Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

PubMed Central

2017-01-01

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image. PMID:29403529

Multiple Representations-Based Face Sketch-Photo Synthesis.

PubMed

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Tao, Dacheng; Li, Xuelong; Li, Jie

2016-11-01

Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

Feature maps driven no-reference image quality prediction of authentically distorted images

NASA Astrophysics Data System (ADS)

Ghadiyaram, Deepti; Bovik, Alan C.

2015-03-01

Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.

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

PubMed Central

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

2010-01-01

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

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.

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.

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…

Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma

PubMed Central

Carey, George B; Kazantsev, Stephanie; Surati, Mosmi; Rolle, Cleo E; Kanteti, Archana; Sadiq, Ahad; Bahroos, Neil; Raumann, Brigitte; Madduri, Ravi; Dave, Paul; Starkey, Adam; Hensing, Thomas; Husain, Aliya N; Vokes, Everett E; Vigneswaran, Wickii; Armato, Samuel G; Kindler, Hedy L; Salgia, Ravi

2012-01-01

Objective An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation. Background We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database. Methods The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A. Results A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data. Discussion We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research. Conclusions The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger, more streamlined databases in the future. PMID:23103606

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.

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

PubMed

Khwaileh, Tariq; Body, Richard; Herbert, Ruth

2014-12-01

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

SparCLeS: dynamic l₁ sparse classifiers with level sets for robust beard/moustache detection and segmentation.

PubMed

Le, T Hoang Ngan; Luu, Khoa; Savvides, Marios

2013-08-01

Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database.

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

PubMed

Öhlschläger, Sabine; Võ, Melissa Le-Hoa

2017-10-01

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

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

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

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

Digital Dental X-ray Database for Caries Screening

NASA Astrophysics Data System (ADS)

Rad, Abdolvahab Ehsani; Rahim, Mohd Shafry Mohd; Rehman, Amjad; Saba, Tanzila

2016-06-01

Standard database is the essential requirement to compare the performance of image analysis techniques. Hence the main issue in dental image analysis is the lack of available image database which is provided in this paper. Periapical dental X-ray images which are suitable for any analysis and approved by many dental experts are collected. This type of dental radiograph imaging is common and inexpensive, which is normally used for dental disease diagnosis and abnormalities detection. Database contains 120 various Periapical X-ray images from top to bottom jaw. Dental digital database is constructed to provide the source for researchers to use and compare the image analysis techniques and improve or manipulate the performance of each technique.

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

PubMed

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

2005-01-01

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

An effective approach for iris recognition using phase-based image matching.

PubMed

Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

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.

Automated processing of shoeprint images based on the Fourier transform for use in forensic science.

PubMed

de Chazal, Philip; Flynn, John; Reilly, Richard B

2005-03-01

The development of a system for automatically sorting a database of shoeprint images based on the outsole pattern in response to a reference shoeprint image is presented. The database images are sorted so that those from the same pattern group as the reference shoeprint are likely to be at the start of the list. A database of 476 complete shoeprint images belonging to 140 pattern groups was established with each group containing two or more examples. A panel of human observers performed the grouping of the images into pattern categories. Tests of the system using the database showed that the first-ranked database image belongs to the same pattern category as the reference image 65 percent of the time and that a correct match appears within the first 5 percent of the sorted images 87 percent of the time. The system has translational and rotational invariance so that the spatial positioning of the reference shoeprint images does not have to correspond with the spatial positioning of the shoeprint images of the database. The performance of the system for matching partial-prints was also determined.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

LIPS database with LIPService: a microscopic image database of intracellular structures in Arabidopsis guard cells.

PubMed

Higaki, Takumi; Kutsuna, Natsumaro; Hasezawa, Seiichiro

2013-05-16

Intracellular configuration is an important feature of cell status. Recent advances in microscopic imaging techniques allow us to easily obtain a large number of microscopic images of intracellular structures. In this circumstance, automated microscopic image recognition techniques are of extreme importance to future phenomics/visible screening approaches. However, there was no benchmark microscopic image dataset for intracellular organelles in a specified plant cell type. We previously established the Live Images of Plant Stomata (LIPS) database, a publicly available collection of optical-section images of various intracellular structures of plant guard cells, as a model system of environmental signal perception and transduction. Here we report recent updates to the LIPS database and the establishment of a database table, LIPService. We updated the LIPS dataset and established a new interface named LIPService to promote efficient inspection of intracellular structure configurations. Cell nuclei, microtubules, actin microfilaments, mitochondria, chloroplasts, endoplasmic reticulum, peroxisomes, endosomes, Golgi bodies, and vacuoles can be filtered using probe names or morphometric parameters such as stomatal aperture. In addition to the serial optical sectional images of the original LIPS database, new volume-rendering data for easy web browsing of three-dimensional intracellular structures have been released to allow easy inspection of their configurations or relationships with cell status/morphology. We also demonstrated the utility of the new LIPS image database for automated organelle recognition of images from another plant cell image database with image clustering analyses. The updated LIPS database provides a benchmark image dataset for representative intracellular structures in Arabidopsis guard cells. The newly released LIPService allows users to inspect the relationship between organellar three-dimensional configurations and morphometrical parameters.

Towards online iris and periocular recognition under relaxed imaging constraints.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2013-10-01

Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.

Effective spatial database support for acquiring spatial information from remote sensing images

NASA Astrophysics Data System (ADS)

Jin, Peiquan; Wan, Shouhong; Yue, Lihua

2009-12-01

In this paper, a new approach to maintain spatial information acquiring from remote-sensing images is presented, which is based on Object-Relational DBMS. According to this approach, the detected and recognized results of targets are stored and able to be further accessed in an ORDBMS-based spatial database system, and users can access the spatial information using the standard SQL interface. This approach is different from the traditional ArcSDE-based method, because the spatial information management module is totally integrated into the DBMS and becomes one of the core modules in the DBMS. We focus on three issues, namely the general framework for the ORDBMS-based spatial database system, the definitions of the add-in spatial data types and operators, and the process to develop a spatial Datablade on Informix. The results show that the ORDBMS-based spatial database support for image-based target detecting and recognition is easy and practical to be implemented.

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

PubMed Central

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

2013-01-01

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

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Zamora, G.; Bauman, W.; Soliz, P.

2012-03-01

Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg, Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.

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.

Laptop Computer - Based Facial Recognition System Assessment

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

R. A. Cain; G. B. Singleton

2001-03-01

The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results.more » After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognition systems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognition system would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in remote locations. Remote users could perform real-time searches where network connectivity is not available. As images are enrolled at the remote locations, periodic database synchronization is necessary.« less

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

PubMed

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

2007-01-20

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

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

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

A User's Applications of Imaging Techniques: The University of Maryland Historic Textile Database.

ERIC Educational Resources Information Center

Anderson, Clarita S.

1991-01-01

Describes the incorporation of textile images into the University of Maryland Historic Textile Database by a computer user rather than a computer expert. Selection of a database management system is discussed, and PICTUREPOWER, a system that integrates photographic quality images with text and numeric information in databases, is described. (three…

Normative Databases for Imaging Instrumentation.

PubMed

Realini, Tony; Zangwill, Linda M; Flanagan, John G; Garway-Heath, David; Patella, Vincent M; Johnson, Chris A; Artes, Paul H; Gaddie, Ian B; Fingeret, Murray

2015-08-01

To describe the process by which imaging devices undergo reference database development and regulatory clearance. The limitations and potential improvements of reference (normative) data sets for ophthalmic imaging devices will be discussed. A symposium was held in July 2013 in which a series of speakers discussed issues related to the development of reference databases for imaging devices. Automated imaging has become widely accepted and used in glaucoma management. The ability of such instruments to discriminate healthy from glaucomatous optic nerves, and to detect glaucomatous progression over time is limited by the quality of reference databases associated with the available commercial devices. In the absence of standardized rules governing the development of reference databases, each manufacturer's database differs in size, eligibility criteria, and ethnic make-up, among other key features. The process for development of imaging reference databases may be improved by standardizing eligibility requirements and data collection protocols. Such standardization may also improve the degree to which results may be compared between commercial instruments.

Normative Databases for Imaging Instrumentation

PubMed Central

Realini, Tony; Zangwill, Linda; Flanagan, John; Garway-Heath, David; Patella, Vincent Michael; Johnson, Chris; Artes, Paul; Ben Gaddie, I.; Fingeret, Murray

2015-01-01

Purpose To describe the process by which imaging devices undergo reference database development and regulatory clearance. The limitations and potential improvements of reference (normative) data sets for ophthalmic imaging devices will be discussed. Methods A symposium was held in July 2013 in which a series of speakers discussed issues related to the development of reference databases for imaging devices. Results Automated imaging has become widely accepted and used in glaucoma management. The ability of such instruments to discriminate healthy from glaucomatous optic nerves, and to detect glaucomatous progression over time is limited by the quality of reference databases associated with the available commercial devices. In the absence of standardized rules governing the development of reference databases, each manufacturer’s database differs in size, eligibility criteria, and ethnic make-up, among other key features. Conclusions The process for development of imaging reference databases may be improved by standardizing eligibility requirements and data collection protocols. Such standardization may also improve the degree to which results may be compared between commercial instruments. PMID:25265003

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

PubMed

Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

2016-12-01

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

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

PubMed

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

2014-12-01

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

DR HAGIS-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients.

PubMed

Holm, Sven; Russell, Greg; Nourrit, Vincent; McLoughlin, Niall

2017-01-01

A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% ([Formula: see text]), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% ([Formula: see text]).

CHRONIS: an animal chromosome image database.

PubMed

Toyabe, Shin-Ichi; Akazawa, Kouhei; Fukushi, Daisuke; Fukui, Kiichi; Ushiki, Tatsuo

2005-01-01

We have constructed a database system named CHRONIS (CHROmosome and Nano-Information System) to collect images of animal chromosomes and related nanotechnological information. CHRONIS enables rapid sharing of information on chromosome research among cell biologists and researchers in other fields via the Internet. CHRONIS is also intended to serve as a liaison tool for researchers who work in different centers. The image database contains more than 3,000 color microscopic images, including karyotypic images obtained from more than 1,000 species of animals. Researchers can browse the contents of the database using a usual World Wide Web interface in the following URL: http://chromosome.med.niigata-u.ac.jp/chronis/servlet/chronisservlet. The system enables users to input new images into the database, to locate images of interest by keyword searches, and to display the images with detailed information. CHRONIS has a wide range of applications, such as searching for appropriate probes for fluorescent in situ hybridization, comparing various kinds of microscopic images of a single species, and finding researchers working in the same field of interest.

Content Based Image Matching for Planetary Science

NASA Astrophysics Data System (ADS)

Deans, M. C.; Meyer, C.

