitude regions during fall, winter, and spring seasons. We present a survey of these MARCI ozone column retrievals versus season, latitude, longitude, and year.

Coupled Retrieval of Liquid Water Cloud and Above-Cloud Aerosol Properties Using the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)

NASA Astrophysics Data System (ADS)

Xu, Feng; van Harten, Gerard; Diner, David J.; Davis, Anthony B.; Seidel, Felix C.; Rheingans, Brian; Tosca, Mika; Alexandrov, Mikhail D.; Cairns, Brian; Ferrare, Richard A.; Burton, Sharon P.; Fenn, Marta A.; Hostetler, Chris A.; Wood, Robert; Redemann, Jens

2018-03-01

An optimization algorithm is developed to retrieve liquid water cloud properties including cloud optical depth (COD), droplet size distribution and cloud top height (CTH), and above-cloud aerosol properties including aerosol optical depth (AOD), single-scattering albedo, and microphysical properties from sweep-mode observations by Jet Propulsion Laboratory's Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) instrument. The retrieval is composed of three major steps: (1) initial estimate of the mean droplet size distribution across the entire image of 80-100 km along track by 10-25 km across track from polarimetric cloudbow observations, (2) coupled retrieval of image-scale cloud and above-cloud aerosol properties by fitting the polarimetric data at all observation angles, and (3) iterative retrieval of 1-D radiative transfer-based COD and droplet size distribution at pixel scale (25 m) by establishing relationships between COD and droplet size and fitting the total radiance measurements. Our retrieval is tested using 134 AirMSPI data sets acquired during the National Aeronautics and Space Administration (NASA) field campaign ObseRvations of Aerosols above CLouds and their intEractionS. The retrieved above-cloud AOD and CTH are compared to coincident HSRL-2 (HSRL-2, NASA Langley Research Center) data, and COD and droplet size distribution parameters (effective radius reff and effective variance veff) are compared to coincident Research Scanning Polarimeter (RSP) (NASA Goddard Institute for Space Studies) data. Mean absolute differences between AirMSPI and HSRL-2 retrievals of above-cloud AOD at 532 nm and CTH are 0.03 and <0.5 km, respectively. At RSP's footprint scale ( 323 m), mean absolute differences between RSP and AirMSPI retrievals of COD, reff, and veff in the cloudbow area are 2.33, 0.69 μm, and 0.020, respectively. Neglect of smoke aerosols above cloud leads to an underestimate of image-averaged COD by 15%.

A selective deficit in imageable concepts: a window to the organization of the conceptual system

PubMed Central

Gvion, Aviah; Friedmann, Naama

2013-01-01

Nissim, a 64 years old Hebrew-speaking man who sustained an ischemic infarct in the left occipital lobe, exhibited an intriguing pattern. He could hold a deep and fluent conversation about abstract and complex issues, such as the social risks in unemployment, but failed to retrieve imageable words such as ball, spoon, carrot, or giraffe. A detailed study of the words he could and could not retrieve, in tasks of picture naming, tactile naming, and naming to definition, indicated that whereas he was able to retrieve abstract words, he had severe difficulties when trying to retrieve imageable words. The same dissociation also applied for proper names—he could retrieve names of people who have no visual image attached to their representation (such as the son of the biblical Abraham), but could not name people who had a visual image (such as his own son, or Barack Obama). When he tried to produce imageable words, he mainly produced perseverations and empty speech, and some semantic paraphasias. He did not produce perseverations when he tried to retrieve abstract words. This suggests that perseverations may occur when the phonological production system produces a word without proper activation in the semantic lexicon. Nissim evinced a similar dissociation in comprehension—he could understand abstract words and sentences but failed to understand sentences with imageable words, and to match spoken imageable words to pictures or to semantically related imageable words. He was able to understand proverbs with imageable literal meaning but abstract figurative meaning. His comprehension was impaired also in tasks of semantic associations of pictures, pointing to a conceptual, rather than lexical source of the deficit. His visual perception as well as his phonological input and output lexicons and buffers (assessed by auditory lexical decision, word and sentence repetition, and writing to dictation) were intact, supporting a selective conceptual system impairment. He was able to retrieve gestures for objects and pictures he saw, indicating that his access to concepts often sufficed for the activation of the motoric information but did not suffice for access to the entry in the semantic lexicon. These results show that imageable concepts can be selectively impaired, and shed light on the organization of conceptual-semantic system. PMID:23785321

A selective deficit in imageable concepts: a window to the organization of the conceptual system.