2006-12-01

Planetary missions generate large volumes of data. With the MER rovers still functioning on Mars, PDS contains over 7200 released images from the Microscopic Imagers alone. These data products are only searchable by keys such as the Sol, spacecraft clock, or rover motion counter index, with little connection to the semantic content of the images. We have developed a method for matching images based on the visual textures in images. For every image in a database, a series of filters compute the image response to localized frequencies and orientations. Filter responses are turned into a low dimensional descriptor vector, generating a 37 dimensional fingerprint. For images such as the MER MI, this represents a compression ratio of 99.9965% (the fingerprint is approximately 0.0035% the size of the original image). At query time, fingerprints are quickly matched to find images with similar appearance. Image databases containing several thousand images are preprocessed offline in a matter of hours. Image matches from the database are found in a matter of seconds. We have demonstrated this image matching technique using three sources of data. The first database consists of 7200 images from the MER Microscopic Imager. The second database consists of 3500 images from the Narrow Angle Mars Orbital Camera (MOC-NA), which were cropped into 1024×1024 sub-images for consistency. The third database consists of 7500 scanned archival photos from the Apollo Metric Camera. Example query results from all three data sources are shown. We have also carried out user tests to evaluate matching performance by hand labeling results. User tests verify approximately 20% false positive rate for the top 14 results for MOC NA and MER MI data. This means typically 10 to 12 results out of 14 match the query image sufficiently. This represents a powerful search tool for databases of thousands of images where the a priori match probability for an image might be less than 1%. Qualitatively, correct matches can also be confirmed by verifying MI images taken in the same z-stack, or MOC image tiles taken from the same image strip. False negatives are difficult to quantify as it would mean finding matches in the database of thousands of images that the algorithm did not detect.

Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems

PubMed Central

Wang, Kaibo; Huai, Yin; Lee, Rubao; Wang, Fusheng; Zhang, Xiaodong; Saltz, Joel H.

2012-01-01

As an important application of spatial databases in pathology imaging analysis, cross-comparing the spatial boundaries of a huge amount of segmented micro-anatomic objects demands extremely data- and compute-intensive operations, requiring high throughput at an affordable cost. However, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solution that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison in a cost-effective way. Our solution consists of an efficient GPU algorithm and a pipelined system framework with task migration support. Extensive experiments with real-world data sets demonstrate the effectiveness of our solution, which improves the performance of spatial cross-comparison by over 18 times compared with a parallelized spatial database approach. PMID:23355955

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

PubMed

Zhao, Jianwei; Lv, Yongbiao; Zhou, Zhenghua; Cao, Feilong

2017-10-01

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Morphology-based Query for Galaxy Image Databases

NASA Astrophysics Data System (ADS)

Shamir, Lior

2017-02-01

Galaxies of rare morphology are of paramount scientific interest, as they carry important information about the past, present, and future Universe. Once a rare galaxy is identified, studying it more effectively requires a set of galaxies of similar morphology, allowing generalization and statistical analysis that cannot be done when N=1. Databases generated by digital sky surveys can contain a very large number of galaxy images, and therefore once a rare galaxy of interest is identified it is possible that more instances of the same morphology are also present in the database. However, when a researcher identifies a certain galaxy of rare morphology in the database, it is virtually impossible to mine the database manually in the search for galaxies of similar morphology. Here we propose a computer method that can automatically search databases of galaxy images and identify galaxies that are morphologically similar to a certain user-defined query galaxy. That is, the researcher provides an image of a galaxy of interest, and the pattern recognition system automatically returns a list of galaxies that are visually similar to the target galaxy. The algorithm uses a comprehensive set of descriptors, allowing it to support different types of galaxies, and it is not limited to a finite set of known morphologies. While the list of returned galaxies is neither clean nor complete, it contains a far higher frequency of galaxies of the morphology of interest, providing a substantial reduction of the data. Such algorithms can be integrated into data management systems of autonomous digital sky surveys such as the Large Synoptic Survey Telescope (LSST), where the number of galaxies in the database is extremely large. The source code of the method is available at http://vfacstaff.ltu.edu/lshamir/downloads/udat.

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.

Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments

NASA Astrophysics Data System (ADS)

Karam, Lina J.; Zhu, Tong

2015-03-01

The varying quality of face images is an important challenge that limits the effectiveness of face recognition technology when applied in real-world applications. Existing face image databases do not consider the effect of distortions that commonly occur in real-world environments. This database (QLFW) represents an initial attempt to provide a set of labeled face images spanning the wide range of quality, from no perceived impairment to strong perceived impairment for face detection and face recognition applications. Types of impairment include JPEG2000 compression, JPEG compression, additive white noise, Gaussian blur and contrast change. Subjective experiments are conducted to assess the perceived visual quality of faces under different levels and types of distortions and also to assess the human recognition performance under the considered distortions. One goal of this work is to enable automated performance evaluation of face recognition technologies in the presence of different types and levels of visual distortions. This will consequently enable the development of face recognition systems that can operate reliably on real-world visual content in the presence of real-world visual distortions. Another goal is to enable the development and assessment of visual quality metrics for face images and for face detection and recognition applications.

Seeing is believing: on the use of image databases for visually exploring plant organelle dynamics.

PubMed

Mano, Shoji; Miwa, Tomoki; Nishikawa, Shuh-ichi; Mimura, Tetsuro; Nishimura, Mikio

2009-12-01

Organelle dynamics vary dramatically depending on cell type, developmental stage and environmental stimuli, so that various parameters, such as size, number and behavior, are required for the description of the dynamics of each organelle. Imaging techniques are superior to other techniques for describing organelle dynamics because these parameters are visually exhibited. Therefore, as the results can be seen immediately, investigators can more easily grasp organelle dynamics. At present, imaging techniques are emerging as fundamental tools in plant organelle research, and the development of new methodologies to visualize organelles and the improvement of analytical tools and equipment have allowed the large-scale generation of image and movie data. Accordingly, image databases that accumulate information on organelle dynamics are an increasingly indispensable part of modern plant organelle research. In addition, image databases are potentially rich data sources for computational analyses, as image and movie data reposited in the databases contain valuable and significant information, such as size, number, length and velocity. Computational analytical tools support image-based data mining, such as segmentation, quantification and statistical analyses, to extract biologically meaningful information from each database and combine them to construct models. In this review, we outline the image databases that are dedicated to plant organelle research and present their potential as resources for image-based computational analyses.

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.

Thermal Protection System Imagery Inspection Management System -TIIMS

NASA Technical Reports Server (NTRS)

Goza, Sharon; Melendrez, David L.; Henningan, Marsha; LaBasse, Daniel; Smith, Daniel J.

2011-01-01

TIIMS is used during the inspection phases of every mission to provide quick visual feedback, detailed inspection data, and determination to the mission management team. This system consists of a visual Web page interface, an SQL database, and a graphical image generator. These combine to allow a user to ascertain quickly the status of the inspection process, and current determination of any problem zones. The TIIMS system allows inspection engineers to enter their determinations into a database and to link pertinent images and video to those database entries. The database then assigns criteria to each zone and tile, and via query, sends the information to a graphical image generation program. Using the official TIPS database tile positions and sizes, the graphical image generation program creates images of the current status of the orbiter, coloring zones, and tiles based on a predefined key code. These images are then displayed on a Web page using customized JAVA scripts to display the appropriate zone of the orbiter based on the location of the user's cursor. The close-up graphic and database entry for that particular zone can then be seen by selecting the zone. This page contains links into the database to access the images used by the inspection engineer when they make the determination entered into the database. Status for the inspection zones changes as determinations are refined and shown by the appropriate color code.

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

PubMed

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

2005-12-01

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

A database for assessment of effect of lossy compression on digital mammograms

NASA Astrophysics Data System (ADS)

Wang, Jiheng; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2018-03-01

With widespread use of screening digital mammography, efficient storage of the vast amounts of data has become a challenge. While lossless image compression causes no risk to the interpretation of the data, it does not allow for high compression rates. Lossy compression and the associated higher compression ratios are therefore more desirable. The U.S. Food and Drug Administration (FDA) currently interprets the Mammography Quality Standards Act as prohibiting lossy compression of digital mammograms for primary image interpretation, image retention, or transfer to the patient or her designated recipient. Previous work has used reader studies to determine proper usage criteria for evaluating lossy image compression in mammography, and utilized different measures and metrics to characterize medical image quality. The drawback of such studies is that they rely on a threshold on compression ratio as the fundamental criterion for preserving the quality of images. However, compression ratio is not a useful indicator of image quality. On the other hand, many objective image quality metrics (IQMs) have shown excellent performance for natural image content for consumer electronic applications. In this paper, we create a new synthetic mammogram database with several unique features. We compare and characterize the impact of image compression on several clinically relevant image attributes such as perceived contrast and mass appearance for different kinds of masses. We plan to use this database to develop a new objective IQM for measuring the quality of compressed mammographic images to help determine the allowed maximum compression for different kinds of breasts and masses in terms of visual and diagnostic quality.

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

PubMed

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

2011-01-01

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

Interhospital network system using the worldwide web and the common gateway interface.

PubMed

Oka, A; Harima, Y; Nakano, Y; Tanaka, Y; Watanabe, A; Kihara, H; Sawada, S

1999-05-01

We constructed an interhospital network system using the worldwide web (WWW) and the Common Gateway Interface (CGI). Original clinical images are digitized and stored as a database for educational and research purposes. Personal computers (PCs) are available for data treatment and browsing. Our system is simple, as digitized images are stored into a Unix server machine. Images of important and interesting clinical cases are selected and registered into the image database using CGI. The main image format is 8- or 12-bit Joint Photographic Experts Group (JPEG) image. Original clinical images are finally stored in CD-ROM using a CD recorder. The image viewer can browse all of the images for one case at once as thumbnail pictures; image quality can be selected depending on the user's purpose. Using the network system, clinical images of interesting cases can be rapidly transmitted and discussed with other related hospitals. Data transmission from relational hospitals takes 1 to 2 minutes per 500 Kbyte of data. More distant hospitals (e.g., Rakusai Hospital, Kyoto) takes 1 minute more. The mean number of accesses our image database in a recent 3-month period was 470. There is a total about 200 cases in our image database, acquired over the past 2 years. Our system is useful for communication and image treatment between hospitals and we will describe the elements of our system and image database.

Mass-storage management for distributed image/video archives

NASA Astrophysics Data System (ADS)

Franchi, Santina; Guarda, Roberto; Prampolini, Franco

1993-04-01

The realization of image/video database requires a specific design for both database structures and mass storage management. This issue has addressed the project of the digital image/video database system that has been designed at IBM SEMEA Scientific & Technical Solution Center. Proper database structures have been defined to catalog image/video coding technique with the related parameters, and the description of image/video contents. User workstations and servers are distributed along a local area network. Image/video files are not managed directly by the DBMS server. Because of their wide size, they are stored outside the database on network devices. The database contains the pointers to the image/video files and the description of the storage devices. The system can use different kinds of storage media, organized in a hierarchical structure. Three levels of functions are available to manage the storage resources. The functions of the lower level provide media management. They allow it to catalog devices and to modify device status and device network location. The medium level manages image/video files on a physical basis. It manages file migration between high capacity media and low access time media. The functions of the upper level work on image/video file on a logical basis, as they archive, move and copy image/video data selected by user defined queries. These functions are used to support the implementation of a storage management strategy. The database information about characteristics of both storage devices and coding techniques are used by the third level functions to fit delivery/visualization requirements and to reduce archiving costs.

Image Format Conversion to DICOM and Lookup Table Conversion to Presentation Value of the Japanese Society of Radiological Technology (JSRT) Standard Digital Image Database.

PubMed

Yanagita, Satoshi; Imahana, Masato; Suwa, Kazuaki; Sugimura, Hitomi; Nishiki, Masayuki

2016-01-01

Japanese Society of Radiological Technology (JSRT) standard digital image database contains many useful cases of chest X-ray images, and has been used in many state-of-the-art researches. However, the pixel values of all the images are simply digitized as relative density values by utilizing a scanned film digitizer. As a result, the pixel values are completely different from the standardized display system input value of digital imaging and communications in medicine (DICOM), called presentation value (P-value), which can maintain a visual consistency when observing images using different display luminance. Therefore, we converted all the images from JSRT standard digital image database to DICOM format followed by the conversion of the pixel values to P-value using an original program developed by ourselves. Consequently, JSRT standard digital image database has been modified so that the visual consistency of images is maintained among different luminance displays.

Enhancing facial features by using clear facial features

NASA Astrophysics Data System (ADS)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Development and Implementation of Kumamoto Technopolis Regional Database T-KIND

NASA Astrophysics Data System (ADS)

Onoue, Noriaki

T-KIND (Techno-Kumamoto Information Network for Data-Base) is a system for effectively searching information of technology, human resources and industries which are necessary to realize Kumamoto Technopolis. It is composed of coded database, image database and LAN inside technoresearch park which is the center of R & D in the Technopolis. It constructs on-line system by networking general-purposed computers, minicomputers, optical disk file systems and so on, and provides the service through public telephone line. Two databases are now available on enterprise information and human resource information. The former covers about 4,000 enterprises, and the latter does about 2,000 persons.

Learning to rank for blind image quality assessment.

PubMed

Gao, Fei; Tao, Dacheng; Gao, Xinbo; Li, Xuelong

2015-10-01

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image Ia is better than that of image Ib for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

Multi-Sensor Scene Synthesis and Analysis

DTIC Science & Technology

1981-09-01

Quad Trees for Image Representation and Processing ...... ... 126 2.6.2 Databases ..... ..... ... ..... ... ..... ..... 138 2.6.2.1 Definitions and...Basic Concepts ....... 138 2.6.3 Use of Databases in Hierarchical Scene Analysis ...... ... ..................... 147 2.6.4 Use of Relational Tables...Multisensor Image Database Systems (MIDAS) . 161 2.7.2 Relational Database System for Pictures .... ..... 168 2.7.3 Relational Pictorial Database

Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

PubMed

Schlaeger, Sarah; Freitag, Friedemann; Klupp, Elisabeth; Dieckmeyer, Michael; Weidlich, Dominik; Inhuber, Stephanie; Deschauer, Marcus; Schoser, Benedikt; Bublitz, Sarah; Montagnese, Federica; Zimmer, Claus; Rummeny, Ernst J; Karampinos, Dimitrios C; Kirschke, Jan S; Baum, Thomas

2018-01-01

Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

Application of furniture images selection based on neural network

NASA Astrophysics Data System (ADS)

Wang, Yong; Gao, Wenwen; Wang, Ying

2018-05-01

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

Online molecular image repository and analysis system: A multicenter collaborative open-source infrastructure for molecular imaging research and application.

PubMed

Rahman, Mahabubur; Watabe, Hiroshi

2018-05-01

Molecular imaging serves as an important tool for researchers and clinicians to visualize and investigate complex biochemical phenomena using specialized instruments; these instruments are either used individually or in combination with targeted imaging agents to obtain images related to specific diseases with high sensitivity, specificity, and signal-to-noise ratios. However, molecular imaging, which is a multidisciplinary research field, faces several challenges, including the integration of imaging informatics with bioinformatics and medical informatics, requirement of reliable and robust image analysis algorithms, effective quality control of imaging facilities, and those related to individualized disease mapping, data sharing, software architecture, and knowledge management. As a cost-effective and open-source approach to address these challenges related to molecular imaging, we develop a flexible, transparent, and secure infrastructure, named MIRA, which stands for Molecular Imaging Repository and Analysis, primarily using the Python programming language, and a MySQL relational database system deployed on a Linux server. MIRA is designed with a centralized image archiving infrastructure and information database so that a multicenter collaborative informatics platform can be built. The capability of dealing with metadata, image file format normalization, and storing and viewing different types of documents and multimedia files make MIRA considerably flexible. With features like logging, auditing, commenting, sharing, and searching, MIRA is useful as an Electronic Laboratory Notebook for effective knowledge management. In addition, the centralized approach for MIRA facilitates on-the-fly access to all its features remotely through any web browser. Furthermore, the open-source approach provides the opportunity for sustainable continued development. MIRA offers an infrastructure that can be used as cross-boundary collaborative MI research platform for the rapid achievement in cancer diagnosis and therapeutics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD).

PubMed

Jiang, Xiangying; Ringwald, Martin; Blake, Judith; Shatkay, Hagit

2017-01-01

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. www.informatics.jax.org. © The Author(s) 2017. Published by Oxford University Press.

Image query and indexing for digital x rays

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Thoma, George R.

1998-12-01

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

Toward a standard reference database for computer-aided mammography

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

Employing image processing techniques for cancer detection using microarray images.

PubMed

Dehghan Khalilabad, Nastaran; Hassanpour, Hamid

2017-02-01

Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance.

PubMed

Yuan, Yading; Chao, Ming; Lo, Yeh-Chi

2017-09-01

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

Construction of image database for newspapaer articles using CTS

NASA Astrophysics Data System (ADS)

Kamio, Tatsuo

Nihon Keizai Shimbun, Inc. developed a system of making articles' image database automatically by use of CTS (Computer Typesetting System). Besides the articles and the headlines inputted in CTS, it reproduces the image of elements of such as photography and graphs by article in accordance with information of position on the paper. So to speak, computer itself clips the articles out of the newspaper. Image database is accumulated in magnetic file and optical file and is output to the facsimile of users. With diffusion of CTS, newspaper companies which start to have structure of articles database are increased rapidly, the said system is the first attempt to make database automatically. This paper describes the device of CTS which supports this system and outline.

Molecular Imaging and Contrast Agent Database (MICAD): evolution and progress.

PubMed

Chopra, Arvind; Shan, Liang; Eckelman, W C; Leung, Kam; Latterner, Martin; Bryant, Stephen H; Menkens, Anne

2012-02-01

The purpose of writing this review is to showcase the Molecular Imaging and Contrast Agent Database (MICAD; www.micad.nlm.nih.gov ) to students, researchers, and clinical investigators interested in the different aspects of molecular imaging. This database provides freely accessible, current, online scientific information regarding molecular imaging (MI) probes and contrast agents (CA) used for positron emission tomography, single-photon emission computed tomography, magnetic resonance imaging, X-ray/computed tomography, optical imaging and ultrasound imaging. Detailed information on >1,000 agents in MICAD is provided in a chapter format and can be accessed through PubMed. Lists containing >4,250 unique MI probes and CAs published in peer-reviewed journals and agents approved by the United States Food and Drug Administration as well as a comma separated values file summarizing all chapters in the database can be downloaded from the MICAD homepage. Users can search for agents in MICAD on the basis of imaging modality, source of signal/contrast, agent or target category, pre-clinical or clinical studies, and text words. Chapters in MICAD describe the chemical characteristics (structures linked to PubChem), the in vitro and in vivo activities, and other relevant information regarding an imaging agent. All references in the chapters have links to PubMed. A Supplemental Information Section in each chapter is available to share unpublished information regarding an agent. A Guest Author Program is available to facilitate rapid expansion of the database. Members of the imaging community registered with MICAD periodically receive an e-mail announcement (eAnnouncement) that lists new chapters uploaded to the database. Users of MICAD are encouraged to provide feedback, comments, or suggestions for further improvement of the database by writing to the editors at micad@nlm.nih.gov.

Molecular Imaging and Contrast Agent Database (MICAD): Evolution and Progress

PubMed Central

Chopra, Arvind; Shan, Liang; Eckelman, W. C.; Leung, Kam; Latterner, Martin; Bryant, Stephen H.; Menkens, Anne

2011-01-01

The purpose of writing this review is to showcase the Molecular Imaging and Contrast Agent Database (MICAD; www.micad.nlm.nih.gov) to students, researchers and clinical investigators interested in the different aspects of molecular imaging. This database provides freely accessible, current, online scientific information regarding molecular imaging (MI) probes and contrast agents (CA) used for positron emission tomography, single-photon emission computed tomography, magnetic resonance imaging, x-ray/computed tomography, optical imaging and ultrasound imaging. Detailed information on >1000 agents in MICAD is provided in a chapter format and can be accessed through PubMed. Lists containing >4250 unique MI probes and CAs published in peer-reviewed journals and agents approved by the United States Food and Drug Administration (FDA) as well as a CSV file summarizing all chapters in the database can be downloaded from the MICAD homepage. Users can search for agents in MICAD on the basis of imaging modality, source of signal/contrast, agent or target category, preclinical or clinical studies, and text words. Chapters in MICAD describe the chemical characteristics (structures linked to PubChem), the in vitro and in vivo activities and other relevant information regarding an imaging agent. All references in the chapters have links to PubMed. A Supplemental Information Section in each chapter is available to share unpublished information regarding an agent. A Guest Author Program is available to facilitate rapid expansion of the database. Members of the imaging community registered with MICAD periodically receive an e-mail announcement (eAnnouncement) that lists new chapters uploaded to the database. Users of MICAD are encouraged to provide feedback, comments or suggestions for further improvement of the database by writing to the editors at: micad@nlm.nih.gov PMID:21989943

Structured Forms Reference Set of Binary Images (SFRS)

National Institute of Standards and Technology Data Gateway

NIST Structured Forms Reference Set of Binary Images (SFRS) (Web, free access)   The NIST Structured Forms Database (Special Database 2) consists of 5,590 pages of binary, black-and-white images of synthesized documents. The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

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

NONE

2011-02-15

Purpose: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.more » Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (''nodule{>=}3 mm,''''nodule<3 mm,'' and ''non-nodule{>=}3 mm''). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results: The Database contains 7371 lesions marked ''nodule'' by at least one radiologist. 2669 of these lesions were marked ''nodule{>=}3 mm'' by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.« less

The UBIRIS.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance.