PubMed

Gvion, Aviah; Friedmann, Naama

2013-01-01

Nissim, a 64 years old Hebrew-speaking man who sustained an ischemic infarct in the left occipital lobe, exhibited an intriguing pattern. He could hold a deep and fluent conversation about abstract and complex issues, such as the social risks in unemployment, but failed to retrieve imageable words such as ball, spoon, carrot, or giraffe. A detailed study of the words he could and could not retrieve, in tasks of picture naming, tactile naming, and naming to definition, indicated that whereas he was able to retrieve abstract words, he had severe difficulties when trying to retrieve imageable words. The same dissociation also applied for proper names-he could retrieve names of people who have no visual image attached to their representation (such as the son of the biblical Abraham), but could not name people who had a visual image (such as his own son, or Barack Obama). When he tried to produce imageable words, he mainly produced perseverations and empty speech, and some semantic paraphasias. He did not produce perseverations when he tried to retrieve abstract words. This suggests that perseverations may occur when the phonological production system produces a word without proper activation in the semantic lexicon. Nissim evinced a similar dissociation in comprehension-he could understand abstract words and sentences but failed to understand sentences with imageable words, and to match spoken imageable words to pictures or to semantically related imageable words. He was able to understand proverbs with imageable literal meaning but abstract figurative meaning. His comprehension was impaired also in tasks of semantic associations of pictures, pointing to a conceptual, rather than lexical source of the deficit. His visual perception as well as his phonological input and output lexicons and buffers (assessed by auditory lexical decision, word and sentence repetition, and writing to dictation) were intact, supporting a selective conceptual system impairment. He was able to retrieve gestures for objects and pictures he saw, indicating that his access to concepts often sufficed for the activation of the motoric information but did not suffice for access to the entry in the semantic lexicon. These results show that imageable concepts can be selectively impaired, and shed light on the organization of conceptual-semantic system.

Let Documents Talk to Each Other: A Computer Model for Connection of Short Documents.

ERIC Educational Resources Information Center

Chen, Z.

1993-01-01

Discusses the integration of scientific texts through the connection of documents and describes a computer model that can connect short documents. Information retrieval and artificial intelligence are discussed; a prototype system of the model is explained; and the model is compared to other computer models. (17 references) (LRW)

Automated Management Of Documents

NASA Technical Reports Server (NTRS)

Boy, Guy

1995-01-01

Report presents main technical issues involved in computer-integrated documentation. Problems associated with automation of management and maintenance of documents analyzed from perspectives of artificial intelligence and human factors. Technologies that may prove useful in computer-integrated documentation reviewed: these include conventional approaches to indexing and retrieval of information, use of hypertext, and knowledge-based artificial-intelligence systems.

Experiments on sparsity assisted phase retrieval of phase objects

NASA Astrophysics Data System (ADS)

Gaur, Charu; Lochab, Priyanka; Khare, Kedar

2017-05-01

Iterative phase retrieval algorithms such as the Gerchberg-Saxton method and the Fienup hybrid input-output method are known to suffer from the twin image stagnation problem, particularly when the solution to be recovered is complex valued and has centrosymmetric support. Recently we showed that the twin image stagnation problem can be addressed using image sparsity ideas (Gaur et al 2015 J. Opt. Soc. Am. A 32 1922). In this work we test this sparsity assisted phase retrieval method with experimental single shot Fourier transform intensity data frames corresponding to phase objects displayed on a spatial light modulator. The standard iterative phase retrieval algorithms are combined with an image sparsity based penalty in an adaptive manner. Illustrations for both binary and continuous phase objects are provided. It is observed that image sparsity constraint has an important role to play in obtaining meaningful phase recovery without encountering the well-known stagnation problems. The results are valuable for enabling single shot coherent diffraction imaging of phase objects for applications involving illumination wavelengths over a wide range of electromagnetic spectrum.

Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

PubMed

Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

2014-01-01

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

Is the bang worth the buck? A RAID performance study

NASA Technical Reports Server (NTRS)

Hauser, Susan E.; Berman, Lewis E.; Thoma, George R.

1996-01-01

Expecting a high data delivery rate as well as data protection, the Lister Hill National Center for Biomedical Communications procured a RAID system to house image files for image delivery applications. A study was undertaken to determine the configuration of the RAID system that would provide for the fastest retrieval of image files. Average retrieval times with single and with concurrent users were measured for several stripe widths and several numbers of disks for RAID levels 0, 0+1 and 5. These are compared to each other and to average retrieval times for non-RAID configurations of the same hardware. Although the study in ongoing, a few conclusions have emerged regarding the tradeoffs among the different configurations with respect to file retrieval speed and cost.

An introduction to information retrieval: applications in genomics

PubMed Central

Nadkarni, P M

2011-01-01

Information retrieval (IR) is the field of computer science that deals with the processing of documents containing free text, so that they can be rapidly retrieved based on keywords specified in a user’s query. IR technology is the basis of Web-based search engines, and plays a vital role in biomedical research, because it is the foundation of software that supports literature search. Documents can be indexed by both the words they contain, as well as the concepts that can be matched to domain-specific thesauri; concept matching, however, poses several practical difficulties that make it unsuitable for use by itself. This article provides an introduction to IR and summarizes various applications of IR and related technologies to genomics. PMID:12049181

Use of remote sensing and UAV for the management of degraded ecosystems: the case study of overgrazing in Randi Forest, Cyprus

NASA Astrophysics Data System (ADS)

Themistocleous, K.; Papadavid, G.; Christoforou, M.; Agapiou, A.; Andreou, K.; Tsaltas, D.; Hadjimitsis, D. G.

2014-08-01

This paper provides the results obtained by using satellite imagery and UAV data for managing a degraded system over Randi Forest in Cyprus. Landsat TM/ETM+ and GeoEye images have been used to retrieve several indices with the main aim to managing the overgrazed area. Aerial photographs were acquired in order to document and monitor the overgrazed areas, which also include seasonal changes in vegetation and soil. UAVs were used to create ortho-photos and DEMS. Satellite images were used to conduct NDVIs of the study area. The resulting findings provide a detailed image of the specific location of overgrazed areas. The results of the study can be used for decision makers to establish effective strategies to avoid similar scenarios of overgrazing in other parts of Cyprus.This study was funded by the FP7 programme CASCADE Project on sudden and catastrophic shifts in dryland Mediterranean ecosystems (2012-2017).