PubMed

Proença, Hugo; Filipe, Sílvio; Santos, Ricardo; Oliveira, João; Alexandre, Luís A

2010-08-01

The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors

PubMed Central

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-01-01

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods. PMID:28587269

Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

PubMed

Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

2017-06-06

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

Effects of task and image properties on visual-attention deployment in image-quality assessment

NASA Astrophysics Data System (ADS)

Alers, Hani; Redi, Judith; Liu, Hantao; Heynderickx, Ingrid

2015-03-01

It is important to understand how humans view images and how their behavior is affected by changes in the properties of the viewed images and the task they are given, particularly the task of scoring the image quality (IQ). This is a complex behavior that holds great importance for the field of image-quality research. This work builds upon 4 years of research work spanning three databases studying image-viewing behavior. Using eye-tracking equipment, it was possible to collect information on human viewing behavior of different kinds of stimuli and under different experimental settings. This work performs a cross-analysis on the results from all these databases using state-of-the-art similarity measures. The results strongly show that asking the viewers to score the IQ significantly changes their viewing behavior. Also muting the color saturation seems to affect the saliency of the images. However, a change in IQ was not consistently found to modify visual attention deployment, neither under free looking nor during scoring. These results are helpful in gaining a better understanding of image viewing behavior under different conditions. They also have important implications on work that collects subjective image-quality scores from human observers.

A database for spectral image quality

NASA Astrophysics Data System (ADS)

Le Moan, Steven; George, Sony; Pedersen, Marius; Blahová, Jana; Hardeberg, Jon Yngve

2015-01-01

We introduce a new image database dedicated to multi-/hyperspectral image quality assessment. A total of nine scenes representing pseudo-at surfaces of different materials (textile, wood, skin. . . ) were captured by means of a 160 band hyperspectral system with a spectral range between 410 and 1000nm. Five spectral distortions were designed, applied to the spectral images and subsequently compared in a psychometric experiment, in order to provide a basis for applications such as the evaluation of spectral image difference measures. The database can be downloaded freely from http://www.colourlab.no/cid.

Document image database indexing with pictorial dictionary

NASA Astrophysics Data System (ADS)

Akbari, Mohammad; Azimi, Reza

2010-02-01

In this paper we introduce a new approach for information retrieval from Persian document image database without using Optical Character Recognition (OCR).At first an attribute called subword upper contour label is defined then, a pictorial dictionary is constructed based on this attribute for the subwords. By this approach we address two issues in document image retrieval: keyword spotting and retrieval according to the document similarities. The proposed methods have been evaluated on a Persian document image database. The results have proved the ability of this approach in document image information retrieval.

Medical Image Databases

PubMed Central

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

1997-01-01

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

The LISS--a public database of common imaging signs of lung diseases for computer-aided detection and diagnosis research and medical education.

PubMed

Han, Guanghui; Liu, Xiabi; Han, Feifei; Santika, I Nyoman Tenaya; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2015-02-01

Lung computed tomography (CT) imaging signs play important roles in the diagnosis of lung diseases. In this paper, we review the significance of CT imaging signs in disease diagnosis and determine the inclusion criterion of CT scans and CT imaging signs of our database. We develop the software of abnormal regions annotation and design the storage scheme of CT images and annotation data. Then, we present a publicly available database of lung CT imaging signs, called LISS for short, which contains 271 CT scans and 677 abnormal regions in them. The 677 abnormal regions are divided into nine categories of common CT imaging signs of lung disease (CISLs). The ground truth of these CISLs regions and the corresponding categories are provided. Furthermore, to make the database publicly available, all private data in CT scans are eliminated or replaced with provisioned values. The main characteristic of our LISS database is that it is developed from a new perspective of CT imaging signs of lung diseases instead of commonly considered lung nodules. Thus, it is promising to apply to computer-aided detection and diagnosis research and medical education.

Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.

PubMed

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

2016-12-01

Lung cancer is the leading cause of cancer-related deaths in the world, and its main manifestation is pulmonary nodules. Detection and classification of pulmonary nodules are challenging tasks that must be done by qualified specialists, but image interpretation errors make those tasks difficult. In order to aid radiologists on those hard tasks, it is important to integrate the computer-based tools with the lesion detection, pathology diagnosis, and image interpretation processes. However, computer-aided diagnosis research faces the problem of not having enough shared medical reference data for the development, testing, and evaluation of computational methods for diagnosis. In order to minimize this problem, this paper presents a public nonrelational document-oriented cloud-based database of pulmonary nodules characterized by 3D texture attributes, identified by experienced radiologists and classified in nine different subjective characteristics by the same specialists. Our goal with the development of this database is to improve computer-aided lung cancer diagnosis and pulmonary nodule detection and classification research through the deployment of this database in a cloud Database as a Service framework. Pulmonary nodule data was provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), image descriptors were acquired by a volumetric texture analysis, and database schema was developed using a document-oriented Not only Structured Query Language (NoSQL) approach. The proposed database is now with 379 exams, 838 nodules, and 8237 images, 4029 of them are CT scans and 4208 manually segmented nodules, and it is allocated in a MongoDB instance on a cloud infrastructure.

Face detection on distorted images using perceptual quality-aware features

NASA Astrophysics Data System (ADS)

Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

2014-02-01

We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

European multicentre database of healthy controls for [123I]FP-CIT SPECT (ENC-DAT): age-related effects, gender differences and evaluation of different methods of analysis.

PubMed

Varrone, Andrea; Dickson, John C; Tossici-Bolt, Livia; Sera, Terez; Asenbaum, Susanne; Booij, Jan; Kapucu, Ozlem L; Kluge, Andreas; Knudsen, Gitte M; Koulibaly, Pierre Malick; Nobili, Flavio; Pagani, Marco; Sabri, Osama; Vander Borght, Thierry; Van Laere, Koen; Tatsch, Klaus

2013-01-01

Dopamine transporter (DAT) imaging with [(123)I]FP-CIT (DaTSCAN) is an established diagnostic tool in parkinsonism and dementia. Although qualitative assessment criteria are available, DAT quantification is important for research and for completion of a diagnostic evaluation. One critical aspect of quantification is the availability of normative data, considering possible age and gender effects on DAT availability. The aim of the European Normal Control Database of DaTSCAN (ENC-DAT) study was to generate a large database of [(123)I]FP-CIT SPECT scans in healthy controls. SPECT data from 139 healthy controls (74 men, 65 women; age range 20-83 years, mean 53 years) acquired in 13 different centres were included. Images were reconstructed using the ordered-subset expectation-maximization algorithm without correction (NOACSC), with attenuation correction (AC), and with both attenuation and scatter correction using the triple-energy window method (ACSC). Region-of-interest analysis was performed using the BRASS software (caudate and putamen), and the Southampton method (striatum). The outcome measure was the specific binding ratio (SBR). A significant effect of age on SBR was found for all data. Gender had a significant effect on SBR in the caudate and putamen for the NOACSC and AC data, and only in the left caudate for the ACSC data (BRASS method). Significant effects of age and gender on striatal SBR were observed for all data analysed with the Southampton method. Overall, there was a significant age-related decline in SBR of between 4 % and 6.7 % per decade. This study provides a large database of [(123)I]FP-CIT SPECT scans in healthy controls across a wide age range and with balanced gender representation. Higher DAT availability was found in women than in men. An average age-related decline in DAT availability of 5.5 % per decade was found for both genders, in agreement with previous reports. The data collected in this study may serve as a reference database for nuclear medicine centres and for clinical trials using [(123)I]FP-CIT SPECT as the imaging marker.

Tchebichef moment based restoration of Gaussian blurred images.

PubMed

Kumar, Ahlad; Paramesran, Raveendran; Lim, Chern-Loon; Dass, Sarat C

2016-11-10

With the knowledge of how edges vary in the presence of a Gaussian blur, a method that uses low-order Tchebichef moments is proposed to estimate the blur parameters: sigma (σ) and size (w). The difference between the Tchebichef moments of the original and the reblurred images is used as feature vectors to train an extreme learning machine for estimating the blur parameters (σ,w). The effectiveness of the proposed method to estimate the blur parameters is examined using cross-database validation. The estimated blur parameters from the proposed method are used in the split Bregman-based image restoration algorithm. A comparative analysis of the proposed method with three existing methods using all the images from the LIVE database is carried out. The results show that the proposed method in most of the cases performs better than the three existing methods in terms of the visual quality evaluated using the structural similarity index.

DISCRIMINATION OF GRANITOIDS AND MINERALIZED GRANITOIDS IN THE MIDYAN REGION, NORTHWESTERN ARABIAN SHIELD, SAUDI ARABIA, BY LANDSAT MSS DATA-ANALYSIS.

USGS Publications Warehouse

Davis, Philip A.; Grolier, Maurice J.

1984-01-01

Landsat multispectral scanner (MSS) band and band-ratio databases of two scenes covering the Midyan region of northwestern Saudi Arabia were examined quantitatively and qualitatively to determine which databases best discriminate the geologic units of this semi-arid and arid region. Unsupervised, linear-discriminant cluster-analysis was performed on these two band-ratio combinations and on the MSS bands for both scenes. The results for granitoid-rock discrimination indicated that the classification images using the MSS bands are superior to the band-ratio classification images for two reasons, discussed in the paper. Yet, the effects of topography and material type (including desert varnish) on the MSS-band data produced ambiguities in the MSS-band classification results. However, these ambiguities were clarified by using a simulated natural-color image in conjunction with the MSS-band classification image.

Open source database of images DEIMOS: extension for large-scale subjective image quality assessment

NASA Astrophysics Data System (ADS)

Vítek, Stanislav

2014-09-01

DEIMOS (Database of Images: Open Source) is an open-source database of images and video sequences for testing, verification and comparison of various image and/or video processing techniques such as compression, reconstruction and enhancement. This paper deals with extension of the database allowing performing large-scale web-based subjective image quality assessment. Extension implements both administrative and client interface. The proposed system is aimed mainly at mobile communication devices, taking into account advantages of HTML5 technology; it means that participants don't need to install any application and assessment could be performed using web browser. The assessment campaign administrator can select images from the large database and then apply rules defined by various test procedure recommendations. The standard test procedures may be fully customized and saved as a template. Alternatively the administrator can define a custom test, using images from the pool and other components, such as evaluating forms and ongoing questionnaires. Image sequence is delivered to the online client, e.g. smartphone or tablet, as a fully automated assessment sequence or viewer can decide on timing of the assessment if required. Environmental data and viewing conditions (e.g. illumination, vibrations, GPS coordinates, etc.), may be collected and subsequently analyzed.

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

Active browsing using similarity pyramids

NASA Astrophysics Data System (ADS)

Chen, Jau-Yuen; Bouman, Charles A.; Dalton, John C.

1998-12-01

In this paper, we describe a new approach to managing large image databases, which we call active browsing. Active browsing integrates relevance feedback into the browsing environment, so that users can modify the database's organization to suit the desired task. Our method is based on a similarity pyramid data structure, which hierarchically organizes the database, so that it can be efficiently browsed. At coarse levels, the similarity pyramid allows users to view the database as large clusters of similar images. Alternatively, users can 'zoom into' finer levels to view individual images. We discuss relevance feedback for the browsing process, and argue that it is fundamentally different from relevance feedback for more traditional search-by-query tasks. We propose two fundamental operations for active browsing: pruning and reorganization. Both of these operations depend on a user-defined relevance set, which represents the image or set of images desired by the user. We present statistical methods for accurately pruning the database, and we propose a new 'worm hole' distance metric for reorganizing the database, so that members of the relevance set are grouped together.

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

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

2000-12-01

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

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

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

2001-01-01

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

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.

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

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

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

Trading efficiency for effectiveness in similarity-based indexing for image databases

NASA Astrophysics Data System (ADS)

Barros, Julio E.; French, James C.; Martin, Worthy N.; Kelly, Patrick M.

1995-11-01

Image databases typically manage feature data that can be viewed as points in a feature space. Some features, however, can be better expressed as a collection of points or described by a probability distribution function (PDF) rather than as a single point. In earlier work we introduced a similarity measure and a method for indexing and searching the PDF descriptions of these items that guarantees an answer equivalent to sequential search. Unfortunately, certain properties of the data can restrict the efficiency of that method. In this paper we extend that work and examine trade-offs between efficiency and answer quality or effectiveness. These trade-offs reduce the amount of work required during a search by reducing the number of undesired items fetched without excluding an excessive number of the desired ones.

Structured Forms Reference Set of Binary Images II (SFRS2)

National Institute of Standards and Technology Data Gateway

NIST Structured Forms Reference Set of Binary Images II (SFRS2) (Web, free access)   The second NIST database of structured forms (Special Database 6) consists of 5,595 pages of binary, black-and-white images of synthesized documents containing hand-print. The documents in this database are 12 different tax forms with the IRS 1040 Package X for the year 1988.

ClearedLeavesDB: an online database of cleared plant leaf images

PubMed Central

2014-01-01

Background Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers. Description The Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database. Conclusions We developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface allows storage of large collections and integrates with leaf image analysis applications via an open application programming interface (API). The open API allows uploading of processed images and other trait data to the database, further enabling distribution and documentation of analyzed data within the community. The initial database is seeded with nearly 19,000 cleared leaf images representing over 40 GB of image data. Extensible storage and growth of the database is ensured by using the data storage resources of the iPlant Discovery Environment. ClearedLeavesDB can be accessed at http://clearedleavesdb.org. PMID:24678985

ClearedLeavesDB: an online database of cleared plant leaf images.

PubMed

Das, Abhiram; Bucksch, Alexander; Price, Charles A; Weitz, Joshua S

2014-03-28

Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers. The Cleared Leaf Image Database (ClearedLeavesDB), is an online web-based resource for a community of researchers to contribute, access and share cleared leaf images. ClearedLeavesDB leverages resources of large-scale, curated collections while enabling the aggregation of small-scale collections within the same online platform. ClearedLeavesDB is built on Drupal, an open source content management platform. It allows plant biologists to store leaf images online with corresponding meta-data, share image collections with a user community and discuss images and collections via a common forum. We provide tools to upload processed images and results to the database via a web services client application that can be downloaded from the database. We developed ClearedLeavesDB, a database focusing on cleared leaf images that combines interactions between users and data via an intuitive web interface. The web interface allows storage of large collections and integrates with leaf image analysis applications via an open application programming interface (API). The open API allows uploading of processed images and other trait data to the database, further enabling distribution and documentation of analyzed data within the community. The initial database is seeded with nearly 19,000 cleared leaf images representing over 40 GB of image data. Extensible storage and growth of the database is ensured by using the data storage resources of the iPlant Discovery Environment. ClearedLeavesDB can be accessed at http://clearedleavesdb.org.

Learning Computational Models of Video Memorability from fMRI Brain Imaging.

PubMed

Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

2015-08-01

Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

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.

[LONI & Co: about the epistemic specificity of digital spaces of knowledge in cognitive neuroscience].

PubMed

Huber, Lara

2011-06-01

In the neurosciences digital databases more and more are becoming important tools of data rendering and distributing. This development is due to the growing impact of imaging based trial design in cognitive neuroscience, including morphological as much as functional imaging technologies. As the case of the 'Laboratory of Neuro Imaging' (LONI) is showing, databases are attributed a specific epistemological power: Since the 1990s databasing is seen to foster the integration of neuroscientific data, although local regimes of data production, -manipulation and--interpretation are also challenging this development. Databasing in the neurosciences goes along with the introduction of new structures of integrating local data, hence establishing digital spaces of knowledge (epistemic spaces): At this stage, inherent norms of digital databases are affecting regimes of imaging-based trial design, for example clinical research into Alzheimer's disease.

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

PubMed

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

2015-06-01

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

DeitY-TU face database: its design, multiple camera capturing, characteristics, and evaluation

NASA Astrophysics Data System (ADS)

Bhowmik, Mrinal Kanti; Saha, Kankan; Saha, Priya; Bhattacharjee, Debotosh

2014-10-01

The development of the latest face databases is providing researchers different and realistic problems that play an important role in the development of efficient algorithms for solving the difficulties during automatic recognition of human faces. This paper presents the creation of a new visual face database, named the Department of Electronics and Information Technology-Tripura University (DeitY-TU) face database. It contains face images of 524 persons belonging to different nontribes and Mongolian tribes of north-east India, with their anthropometric measurements for identification. Database images are captured within a room with controlled variations in illumination, expression, and pose along with variability in age, gender, accessories, make-up, and partial occlusion. Each image contains the combined primary challenges of face recognition, i.e., illumination, expression, and pose. This database also represents some new features: soft biometric traits such as mole, freckle, scar, etc., and facial anthropometric variations that may be helpful for researchers for biometric recognition. It also gives an equivalent study of the existing two-dimensional face image databases. The database has been tested using two baseline algorithms: linear discriminant analysis and principal component analysis, which may be used by other researchers as the control algorithm performance score.

Image matching algorithms for breech face marks and firing pins in a database of spent cartridge cases of firearms.

PubMed

Geradts, Z J; Bijhold, J; Hermsen, R; Murtagh, F

2001-06-01

On the market several systems exist for collecting spent ammunition data for forensic investigation. These databases store images of cartridge cases and the marks on them. Image matching is used to create hit lists that show which marks on a cartridge case are most similar to another cartridge case. The research in this paper is focused on the different methods of feature selection and pattern recognition that can be used for optimizing the results of image matching. The images are acquired by side light images for the breech face marks and by ring light for the firing pin impression. For these images a standard way of digitizing the images used. For the side light images and ring light images this means that the user has to position the cartridge case in the same position according to a protocol. The positioning is important for the sidelight, since the image that is obtained of a striation mark depends heavily on the angle of incidence of the light. In practice, it appears that the user positions the cartridge case with +/-10 degrees accuracy. We tested our algorithms using 49 cartridge cases of 19 different firearms, where the examiner determined that they were shot with the same firearm. For testing, these images were mixed with a database consisting of approximately 4900 images that were available from the Drugfire database of different calibers.In cases where the registration and the light conditions among those matching pairs was good, a simple computation of the standard deviation of the subtracted gray levels, delivered the best-matched images. For images that were rotated and shifted, we have implemented a "brute force" way of registration. The images are translated and rotated until the minimum of the standard deviation of the difference is found. This method did not result in all relevant matches in the top position. This is caused by the effect that shadows and highlights are compared in intensity. Since the angle of incidence of the light will give a different intensity profile, this method is not optimal. For this reason a preprocessing of the images was required. It appeared that the third scale of the "à trous" wavelet transform gives the best results in combination with brute force. Matching the contents of the images is less sensitive to the variation of the lighting. The problem with the brute force method is however that the time for calculation for 49 cartridge cases to compare between them, takes over 1 month of computing time on a Pentium II-computer with 333MHz. For this reason a faster approach is implemented: correlation in log polar coordinates. This gave similar results as the brute force calculation, however it was computed in 24h for a complete database with 4900 images.A fast pre-selection method based on signatures is carried out that is based on the Kanade Lucas Tomasi (KLT) equation. The positions of the points computed with this method are compared. In this way, 11 of the 49 images were in the top position in combination with the third scale of the à trous equation. It depends however on the light conditions and the prominence of the marks if correct matches are found in the top ranked position. All images were retrieved in the top 5% of the database. This method takes only a few minutes for the complete database if, and can be optimized for comparison in seconds if the location of points are stored in files. For further improvement, it is useful to have the refinement in which the user selects the areas that are relevant on the cartridge case for their marks. This is necessary if this cartridge case is damaged and other marks that are not from the firearm appear on it.

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

PubMed Central

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

2014-01-01

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

Interactive searching of facial image databases

NASA Astrophysics Data System (ADS)

Nicholls, Robert A.; Shepherd, John W.; Shepherd, Jean

1995-09-01

A set of psychological facial descriptors has been devised to enable computerized searching of criminal photograph albums. The descriptors have been used to encode image databased of up to twelve thousand images. Using a system called FACES, the databases are searched by translating a witness' verbal description into corresponding facial descriptors. Trials of FACES have shown that this coding scheme is more productive and efficient than searching traditional photograph albums. An alternative method of searching the encoded database using a genetic algorithm is currenly being tested. The genetic search method does not require the witness to verbalize a description of the target but merely to indicate a degree of similarity between the target and a limited selection of images from the database. The major drawback of FACES is that is requires a manual encoding of images. Research is being undertaken to automate the process, however, it will require an algorithm which can predict human descriptive values. Alternatives to human derived coding schemes exist using statistical classifications of images. Since databases encoded using statistical classifiers do not have an obvious direct mapping to human derived descriptors, a search method which does not require the entry of human descriptors is required. A genetic search algorithm is being tested for such a purpose.

Conversion of a traditional image archive into an image resource on compact disc.

PubMed Central

Andrew, S M; Benbow, E W

1997-01-01

The conversion of a traditional archive of pathology images was organised on 35 mm slides into a database of images stored on compact disc (CD-ROM), and textual descriptions were added to each image record. Students on a didactic pathology course found this resource useful as an aid to revision, despite relative computer illiteracy, and it is anticipated that students on a new problem based learning course, which incorporates experience with information technology, will benefit even more readily when they use the database as an educational resource. A text and image database on CD-ROM can be updated repeatedly, and the content manipulated to reflect the content and style of the courses it supports. Images PMID:9306931

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.

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

PubMed Central

Lowe, H. J.

1993-01-01

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

Retinal blood vessel segmentation using fully convolutional network with transfer learning.

PubMed

Jiang, Zhexin; Zhang, Hao; Wang, Yi; Ko, Seok-Bum

2018-04-26

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging. Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. Extensive experiments have been conducted on DRIVE, STARE, CHASE_DB1 and HRF databases, and the accuracy of the cross-database test on these four databases is state-of-the-art, which also presents the high robustness of the proposed approach. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging. Copyright © 2018 Elsevier Ltd. All rights reserved.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

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

NASA Astrophysics Data System (ADS)

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

1993-04-01

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

SU-E-J-129: A Strategy to Consolidate the Image Database of a VERO Unit Into a Radiotherapy Management System

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

Yan, Y; Medin, P; Yordy, J

2014-06-01

Purpose: To present a strategy to integrate the imaging database of a VERO unit with a treatment management system (TMS) to improve clinical workflow and consolidate image data to facilitate clinical quality control and documentation. Methods: A VERO unit is equipped with both kV and MV imaging capabilities for IGRT treatments. It has its own imaging database behind a firewall. It has been a challenge to transfer images on this unit to a TMS in a radiation therapy clinic so that registered images can be reviewed remotely with an approval or rejection record. In this study, a software system, iPump-VERO,more » was developed to connect VERO and a TMS in our clinic. The patient database folder on the VERO unit was mapped to a read-only folder on a file server outside VERO firewall. The application runs on a regular computer with the read access to the patient database folder. It finds the latest registered images and fuses them in one of six predefined patterns before sends them via DICOM connection to the TMS. The residual image registration errors will be overlaid on the fused image to facilitate image review. Results: The fused images of either registered kV planar images or CBCT images are fully DICOM compatible. A sentinel module is built to sense new registered images with negligible computing resources from the VERO ExacTrac imaging computer. It takes a few seconds to fuse registered images and send them to the TMS. The whole process is automated without any human intervention. Conclusion: Transferring images in DICOM connection is the easiest way to consolidate images of various sources in your TMS. Technically the attending does not have to go to the VERO treatment console to review image registration prior delivery. It is a useful tool for a busy clinic with a VERO unit.« less

NOAA Data Rescue of Key Solar Databases and Digitization of Historical Solar Images

NASA Astrophysics Data System (ADS)

Coffey, H. E.