Effect of anxiety on behavioural pattern separation in humans

PubMed Central

Mathur, Ambika; Adu-Brimpong, Joel; Hale, Elizabeth A.; Ernst, Monique; Grillon, Christian

2016-01-01

Behavioural pattern separation (BPS), the ability to distinguish among similar stimuli based on subtle physical differences, has been used to study the mechanism underlying stimulus generalisation. Fear overgeneralisation is often observed in individuals with posttraumatic stress disorder and other anxiety disorders. However, the relationship between anxiety and BPS remains unclear. The purpose of this study was to determine the effect of anxiety (threat of shock) on BPS, which was assessed across separate encoding and retrieval sessions. Images were encoded/retrieved during blocks of threat or safety in a 2 × 2 factorial design. During retrieval, participants indicated whether images were new, old, or altered. Better accuracy was observed for altered images encoded during periods of threat compared to safety, but only if those images were also retrieved during periods of safety. These results suggest that overgeneralisation in anxiety may be due to altered pattern separation. PMID:26480349

Caval penetration by retrievable inferior vena cava filters: a retrospective comparison of Option and Günther Tulip filters.

PubMed

Olorunsola, Olufoladare G; Kohi, Maureen P; Fidelman, Nicholas; Westphalen, Antonio C; Kolli, Pallav K; Taylor, Andrew G; Gordon, Roy L; LaBerge, Jeanne M; Kerlan, Robert K

2013-04-01

To compare the frequency of vena caval penetration by the struts of the Option and Günther Tulip cone filters on postplacement computed tomography (CT) imaging. All patients who had an Option or Günther Tulip inferior vena cava (IVC) filter placed between January 2010 and May 2012 were identified retrospectively from medical records. Of the 208 IVC filters placed, the positions of 58 devices (21 Option filters, 37 Günther Tulip filters [GTFs]) were documented on follow-up CT examinations obtained for reasons unrelated to filter placement. In cases when multiple CT studies were obtained after placement, each study was reviewed, for a total of 80 examinations. Images were assessed for evidence of caval wall penetration by filter components, noting the number of penetrating struts and any effect on pericaval tissues. Penetration of at least one strut was observed in 17% of all filters imaged by CT between 1 and 447 days following placement. Although there was no significant difference in the overall prevalence of penetration when comparing the Option filter and GTF (Option, 10%; GTF, 22%), only GTFs showed time-dependent penetration, with penetration becoming more likely after prolonged indwelling times. No patient had damage to pericaval tissues or documented symptoms attributed to penetration. Although the Günther Tulip and Option filters exhibit caval penetration at CT imaging, only the GTF exhibits progressive penetration over time. Copyright © 2013 SIR. Published by Elsevier Inc. All rights reserved.

Wavelet optimization for content-based image retrieval in medical databases.

PubMed

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

X-Ray Phase Imaging for Breast Cancer Detection

DTIC Science & Technology

2012-09-01

the Gerchberg-Saxton algorithm in the Fresnel diffraction regime, and is much more robust against image noise than the TIE-based method. For details...developed efficient coding with the software modules for the image registration, flat-filed correction , and phase retrievals. In addition, we...X, Liu H. 2010. Performance analysis of the attenuation-partition based iterative phase retrieval algorithm for in-line phase-contrast imaging

Retrieval of the thickness of undeformed sea ice from C-band compact polarimetric SAR images

NASA Astrophysics Data System (ADS)

Zhang, X.; Dierking, W.; Zhang, J.; Meng, J. M.; Lang, H. T.

2015-10-01

In this paper we introduce a parameter for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric SAR images. The parameter is denoted as "CP-Ratio". In model simulations we investigated the sensitivity of CP-Ratio to the dielectric constant, thickness, surface roughness, and incidence angle. From the results of the simulations we deduced optimal conditions for the thickness retrieval. On the basis of C-band CTLR SAR data, which were generated from Radarsat-2 quad-polarization images acquired jointly with helicopter-borne sea ice thickness measurements in the region of the Sea of Labrador, we tested empirical equations for thickness retrieval. An exponential fit between CP-Ratio and ice thickness provides the most reliable results. Based on a validation using other compact polarimetric SAR images from the same region we found a root mean square (rms) error of 8 cm and a maximum correlation coefficient of 0.92 for the retrieval procedure when applying it on level ice of 0.9 m mean thickness.

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.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar

NASA Technical Reports Server (NTRS)

Zhao, Feng; Yang, Xiaoyuan; Schull, Mithcell A.; Roman-Colon, Miguel O.; Yao, Tian; Wang, Zhuosen; Zhang, Qingling; Jupp, David L. B.; Lovell, Jenny L.; Culvenor, Darius;

Learning Short Binary Codes for Large-scale Image Retrieval.

PubMed

Liu, Li; Yu, Mengyang; Shao, Ling

2017-03-01

Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications, such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices. In this paper, we propose a novel unsupervised hashing approach called min-cost ranking (MCR) specifically for learning powerful short binary codes (i.e., usually the code length shorter than 100 b) for scalable image retrieval tasks. By exploring the discriminative ability of each dimension of data, MCR can generate one bit binary code for each dimension and simultaneously rank the discriminative separability of each bit according to the proposed cost function. Only top-ranked bits with minimum cost-values are then selected and grouped together to compose the final salient binary codes. Extensive experimental results on large-scale retrieval demonstrate that MCR can achieve comparative performance as the state-of-the-art hashing algorithms but with significantly shorter codes, leading to much faster large-scale retrieval.