2006-08-01

Over a number of years, the staff at NOAA National Geophysical Data Center (NGDC) has worked to rescue key solar databases by converting them to digital format and making them available via the World Wide Web. NOAA has had several data rescue programs where staff compete for funds to rescue important and critical historical data that are languishing in archives and at risk of being lost due to deteriorating condition, loss of any metadata or descriptive text that describe the databases, lack of interest or funding in maintaining databases, etc. The Solar-Terrestrial Physics Division at NGDC was able to obtain funds to key in some critical historical tabular databases. Recently the NOAA Climate Database Modernization Program (CDMP) funded a project to digitize historical solar images, producing a large online database of historical daily full disk solar images. The images include the wavelengths Calcium K, Hydrogen Alpha, and white light photos, as well as sunspot drawings and the comprehensive drawings of a multitude of solar phenomena on one daily map (Fraunhofer maps and Wendelstein drawings). Included in the digitization are high resolution solar H-alpha images taken at the Boulder Solar Observatory 1967-1984. The scanned daily images document many phases of solar activity, from decadal variation to rotational variation to daily changes. Smaller versions are available online. Larger versions are available by request. See http://www.ngdc.noaa.gov/stp/SOLAR/ftpsolarimages.html. The tabular listings and solar imagery will be discussed.

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.

Integrated radiologist's workstation enabling the radiologist as an effective clinical consultant

NASA Astrophysics Data System (ADS)

McEnery, Kevin W.; Suitor, Charles T.; Hildebrand, Stan; Downs, Rebecca; Thompson, Stephen K.; Shepard, S. Jeff

2002-05-01

Since February 2000, radiologists at the M. D. Anderson Cancer Center have accessed clinical information through an internally developed radiologist's clinical interpretation workstation called RadStation. This project provides a fully integrated digital dictation workstation with clinical data review. RadStation enables the radiologist as an effective clinical consultant with access to pertinent sources of clinical information at the time of dictation. Data sources not only include prior radiology reports from the radiology information system (RIS) but access to pathology data, laboratory data, history and physicals, clinic notes, and operative reports. With integrated clinical information access, a radiologists's interpretation not only comments on morphologic findings but also can enable evaluation of study findings in the context of pertinent clinical presentation and history. Image access is enabled through the integration of an enterprise image archive (Stentor, San Francisco). Database integration is achieved by a combination of real time HL7 messaging and queries to SQL-based legacy databases. A three-tier system architecture accommodates expanding access to additional databases including real-time patient schedule as well as patient medications and allergies.

Semantic Segmentation and Difference Extraction via Time Series Aerial Video Camera and its Application

NASA Astrophysics Data System (ADS)

Amit, S. N. K.; Saito, S.; Sasaki, S.; Kiyoki, Y.; Aoki, Y.

2015-04-01

Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a time consuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effective method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial images from Massachusetts's road and building open datasets, Saitama district datasets are used as input images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of images by implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorly registered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set as ground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1 dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next, ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different time series are compared via difference extraction method. The map will only update where local changes had occurred. Hence, map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only update changed region.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

Matching CCD images to a stellar catalog using locality-sensitive hashing

NASA Astrophysics Data System (ADS)

Liu, Bo; Yu, Jia-Zong; Peng, Qing-Yu

2018-02-01

The usage of a subset of observed stars in a CCD image to find their corresponding matched stars in a stellar catalog is an important issue in astronomical research. Subgraph isomorphic-based algorithms are the most widely used methods in star catalog matching. When more subgraph features are provided, the CCD images are recognized better. However, when the navigation feature database is large, the method requires more time to match the observing model. To solve this problem, this study investigates further and improves subgraph isomorphic matching algorithms. We present an algorithm based on a locality-sensitive hashing technique, which allocates quadrilateral models in the navigation feature database into different hash buckets and reduces the search range to the bucket in which the observed quadrilateral model is located. Experimental results indicate the effectivity of our method.

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.

Architecture for biomedical multimedia information delivery on the World Wide Web

NASA Astrophysics Data System (ADS)

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

1997-10-01

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

A complete database for the Einstein imaging proportional counter

NASA Technical Reports Server (NTRS)

Helfand, David J.

1991-01-01

A complete database for the Einstein Imaging Proportional Counter (IPC) was completed. The original data that makes up the archive is described as well as the structure of the database, the Op-Ed analysis system, the technical advances achieved relative to the analysis of (IPC) data, the data products produced, and some uses to which the database has been put by scientists outside Columbia University over the past year.

Image Encryption Algorithm Based on Hyperchaotic Maps and Nucleotide Sequences Database

PubMed Central

2017-01-01

Image encryption technology is one of the main means to ensure the safety of image information. Using the characteristics of chaos, such as randomness, regularity, ergodicity, and initial value sensitiveness, combined with the unique space conformation of DNA molecules and their unique information storage and processing ability, an efficient method for image encryption based on the chaos theory and a DNA sequence database is proposed. In this paper, digital image encryption employs a process of transforming the image pixel gray value by using chaotic sequence scrambling image pixel location and establishing superchaotic mapping, which maps quaternary sequences and DNA sequences, and by combining with the logic of the transformation between DNA sequences. The bases are replaced under the displaced rules by using DNA coding in a certain number of iterations that are based on the enhanced quaternary hyperchaotic sequence; the sequence is generated by Chen chaos. The cipher feedback mode and chaos iteration are employed in the encryption process to enhance the confusion and diffusion properties of the algorithm. Theoretical analysis and experimental results show that the proposed scheme not only demonstrates excellent encryption but also effectively resists chosen-plaintext attack, statistical attack, and differential attack. PMID:28392799

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

NASA Astrophysics Data System (ADS)

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

2004-05-01

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

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

PubMed

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

2004-01-01

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

A Web application for the management of clinical workflow in image-guided and adaptive proton therapy for prostate cancer treatments.

PubMed

Yeung, Daniel; Boes, Peter; Ho, Meng Wei; Li, Zuofeng

2015-05-08

Image-guided radiotherapy (IGRT), based on radiopaque markers placed in the prostate gland, was used for proton therapy of prostate patients. Orthogonal X-rays and the IBA Digital Image Positioning System (DIPS) were used for setup correction prior to treatment and were repeated after treatment delivery. Following a rationale for margin estimates similar to that of van Herk,(1) the daily post-treatment DIPS data were analyzed to determine if an adaptive radiotherapy plan was necessary. A Web application using ASP.NET MVC5, Entity Framework, and an SQL database was designed to automate this process. The designed features included state-of-the-art Web technologies, a domain model closely matching the workflow, a database-supporting concurrency and data mining, access to the DIPS database, secured user access and roles management, and graphing and analysis tools. The Model-View-Controller (MVC) paradigm allowed clean domain logic, unit testing, and extensibility. Client-side technologies, such as jQuery, jQuery Plug-ins, and Ajax, were adopted to achieve a rich user environment and fast response. Data models included patients, staff, treatment fields and records, correction vectors, DIPS images, and association logics. Data entry, analysis, workflow logics, and notifications were implemented. The system effectively modeled the clinical workflow and IGRT process.

Supporting reputation based trust management enhancing security layer for cloud service models

NASA Astrophysics Data System (ADS)

Karthiga, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.

2017-11-01

In the existing system trust between cloud providers and consumers is inadequate to establish the service level agreement though the consumer’s response is good cause to assess the overall reliability of cloud services. Investigators recognized the significance of trust can be managed and security can be provided based on feedback collected from participant. In this work a face recognition system that helps to identify the user effectively. So we use an image comparison algorithm where the user face is captured during registration time and get stored in database. With that original image we compare it with the sample image that is already stored in database. If both the image get matched then the users are identified effectively. When the confidential data are subcontracted to the cloud, data holders will become worried about the confidentiality of their data in the cloud. Encrypting the data before subcontracting has been regarded as the important resources of keeping user data privacy beside the cloud server. So in order to keep the data secure we use an AES algorithm. Symmetric-key algorithms practice a shared key concept, keeping data secret requires keeping this key secret. So only the user with private key can decrypt data.

On detection of median filtering in digital images

NASA Astrophysics Data System (ADS)

Kirchner, Matthias; Fridrich, Jessica

2010-01-01

In digital image forensics, it is generally accepted that intentional manipulations of the image content are most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. However, it is also beneficial to know as much as possible about the general processing history of an image, including content-preserving operations, since they can affect the reliability of forensic methods in various ways. In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method is backed with experimental evidence on a large image database.

Distributed data collection for a database of radiological image interpretations

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Ostchega, Yechiam; Goh, Gin-Hua; Thoma, George R.

1997-01-01

The National Library of Medicine, in collaboration with the National Center for Health Statistics and the National Institute for Arthritis and Musculoskeletal and Skin Diseases, has built a system for collecting radiological interpretations for a large set of x-ray images acquired as part of the data gathered in the second National Health and Nutrition Examination Survey. This system is capable of delivering across the Internet 5- and 10-megabyte x-ray images to Sun workstations equipped with X Window based 2048 X 2560 image displays, for the purpose of having these images interpreted for the degree of presence of particular osteoarthritic conditions in the cervical and lumbar spines. The collected interpretations can then be stored in a database at the National Library of Medicine, under control of the Illustra DBMS. This system is a client/server database application which integrates (1) distributed server processing of client requests, (2) a customized image transmission method for faster Internet data delivery, (3) distributed client workstations with high resolution displays, image processing functions and an on-line digital atlas, and (4) relational database management of the collected data.

How to design a horizontal patient-focused hospital.

PubMed

Murphy, E C; Ruflin, P

1993-05-01

Work Imaging is an executive information system for analyzing the cost effectiveness and efficiency of work processes and structures in health care. Advanced Work Imaging relational database technology allows managers and employees to take a sample work activities profile organization-wide. This is married to financial and organizational data to produce images of work within and across all functions, departments, and levels. The images are benchmarked against best practice data to provide insight on the quality and cost efficiency of work practice patterns, from individual roles to departmental skill mix to organization-wide service processes.

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.

DIGITAL CARTOGRAPHY OF THE PLANETS: NEW METHODS, ITS STATUS, AND ITS FUTURE.

USGS Publications Warehouse

Batson, R.M.

1987-01-01

A system has been developed that establishes a standardized cartographic database for each of the 19 planets and major satellites that have been explored to date. Compilation of the databases involves both traditional and newly developed digital image processing and mosaicking techniques, including radiometric and geometric corrections of the images. Each database, or digital image model (DIM), is a digital mosaic of spacecraft images that have been radiometrically and geometrically corrected and photometrically modeled. During compilation, ancillary data files such as radiometric calibrations and refined photometric values for all camera lens and filter combinations and refined camera-orientation matrices for all images used in the mapping are produced.

Searching for patterns in remote sensing image databases using neural networks

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1995-01-01

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

A database system to support image algorithm evaluation

NASA Technical Reports Server (NTRS)

Lien, Y. E.

1977-01-01

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

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

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

ERIC Educational Resources Information Center

Stephenson, Christie

1999-01-01

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

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

ERIC Educational Resources Information Center

Jack, Robert F.

1984-01-01

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

Predicting perceptual quality of images in realistic scenario using deep filter banks

NASA Astrophysics Data System (ADS)

Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

2018-03-01

Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

Case retrieval in medical databases by fusing heterogeneous information.

PubMed

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

2011-01-01

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

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

PubMed Central

Frankewitsch, T.; Prokosch, U.

1999-01-01

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

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

PubMed

Frankewitsch, T; Prokosch, U

1999-01-01

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

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

PubMed

Carazo, J M; Stelzer, E H

1999-01-01

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

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

NASA Astrophysics Data System (ADS)

Meyer, Joerg; Pahwa, Ash

2004-10-01

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

Design and implementation of a fault-tolerant and dynamic metadata database for clinical trials

NASA Astrophysics Data System (ADS)

Lee, J.; Zhou, Z.; Talini, E.; Documet, J.; Liu, B.

2007-03-01

In recent imaging-based clinical trials, quantitative image analysis (QIA) and computer-aided diagnosis (CAD) methods are increasing in productivity due to higher resolution imaging capabilities. A radiology core doing clinical trials have been analyzing more treatment methods and there is a growing quantity of metadata that need to be stored and managed. These radiology centers are also collaborating with many off-site imaging field sites and need a way to communicate metadata between one another in a secure infrastructure. Our solution is to implement a data storage grid with a fault-tolerant and dynamic metadata database design to unify metadata from different clinical trial experiments and field sites. Although metadata from images follow the DICOM standard, clinical trials also produce metadata specific to regions-of-interest and quantitative image analysis. We have implemented a data access and integration (DAI) server layer where multiple field sites can access multiple metadata databases in the data grid through a single web-based grid service. The centralization of metadata database management simplifies the task of adding new databases into the grid and also decreases the risk of configuration errors seen in peer-to-peer grids. In this paper, we address the design and implementation of a data grid metadata storage that has fault-tolerance and dynamic integration for imaging-based clinical trials.

The gene expression database for mouse development (GXD): putting developmental expression information at your fingertips.

PubMed

Smith, Constance M; Finger, Jacqueline H; Kadin, James A; Richardson, Joel E; Ringwald, Martin

2014-10-01

Because molecular mechanisms of development are extraordinarily complex, the understanding of these processes requires the integration of pertinent research data. Using the Gene Expression Database for Mouse Development (GXD) as an example, we illustrate the progress made toward this goal, and discuss relevant issues that apply to developmental databases and developmental research in general. Since its first release in 1998, GXD has served the scientific community by integrating multiple types of expression data from publications and electronic submissions and by making these data freely and widely available. Focusing on endogenous gene expression in wild-type and mutant mice and covering data from RNA in situ hybridization, in situ reporter (knock-in), immunohistochemistry, reverse transcriptase-polymerase chain reaction, Northern blot, and Western blot experiments, the database has grown tremendously over the years in terms of data content and search utilities. Currently, GXD includes over 1.4 million annotated expression results and over 260,000 images. All these data and images are readily accessible to many types of database searches. Here we describe the data and search tools of GXD; explain how to use the database most effectively; discuss how we acquire, curate, and integrate developmental expression information; and describe how the research community can help in this process. Copyright © 2014 The Authors Developmental Dynamics published by Wiley Periodicals, Inc. on behalf of American Association of Anatomists.

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

NASA Technical Reports Server (NTRS)

Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.

2012-01-01

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

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

ERIC Educational Resources Information Center

Alam, Javed; And Others

1991-01-01

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

Air Land Sea Bulletin. Issue No. 2013-1

DTIC Science & Technology

2013-01-01

face, finger- print, iris , DNA, and palm print. Biometric capabilities may achieve enabling effects such as the ability to separate, identify...to obtain forensic-quality fingerprints, latent fingerprints, iris images, photos, and other biometric data. Figure 1. SEEK II ALSB 2013-1 12...logical and biographical contextual data of POIs and matches fingerprints and iris images against an internal biomet- rics enrollment database. The

The Database Business: Managing Today--Planning for Tomorrow. Quality Assurance of Text and Image Databases at the U.S. Patent and Trademark Office.

ERIC Educational Resources Information Center

Grooms, David W.

1988-01-01

Discusses the quality controls imposed on text and image data that is currently being converted from paper to digital images by the Patent and Trademark Office. The methods of inspection used on text and on images are described, and the quality of the data delivered thus far is discussed. (CLB)

Role of HIS/RIS DICOM interfaces in the integration of imaging into the Department of Veterans Affairs healthcare enterprise

NASA Astrophysics Data System (ADS)

Kuzmak, Peter M.; Dayhoff, Ruth E.

1998-07-01

The U.S. Department of Veterans Affairs is integrating imaging into the healthcare enterprise using the Digital Imaging and Communication in Medicine (DICOM) standard protocols. Image management is directly integrated into the VistA Hospital Information System (HIS) software and clinical database. Radiology images are acquired via DICOM, and are stored directly in the HIS database. Images can be displayed on low- cost clinician's workstations throughout the medical center. High-resolution diagnostic quality multi-monitor VistA workstations with specialized viewing software can be used for reading radiology images. DICOM has played critical roles in the ability to integrate imaging functionality into the Healthcare Enterprise. Because of its openness, it allows the integration of system components from commercial and non- commercial sources to work together to provide functional cost-effective solutions (see Figure 1). Two approaches are used to acquire and handle images within the radiology department. At some VA Medical Centers, DICOM is used to interface a commercial Picture Archiving and Communications System (PACS) to the VistA HIS. At other medical centers, DICOM is used to interface the image producing modalities directly to the image acquisition and display capabilities of VistA itself. Both of these approaches use a small set of DICOM services that has been implemented by VistA to allow patient and study text data to be transmitted to image producing modalities and the commercial PACS, and to enable images and study data to be transferred back.

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…

Systematic review and modelling of the cost-effectiveness of cardiac magnetic resonance imaging compared with current existing testing pathways in ischaemic cardiomyopathy.

PubMed

Campbell, Fiona; Thokala, Praveen; Uttley, Lesley C; Sutton, Anthea; Sutton, Alex J; Al-Mohammad, Abdallah; Thomas, Steven M

2014-09-01

Cardiac magnetic resonance imaging (CMR) is increasingly used to assess patients for myocardial viability prior to revascularisation. This is important to ensure that only those likely to benefit are subjected to the risk of revascularisation. To assess current evidence on the accuracy and cost-effectiveness of CMR to test patients prior to revascularisation in ischaemic cardiomyopathy; to develop an economic model to assess cost-effectiveness for different imaging strategies; and to identify areas for further primary research. Databases searched were: MEDLINE including MEDLINE In-Process & Other Non-Indexed Citations Initial searches were conducted in March 2011 in the following databases with dates: MEDLINE including MEDLINE In-Process & Other Non-Indexed Citations via Ovid (1946 to March 2011); Bioscience Information Service (BIOSIS) Previews via Web of Science (1969 to March 2011); EMBASE via Ovid (1974 to March 2011); Cochrane Database of Systematic Reviews via The Cochrane Library (1996 to March 2011); Cochrane Central Register of Controlled Trials via The Cochrane Library 1998 to March 2011; Database of Abstracts of Reviews of Effects via The Cochrane Library (1994 to March 2011); NHS Economic Evaluation Database via The Cochrane Library (1968 to March 2011); Health Technology Assessment Database via The Cochrane Library (1989 to March 2011); and the Science Citation Index via Web of Science (1900 to March 2011). Additional searches were conducted from October to November 2011 in the following databases with dates: MEDLINE including MEDLINE In-Process & Other Non-Indexed Citations via Ovid (1946 to November 2011); BIOSIS Previews via Web of Science (1969 to October 2011); EMBASE via Ovid (1974 to November 2011); Cochrane Database of Systematic Reviews via The Cochrane Library (1996 to November 2011); Cochrane Central Register of Controlled Trials via The Cochrane Library (1998 to November 2011); Database of Abstracts of Reviews of Effects via The Cochrane Library (1994 to November 2011); NHS Economic Evaluation Database via The Cochrane Library (1968 to November 2011); Health Technology Assessment Database via The Cochrane Library (1989 to November 2011); and the Science Citation Index via Web of Science (1900 to October 2011). Electronic databases were searched March-November 2011. The systematic review selected studies that assessed the clinical effectiveness and cost-effectiveness of CMR to establish the role of CMR in viability assessment compared with other imaging techniques: stress echocardiography, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). Studies had to have an appropriate reference standard and contain accuracy data or sufficient details so that accuracy data could be calculated. Data were extracted by two reviewers and discrepancies resolved by discussion. Quality of studies was assessed using the QUADAS II tool (University of Bristol, Bristol, UK). A rigorous diagnostic accuracy systematic review assessed clinical and cost-effectiveness of CMR in viability assessment. A health economic model estimated costs and quality-adjusted life-years (QALYs) accrued by diagnostic pathways for identifying patients with viable myocardium in ischaemic cardiomyopathy with a view to revascularisation. The pathways involved CMR, stress echocardiography, SPECT, PET alone or in combination. Strategies of no testing and revascularisation were included to determine the most cost-effective strategy. Twenty-four studies met the inclusion criteria. All were prospective. Participant numbers ranged from 8 to 52. The mean left ventricular ejection fraction in studies reporting this outcome was 24-62%. CMR approaches included stress CMR and late gadolinium-enhanced cardiovascular magnetic resonance imaging (CE CMR). Recovery following revascularisation was the reference standard. Twelve studies assessed diagnostic accuracy of stress CMR and 14 studies assessed CE CMR. A bivariate regression model was used to calculate the sensitivity and specificity of CMR. Summary sensitivity and specificity for stress CMR was 82.2% [95% confidence interval (CI) 73.2% to 88.7%] and 87.1% (95% CI 80.4% to 91.7%) and for CE CMR was 95.5% (95% CI 94.1% to 96.7%) and 53% (95% CI 40.4% to 65.2%) respectively. The sensitivity and specificity of PET, SPECT and stress echocardiography were calculated using data from 10 studies and systematic reviews. The sensitivity of PET was 94.7% (95% CI 90.3% to 97.2%), of SPECT was 85.1% (95% CI 78.1% to 90.2%) and of stress echocardiography was 77.6% (95% CI 70.7% to 83.3%). The specificity of PET was 68.8% (95% CI 50% to 82.9%), of SPECT was 62.1% (95% CI 52.7% to 70.7%) and of stress echocardiography was 69.6% (95% CI 62.4% to 75.9%). All currently used diagnostic strategies were cost-effective compared with no testing at current National Institute for Health and Care Excellence thresholds. If the annual mortality rates for non-viable patients were assumed to be higher for revascularised patients, then testing with CE CMR was most cost-effective at a threshold of £20,000/QALY. The proportion of model runs in which each strategy was most cost-effective, at a threshold of £20,000/QALY, was 40% for CE CMR, 42% for PET and 16.5% for revascularising everyone. The expected value of perfect information at £20,000/QALY was £620 per patient. If all patients (viable or not) gained benefit from revascularisation, then it was most cost-effective to revascularise all patients. Definitions and techniques assessing viability were highly variable, making data extraction and comparisons difficult. Lack of evidence meant assumptions were made in the model leading to uncertainty; differing scenarios were generated around key assumptions. All the diagnostic pathways are a cost-effective use of NHS resources. Given the uncertainty in the mortality rates, the cost-effectiveness analysis was performed using a set of scenarios. The cost-effectiveness analyses suggest that CE CMR and revascularising everyone were the optimal strategies. Future research should look at implementation costs for this type of imaging service, provide guidance on consistent reporting of diagnostic testing data for viability assessment, and focus on the impact of revascularisation or best medical therapy in this group of high-risk patients. The National Institute of Health Technology Assessment programme.