High red blood cell composition in clots is associated with successful recanalization during intra-arterial thrombectomy.

PubMed

Shin, Jong Wook; Jeong, Hye Seon; Kwon, Hyon-Jo; Song, Kyu Sang; Kim, Jei

2018-01-01

We evaluated the composition of individual clots retrieved during intra-arterial thrombectomy in relation to recanalization success, stroke subtype, and the presence of clot signs on initial brain images. We analyzed clot and interventional data from 145 retrieval trials performed for 37 patients (69.5±14.0 years, 20 men, large artery atherosclerosis, n = 7; cardioembolism, n = 22; undetermined etiology, n = 8) who had undergone intra-arterial thrombectomy. Rates of clot retrieval and successful recanalization (Arterial Occlusive Lesion score of 2-3) for separate retrieval trials were evaluated. The area occupied by red blood cell (RBC), fibrin/platelets, and white blood cell (WBC) was measured from digitized images of hematoxylin-eosin stained clots. Compositional differences were compared according to recanalization success, stroke subtype, and the presence of hyperdense clot sign on initial computed tomography and/or blooming artifact on magnetic resonance image. Of the 145 total retrieval trials (3.4±2.4 times per patient), clot was retrieved in 93 trials (64%), while recanalization was successful in 73 (50%). Fibrin/platelets (63%) occupied the greatest area in retrieved clots, followed by RBCs (33%) and WBCs (4%). Clots retrieved from successful recanalization exhibited higher RBC composition (37%) than those retrieved from non-recanalization trials (20%, p = 0.001). RBC composition was higher in cardioembolic stroke (38%) rather than large artery atherosclerosis (23%) and undetermined etiology (26%, p = 0.01). Clots exhibiting clot signs (40%) had higher RBC composition than those without clot signs (19%, p = 0.001). RBC-rich clots were associated with successful recanalization of intra-arterial thrombectomy, cardioembolic stroke, and the presence of clot-signs on initial brain images.

High red blood cell composition in clots is associated with successful recanalization during intra-arterial thrombectomy

PubMed Central

Shin, Jong Wook; Jeong, Hye Seon; Kwon, Hyon-Jo; Song, Kyu Sang

2018-01-01

We evaluated the composition of individual clots retrieved during intra-arterial thrombectomy in relation to recanalization success, stroke subtype, and the presence of clot signs on initial brain images. We analyzed clot and interventional data from 145 retrieval trials performed for 37 patients (69.5±14.0 years, 20 men, large artery atherosclerosis, n = 7; cardioembolism, n = 22; undetermined etiology, n = 8) who had undergone intra-arterial thrombectomy. Rates of clot retrieval and successful recanalization (Arterial Occlusive Lesion score of 2–3) for separate retrieval trials were evaluated. The area occupied by red blood cell (RBC), fibrin/platelets, and white blood cell (WBC) was measured from digitized images of hematoxylin-eosin stained clots. Compositional differences were compared according to recanalization success, stroke subtype, and the presence of hyperdense clot sign on initial computed tomography and/or blooming artifact on magnetic resonance image. Of the 145 total retrieval trials (3.4±2.4 times per patient), clot was retrieved in 93 trials (64%), while recanalization was successful in 73 (50%). Fibrin/platelets (63%) occupied the greatest area in retrieved clots, followed by RBCs (33%) and WBCs (4%). Clots retrieved from successful recanalization exhibited higher RBC composition (37%) than those retrieved from non-recanalization trials (20%, p = 0.001). RBC composition was higher in cardioembolic stroke (38%) rather than large artery atherosclerosis (23%) and undetermined etiology (26%, p = 0.01). Clots exhibiting clot signs (40%) had higher RBC composition than those without clot signs (19%, p = 0.001). RBC-rich clots were associated with successful recanalization of intra-arterial thrombectomy, cardioembolic stroke, and the presence of clot-signs on initial brain images. PMID:29782513

Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

NASA Astrophysics Data System (ADS)

Acton, Scott T.; Gilliam, Andrew D.; Li, Bing; Rossi, Adam

2008-02-01

Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture (wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach to match annotated printed circuit board images for which no schematic or connectivity information is available. The shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.

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.

Line-based logo recognition through a web-camera

NASA Astrophysics Data System (ADS)

Chen, Xiaolu; Wang, Yangsheng; Feng, Xuetao

2007-11-01

Logo recognition has gained much development in the document retrieval and shape analysis domain. As human computer interaction becomes more and more popular, the logo recognition through a web-camera is a promising technology in view of application. But for practical application, the study of logo recognition in real scene is much more difficult than the work in clear scene. To cope with the need, we make some improvements on conventional method. First, moment information is used to calculate the test image's orientation angle, which is used to normalize the test image. Second, the main structure of the test image, which is represented by lines patterns, is acquired and modified Hausdorff distance is employed to match the image and each of the existing templates. The proposed method, which is invariant to scale and rotation, gives good result and can work at real-time. The main contribution of this paper is that some improvements are introduced into the exiting recognition framework which performs much better than the original one. Besides, we have built a highly successful logo recognition system using our improved method.

Three-dimensional imaging using phase retrieval with two focus planes

NASA Astrophysics Data System (ADS)

Ilovitsh, Tali; Ilovitsh, Asaf; Weiss, Aryeh; Meir, Rinat; Zalevsky, Zeev

2016-03-01

This work presents a technique for a full 3D imaging of biological samples tagged with gold-nanoparticles (GNPs) using only two images, rather than many images per volume as is currently needed for 3D optical sectioning microscopy. The proposed approach is based on the Gerchberg-Saxton (GS) phase retrieval algorithm. The reconstructed field is free space propagated to all other focus planes using post processing, and the 2D z-stack is merged to create a 3D image of the sample with high fidelity. Because we propose to apply the phase retrieving on nano particles, the regular ambiguities typical to the Gerchberg-Saxton algorithm, are eliminated. In addition, since the method requires the capturing of two images only, it can be suitable for 3D live cell imaging. The proposed concept is presented and validated both on simulated data as well as experimentally.

Secure image retrieval with multiple keys

NASA Astrophysics Data System (ADS)

Liang, Haihua; Zhang, Xinpeng; Wei, Qiuhan; Cheng, Hang

2018-03-01

This article proposes a secure image retrieval scheme under a multiuser scenario. In this scheme, the owner first encrypts and uploads images and their corresponding features to the cloud; then, the user submits the encrypted feature of the query image to the cloud; next, the cloud compares the encrypted features and returns encrypted images with similar content to the user. To find the nearest neighbor in the encrypted features, an encryption with multiple keys is proposed, in which the query feature of each user is encrypted by his/her own key. To improve the key security and space utilization, global optimization and Gaussian distribution are, respectively, employed to generate multiple keys. The experiments show that the proposed encryption can provide effective and secure image retrieval for each user and ensure confidentiality of the query feature of each user.

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

NASA Technical Reports Server (NTRS)

Sun-Mack, Sunny; Minnis, Patrick; Chen, Yan; Yi, Yuhong; Huang, Jainping; Lin, Bin; Fan, Alice; Gibson, Sharon; Chang, Fu-Lung

2006-01-01

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces.

A Method for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data

NASA Technical Reports Server (NTRS)

Koshak, William J.

2009-01-01

A general theory for retrieving the fraction of ground flashes in N lightning observed by a satellite-based lightning imager is provided. An "exponential model" is applied as a physically reasonable constraint to describe the measured optical parameter distributions, and population statistics (i.e., mean, variance) are invoked to add additional constraints to the retrieval process. The retrieval itself is expressed in terms of a Bayesian inference, and the Maximum A Posteriori (MAP) solution is obtained. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing satellite global lightning climatology into separate ground and cloud flash climatologies will improve estimates of lightning nitrogen oxides (NOx) production; this in turn will improve both regional air quality and global chemistry/climate model predictions.

Deep Question Answering for protein annotation

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/ PMID:26384372

Deep Question Answering for protein annotation.

PubMed

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

Digital adaptive optics confocal microscopy based on iterative retrieval of optical aberration from a guidestar hologram

PubMed Central

Liu, Changgeng; Thapa, Damber; Yao, Xincheng

2017-01-01

Guidestar hologram based digital adaptive optics (DAO) is one recently emerging active imaging modality. It records each complex distorted line field reflected or scattered from the sample by an off-axis digital hologram, measures the optical aberration from a separate off-axis digital guidestar hologram, and removes the optical aberration from the distorted line fields by numerical processing. In previously demonstrated DAO systems, the optical aberration was directly retrieved from the guidestar hologram by taking its Fourier transform and extracting the phase term. For the direct retrieval method (DRM), when the sample is not coincident with the guidestar focal plane, the accuracy of the optical aberration retrieved by DRM undergoes a fast decay, leading to quality deterioration of corrected images. To tackle this problem, we explore here an image metrics-based iterative method (MIM) to retrieve the optical aberration from the guidestar hologram. Using an aberrated objective lens and scattering samples, we demonstrate that MIM can improve the accuracy of the retrieved aberrations from both focused and defocused guidestar holograms, compared to DRM, to improve the robustness of the DAO. PMID:28380937

Surface reflectance retrieval from imaging spectrometer data using three atmospheric codes

NASA Astrophysics Data System (ADS)

Staenz, Karl; Williams, Daniel J.; Fedosejevs, Gunar; Teillet, Phil M.

1994-12-01

Surface reflectance retrieval from imaging spectrometer data has become important for quantitative information extraction in many application areas. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes play an important role for removal of the scattering and gaseous absorption effects of the atmosphere. The present study evaluates surface reflectances retrieved from airborne visible/infrared imaging spectrometer (AVIRIS) data using three radiative transfer codes: modified 5S (M5S), 6S, and MODTRAN2. Comparisons of the retrieved surface reflectance with ground-based reflectance were made for different target types such as asphalt, gravel, grass/soil mixture (soccer field), and water (Sooke Lake). The results indicate that the estimation of the atmospheric water vapor content is important for an accurate surface reflectance retrieval regardless of the radiative transfer code used. For the present atmospheric conditions, a difference of 0.1 in aerosol optical depth had little impact on the retrieved surface reflectance. The performance of MODTRAN2 is superior in the gas absorption regions compared to M5S and 6S.

On-Line Retrieval II.

ERIC Educational Resources Information Center

Kurtz, Peter; And Others

This report is concerned with the implementation of two interrelated computer systems: an automatic document analysis and classification package, and an on-line interactive information retrieval system which utilizes the information gathered during the automatic classification phase. Well-known techniques developed by Salton and Dennis have been…

An Experiment in Index Term Frequency

ERIC Educational Resources Information Center

Svenonius, Elaine

1972-01-01

The question is asked: Of index terms assigned to documents, which function most effectively in retrieval, the most used or popular terms, or those which are used relatively infrequently? The experiment is a retrieval experiment and uses the Cranfield-Salton data. (14 references) (Author)

Comparing the performance of two CBIRS indexing schemes

NASA Astrophysics Data System (ADS)

Mueller, Wolfgang; Robbert, Guenter; Henrich, Andreas

2003-01-01

Content based image retrieval (CBIR) as it is known today has to deal with a number of challenges. Quickly summarized, the main challenges are firstly, to bridge the semantic gap between high-level concepts and low-level features using feedback, secondly to provide performance under adverse conditions. High-dimensional spaces, as well as a demanding machine learning task make the right way of indexing an important issue. When indexing multimedia data, most groups opt for extraction of high-dimensional feature vectors from the data, followed by dimensionality reduction like PCA (Principal Components Analysis) or LSI (Latent Semantic Indexing). The resulting vectors are indexed using spatial indexing structures such as kd-trees or R-trees, for example. Other projects, such as MARS and Viper propose the adaptation of text indexing techniques, notably the inverted file. Here, the Viper system is the most direct adaptation of text retrieval techniques to quantized vectors. However, while the Viper query engine provides decent performance together with impressive user-feedback behavior, as well as the possibility for easy integration of long-term learning algorithms, and support for potentially infinite feature vectors, there has been no comparison of vector-based methods and inverted-file-based methods under similar conditions. In this publication, we compare a CBIR query engine that uses inverted files (Bothrops, a rewrite of the Viper query engine based on a relational database), and a CBIR query engine based on LSD (Local Split Decision) trees for spatial indexing using the same feature sets. The Benchathlon initiative works on providing a set of images and ground truth for simulating image queries by example and corresponding user feedback. When performing the Benchathlon benchmark on a CBIR system (the System Under Test, SUT), a benchmarking harness connects over internet to the SUT, performing a number of queries using an agreed-upon protocol, the multimedia retrieval markup language (MRML). Using this benchmark one can measure the quality of retrieval, as well as the overall (speed) performance of the benchmarked system. Our Benchmarks will draw on the Benchathlon"s work for documenting the retrieval performance of both inverted file-based and LSD tree based techniques. However in addition to these results, we will present statistics, that can be obtained only inside the system under test. These statistics will include the number of complex mathematical operations, as well as the amount of data that has to be read from disk during operation of a query.

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.

WEBCAP: Web Scheduler for Distance Learning Multimedia Documents with Web Workload Considerations

ERIC Educational Resources Information Center

Habib, Sami; Safar, Maytham

2008-01-01

In many web applications, such as the distance learning, the frequency of refreshing multimedia web documents places a heavy burden on the WWW resources. Moreover, the updated web documents may encounter inordinate delays, which make it difficult to retrieve web documents in time. Here, we present an Internet tool called WEBCAP that can schedule…

Electronic Document Delivery: OCLC's Prototype System.

ERIC Educational Resources Information Center

Hickey, Thomas B.; Calabrese, Andrew M.

1986-01-01

Describes development of system for retrieval of documents from magnetic storage that uses stored font definition codes to control an inexpensive laser printer in the production of copies that closely resemble original document. Trends in information equipment and printing industries that will govern future application of this technology are…

Inverted File Compression through Document Identifier Reassignment.

ERIC Educational Resources Information Center

Shieh, Wann-Yun; Chen, Tien-Fu; Shann, Jean Jyh-Jiun; Chung, Chung-Ping

2003-01-01

Discusses the use of inverted files in information retrieval systems and proposes a document identifier reassignment method to reduce the average gap values in an inverted file. Highlights include the d-gap technique; document similarity; heuristic algorithms; file compression; and performance evaluation from a simulation environment. (LRW)

A Novel Navigation Paradigm for XML Repositories.

ERIC Educational Resources Information Center

Azagury, Alain; Factor, Michael E.; Maarek, Yoelle S.; Mandler, Benny

2002-01-01

Discusses data exchange over the Internet and describes the architecture and implementation of an XML document repository that promotes a navigation paradigm for XML documents based on content and context. Topics include information retrieval and semistructured documents; and file systems as information storage infrastructure, particularly XMLFS.…

Organ donation in the ICU: A document analysis of institutional policies, protocols, and order sets.

PubMed

Oczkowski, Simon J W; Centofanti, John E; Durepos, Pamela; Arseneau, Erika; Kelecevic, Julija; Cook, Deborah J; Meade, Maureen O

2018-04-01

To better understand how local policies influence organ donation rates. We conducted a document analysis of our ICU organ donation policies, protocols and order sets. We used a systematic search of our institution's policy library to identify documents related to organ donation. We used Mindnode software to create a publication timeline, basic statistics to describe document characteristics, and qualitative content analysis to extract document themes. Documents were retrieved from Hamilton Health Sciences, an academic hospital system with a high volume of organ donation, from database inception to October 2015. We retrieved 12 active organ donation documents, including six protocols, two policies, two order sets, and two unclassified documents, a majority (75%) after the introduction of donation after circulatory death in 2006. Four major themes emerged: organ donation process, quality of care, patient and family-centred care, and the role of the institution. These themes indicate areas where documented institutional standards may be beneficial. Further research is necessary to determine the relationship of local policies, protocols, and order sets to actual organ donation practices, and to identify barriers and facilitators to improving donation rates. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Adaptation of reference volumes for correlation-based digital holographic particle tracking

NASA Astrophysics Data System (ADS)

Hesseling, Christina; Peinke, Joachim; Gülker, Gerd

2018-04-01

Numerically reconstructed reference volumes tailored to particle images are used for particle position detection by means of three-dimensional correlation. After a first tracking of these positions, the experimentally recorded particle images are retrieved as a posteriori knowledge about the particle images in the system. This knowledge is used for a further refinement of the detected positions. A transparent description of the individual algorithm steps including the results retrieved with experimental data complete the paper. The work employs extraordinarily small particles, smaller than the pixel pitch of the camera sensor. It is the first approach known to the authors that combines numerical knowledge about particle images and particle images retrieved from the experimental system to an iterative particle tracking approach for digital holographic particle tracking velocimetry.

Effortful Retrieval Reduces Hippocampal Activity and Impairs Incidental Encoding

PubMed Central

Reas, Emilie T.; Brewer, James B.

2014-01-01

Functional imaging studies frequently report that the hippocampus is engaged by successful episodic memory retrieval. However, considering that concurrent encoding of the background environment occurs during retrieval and influences medial temporal lobe activity, it is plausible that hippocampal encoding functions are reduced with increased attentional engagement during effortful retrieval. Expanding upon evidence that retrieval efforts suppress activity in hippocampal regions implicated in encoding, this study examines the influence of retrieval effort on encoding performance and the interactive effects of encoding and retrieval on hippocampal and neocortical activity. Functional magnetic resonance imaging was conducted while subjects performed a word recognition task with incidental picture encoding. Both lower memory strength and increased search duration were associated with encoding failure and reduced hippocampal and default network activity. Activity in the anterior hippocampus tracked encoding, which was more strongly deactivated when incidental encoding was unsuccessful. These findings highlight potential contributions from background encoding processes to hippocampal activations during neuroimaging studies of episodic memory retrieval. PMID:23378272

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments.

PubMed

García-Olalla, Oscar; Alegre, Enrique; Fernández-Robles, Laura; Fidalgo, Eduardo; Saikia, Surajit

2018-04-25

Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

PubMed Central

García-Olalla, Oscar; Saikia, Surajit

2018-01-01

Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments. PMID:29693590

Facing the phase problem in Coherent Diffractive Imaging via Memetic Algorithms.

PubMed

Colombo, Alessandro; Galli, Davide Emilio; De Caro, Liberato; Scattarella, Francesco; Carlino, Elvio

2017-02-09

Coherent Diffractive Imaging is a lensless technique that allows imaging of matter at a spatial resolution not limited by lens aberrations. This technique exploits the measured diffraction pattern of a coherent beam scattered by periodic and non-periodic objects to retrieve spatial information. The diffracted intensity, for weak-scattering objects, is proportional to the modulus of the Fourier Transform of the object scattering function. Any phase information, needed to retrieve its scattering function, has to be retrieved by means of suitable algorithms. Here we present a new approach, based on a memetic algorithm, i.e. a hybrid genetic algorithm, to face the phase problem, which exploits the synergy of deterministic and stochastic optimization methods. The new approach has been tested on simulated data and applied to the phasing of transmission electron microscopy coherent electron diffraction data of a SrTiO 3 sample. We have been able to quantitatively retrieve the projected atomic potential, and also image the oxygen columns, which are not directly visible in the relevant high-resolution transmission electron microscopy images. Our approach proves to be a new powerful tool for the study of matter at atomic resolution and opens new perspectives in those applications in which effective phase retrieval is necessary.

PACS-Graz, 1985-2000: from a scientific pilot to a state-wide multimedia radiological information system

NASA Astrophysics Data System (ADS)

Gell, Guenther

2000-05-01

1971/72 began the implementation of a computerized radiological documentation system as the Department of Radiology of the University of Graz, which developed over the years into a full RIS. 1985 started a scientific cooperation with SIEMENS to develop a PACS. The two systems were linked and evolved into a highly integrated RIS-PACS for the state wide hospital system in Styria. During its lifetime the RIS, originally implemented in FORTRAN on a UNIVAC 494 mainframe migrated to a PDP15, on to a PDP11, then VAX and Alphas. The flexible original record structure with variable length fields and the powerful retrieval language were retained. The data acquisition part with the user interface was rewritten several times and many service programs have been added. During our PACS cooperation many ideas like the folder concept or functionalities of the GUI have been designed and tested and were then implemented in the SIENET product. The actual RIS/PACS supports the whole workflow in the Radiology Department. It is installed in a 2.300 bed university hospital and the smaller hospitals of the State of Styria. Modalities from different vendors are connected via DICOM to the RIS (modality worklist) and to the PACS. PACSubsystems from other vendors have been integrated. Images are distributed to referring clinics and for teleconsultation and image processing and reports are available on line to all connected hospitals. We spent great efforts to guarantee optimal support of the workflow and to ensure an enhanced cost/benefit ratio for each user (class). Another special feature is selective image distribution. Using the high level retrieval language individual filters can be constructed easily to implement any image distribution policy agreed upon by radiologists and referring clinicians.

The effects of aging on emotion-induced modulations of source retrieval ERPs: evidence for valence biases.

PubMed

Newsome, Rachel N; Dulas, Michael R; Duarte, Audrey

2012-12-01

Many behavioral studies have shown that memory is enhanced for emotionally salient events across the lifespan. It has been suggested that this mnemonic boost may be observed for both age groups, particularly the old, in part because emotional information is retrieved with less effort than neutral information. Neuroimaging evidence suggests that inefficient retrieval processing (temporally delayed and attenuated) may contribute to age-related impairments in episodic memory for neutral events. It is not entirely clear whether emotional salience may reduce these age-related changes in neural activity associated with episodic retrieval for neutral events. Here, we investigated these ideas using event-related potentials (ERPs) to assess the neural correlates of successful source memory retrieval ("old-new effects") for neutral and emotional (negative and positive) images. Behavioral results showed that older adults demonstrated source memory impairments compared to the young but that both groups showed reduced source memory accuracy for negative compared to positive and neutral images; most likely due to an arousal-induced memory tradeoff for the negative images, which were subjectively more arousing than both positive and neutral images. ERP results showed that early onsetting old-new effects, between 100 and 300 ms, were observed for emotional but not neutral images in both age groups. Interestingly, these early effects were observed for negative items in the young and for positive items in the old. These ERP findings offer support for the idea that emotional events may be retrieved more automatically than neutral events across the lifespan. Furthermore, we suggest that very early retrieval mechanisms, possibly perceptual priming or familiarity, may underlie the negativity and positivity effects sometimes observed in the young and old, respectively, for various behavioral measures of attention and memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land

PubMed Central

Bassani, Cristiana; Cavalli, Rosa Maria; Pignatti, Stefano

2010-01-01

Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness. PMID:22163558

Image and information management system

NASA Technical Reports Server (NTRS)

Robertson, Tina L. (Inventor); Raney, Michael C. (Inventor); Dougherty, Dennis M. (Inventor); Kent, Peter C. (Inventor); Brucker, Russell X. (Inventor); Lampert, Daryl A. (Inventor)

2009-01-01

A system and methods through which pictorial views of an object's configuration, arranged in a hierarchical fashion, are navigated by a person to establish a visual context within the configuration. The visual context is automatically translated by the system into a set of search parameters driving retrieval of structured data and content (images, documents, multimedia, etc.) associated with the specific context. The system places ''hot spots'', or actionable regions, on various portions of the pictorials representing the object. When a user interacts with an actionable region, a more detailed pictorial from the hierarchy is presented representing that portion of the object, along with real-time feedback in the form of a popup pane containing information about that region, and counts-by-type reflecting the number of items that are available within the system associated with the specific context and search filters established at that point in time.

Image and information management system

NASA Technical Reports Server (NTRS)

Robertson, Tina L. (Inventor); Kent, Peter C. (Inventor); Raney, Michael C. (Inventor); Dougherty, Dennis M. (Inventor); Brucker, Russell X. (Inventor); Lampert, Daryl A. (Inventor)

2007-01-01

A system and methods through which pictorial views of an object's configuration, arranged in a hierarchical fashion, are navigated by a person to establish a visual context within the configuration. The visual context is automatically translated by the system into a set of search parameters driving retrieval of structured data and content (images, documents, multimedia, etc.) associated with the specific context. The system places hot spots, or actionable regions, on various portions of the pictorials representing the object. When a user interacts with an actionable region, a more detailed pictorial from the hierarchy is presented representing that portion of the object, along with real-time feedback in the form of a popup pane containing information about that region, and counts-by-type reflecting the number of items that are available within the system associated with the specific context and search filters established at that point in time.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

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

Brendel, Bernhard, E-mail: bernhard.brendel@philips.com; Teuffenbach, Maximilian von; Noël, Peter B.

2016-01-15

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penaltymore » comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.« less

Image standards in tissue-based diagnosis (diagnostic surgical pathology).

PubMed

Kayser, Klaus; Görtler, Jürgen; Goldmann, Torsten; Vollmer, Ekkehard; Hufnagl, Peter; Kayser, Gian

2008-04-18

Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. THEORY AND EXPERIENCES: Images used in tissue-based diagnosis present with pathology-specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease-image combination, human-diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image acquisition systems (resolution, colour temperature, focus, brightness, and quality evaluation procedures), display resolution data, implemented image formats, storage, cycle frequency, backup procedures, operation system, and external system accessibility. The lowest third level describes the permitted limits and threshold in detail. At present, an applicable standard including all mentioned features does not exist to our knowledge; some aspects can be taken from radiological standards (PACS, DICOM 3); others require specific solutions or are not covered yet. The progress in virtual microscopy and application of artificial intelligence (AI) in tissue-based diagnosis demands fast preparation and implementation of an internationally acceptable standard. The described hierarchic order as well as analytic investigation in all potentially necessary aspects and details offers an appropriate tool to specifically determine standardized requirements.