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

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.

Techniques of Photometry and Astrometry with APASS, Gaia, and Pan-STARRs Results (Abstract)

NASA Astrophysics Data System (ADS)

Green, W.

2017-12-01

(Abstract only) The databases with the APASS DR9, Gaia DR1, and the Pan-STARRs 3pi DR1 data releases are publicly available for use. There is a bit of data-mining involved to download and manage these reference stars. This paper discusses the use of these databases to acquire accurate photometric references as well as techniques for improving results. Images are prepared in the usual way: zero, dark, flat-fields, and WCS solutions with Astrometry.net. Images are then processed with Sextractor to produce an ASCII table of identifying photometric features. The database manages photometics catalogs and images converted to ASCII tables. Scripts convert the files into SQL and assimilate them into database tables. Using SQL techniques, each image star is merged with reference data to produce publishable results. The VYSOS has over 13,000 images of the ONC5 field to process with roughly 100 total fields in the campaign. This paper provides the overview for this daunting task.

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

NASA Astrophysics Data System (ADS)

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

2011-02-01

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

Cost-effective data storage/archival subsystem for functional PACS

NASA Astrophysics Data System (ADS)

Chen, Y. P.; Kim, Yongmin

1993-09-01

Not the least of the requirements of a workable PACS is the ability to store and archive vast amounts of information. A medium-size hospital will generate between 1 and 2 TBytes of data annually on a fully functional PACS. A high-speed image transmission network coupled with a comparably high-speed central data storage unit can make local memory and magnetic disks in the PACS workstations less critical and, in an extreme case, unnecessary. Under these circumstances, the capacity and performance of the central data storage subsystem and database is critical in determining the response time at the workstations, thus significantly affecting clinical acceptability. The central data storage subsystem not only needs to provide sufficient capacity to store about ten days worth of images (five days worth of new studies, and on the average, about one comparison study for each new study), but also supplies images to the requesting workstation in a timely fashion. The database must provide fast retrieval responses upon users' requests for images. This paper analyzes both advantages and disadvantages of multiple parallel transfer disks versus RAID disks for short-term central data storage subsystem, as well as optical disk jukebox versus digital recorder tape subsystem for long-term archive. Furthermore, an example high-performance cost-effective storage subsystem which integrates both the RAID disks and high-speed digital tape subsystem as a cost-effective PACS data storage/archival unit are presented.

Enhancing the Utility of the Rapid Environmental Assessment Database Through the Use of In Situ and Modelled Data Sets During Q316

DTIC Science & Technology

2008-11-01

17ºC; red: 17-18ºC. Although the image produced in Figure 9 is useful, the image itself is not the most important aspect of the process . Two...climatology for the Scotian Shelf. The database is intended for use while ashore and also while at-sea. Trial Q316 was the maiden voyage of the database...to the process of data transfer from external sources to the database, and also how the database can be restructured to be more accommodating of

[Establishement for regional pelvic trauma database in Hunan Province].

PubMed

Cheng, Liang; Zhu, Yong; Long, Haitao; Yang, Junxiao; Sun, Buhua; Li, Kanghua

2017-04-28

To establish a database for pelvic trauma in Hunan Province, and to start the work of multicenter pelvic trauma registry.
 Methods: To establish the database, literatures relevant to pelvic trauma were screened, the experiences from the established trauma database in China and abroad were learned, and the actual situations for pelvic trauma rescue in Hunan Province were considered. The database for pelvic trauma was established based on the PostgreSQL and the advanced programming language Java 1.6.
 Results: The complex procedure for pelvic trauma rescue was described structurally. The contents for the database included general patient information, injurious condition, prehospital rescue, conditions in admission, treatment in hospital, status on discharge, diagnosis, classification, complication, trauma scoring and therapeutic effect. The database can be accessed through the internet by browser/servicer. The functions for the database include patient information management, data export, history query, progress report, video-image management and personal information management.
 Conclusion: The database with whole life cycle pelvic trauma is successfully established for the first time in China. It is scientific, functional, practical, and user-friendly.

Quantitative approach for optimizing e-beam condition of photoresist inspection and measurement

NASA Astrophysics Data System (ADS)

Lin, Chia-Jen; Teng, Chia-Hao; Cheng, Po-Chung; Sato, Yoshishige; Huang, Shang-Chieh; Chen, Chu-En; Maruyama, Kotaro; Yamazaki, Yuichiro

2018-03-01

Severe process margin in advanced technology node of semiconductor device is controlled by e-beam metrology system and e-beam inspection system with scanning electron microscopy (SEM) image. By using SEM, larger area image with higher image quality is required to collect massive amount of data for metrology and to detect defect in a large area for inspection. Although photoresist is the one of the critical process in semiconductor device manufacturing, observing photoresist pattern by SEM image is crucial and troublesome especially in the case of large image. The charging effect by e-beam irradiation on photoresist pattern causes deterioration of image quality, and it affect CD variation on metrology system and causes difficulties to continue defect inspection in a long time for a large area. In this study, we established a quantitative approach for optimizing e-beam condition with "Die to Database" algorithm of NGR3500 on photoresist pattern to minimize charging effect. And we enhanced the performance of measurement and inspection on photoresist pattern by using optimized e-beam condition. NGR3500 is the geometry verification system based on "Die to Database" algorithm which compares SEM image with design data [1]. By comparing SEM image and design data, key performance indicator (KPI) of SEM image such as "Sharpness", "S/N", "Gray level variation in FOV", "Image shift" can be retrieved. These KPIs were analyzed with different e-beam conditions which consist of "Landing Energy", "Probe Current", "Scanning Speed" and "Scanning Method", and the best e-beam condition could be achieved with maximum image quality, maximum scanning speed and minimum image shift. On this quantitative approach of optimizing e-beam condition, we could observe dependency of SEM condition on photoresist charging. By using optimized e-beam condition, measurement could be continued on photoresist pattern over 24 hours stably. KPIs of SEM image proved image quality during measurement and inspection was stabled enough.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Space Images for NASA JPL Android Version

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

NASA Technical Reports Server (NTRS)

Roussopoulos, N.; Sellis, Timos

1992-01-01

One of biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental database access method, VIEWCACHE, provides such an interface for accessing distributed data sets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image data sets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate distributed database search.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

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

Development of a networked four-million-pixel pathological and radiological digital image presentation system and its application to medical conferences

NASA Astrophysics Data System (ADS)

Sakano, Toshikazu; Furukawa, Isao; Okumura, Akira; Yamaguchi, Takahiro; Fujii, Tetsuro; Ono, Sadayasu; Suzuki, Junji; Matsuya, Shoji; Ishihara, Teruo

2001-08-01

The wide spread of digital technology in the medical field has led to a demand for the high-quality, high-speed, and user-friendly digital image presentation system in the daily medical conferences. To fulfill this demand, we developed a presentation system for radiological and pathological images. It is composed of a super-high-definition (SHD) imaging system, a radiological image database (R-DB), a pathological image database (P-DB), and the network interconnecting these three. The R-DB consists of a 270GB RAID, a database server workstation, and a film digitizer. The P-DB includes an optical microscope, a four-million-pixel digital camera, a 90GB RAID, and a database server workstation. A 100Mbps Ethernet LAN interconnects all the sub-systems. The Web-based system operation software was developed for easy operation. We installed the whole system in NTT East Kanto Hospital to evaluate it in the weekly case conferences. The SHD system could display digital full-color images of 2048 x 2048 pixels on a 28-inch CRT monitor. The doctors evaluated the image quality and size, and found them applicable to the actual medical diagnosis. They also appreciated short image switching time that contributed to smooth presentation. Thus, we confirmed that its characteristics met the requirements.

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685

An image database management system for conducting CAD research

NASA Astrophysics Data System (ADS)

Gruszauskas, Nicholas; Drukker, Karen; Giger, Maryellen L.

2007-03-01

The development of image databases for CAD research is not a trivial task. The collection and management of images and their related metadata from multiple sources is a time-consuming but necessary process. By standardizing and centralizing the methods in which these data are maintained, one can generate subsets of a larger database that match the specific criteria needed for a particular research project in a quick and efficient manner. A research-oriented management system of this type is highly desirable in a multi-modality CAD research environment. An online, webbased database system for the storage and management of research-specific medical image metadata was designed for use with four modalities of breast imaging: screen-film mammography, full-field digital mammography, breast ultrasound and breast MRI. The system was designed to consolidate data from multiple clinical sources and provide the user with the ability to anonymize the data. Input concerning the type of data to be stored as well as desired searchable parameters was solicited from researchers in each modality. The backbone of the database was created using MySQL. A robust and easy-to-use interface for entering, removing, modifying and searching information in the database was created using HTML and PHP. This standardized system can be accessed using any modern web-browsing software and is fundamental for our various research projects on computer-aided detection, diagnosis, cancer risk assessment, multimodality lesion assessment, and prognosis. Our CAD database system stores large amounts of research-related metadata and successfully generates subsets of cases that match the user's desired search criteria.

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.

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

NASA Astrophysics Data System (ADS)

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

1994-08-01

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

Information-theoretic CAD system in mammography: Entropy-based indexing for computational efficiency and robust performance

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

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

2007-08-15

We have previously presented a knowledge-based computer-assisted detection (KB-CADe) system for the detection of mammographic masses. The system is designed to compare a query mammographic region with mammographic templates of known ground truth. The templates are stored in an adaptive knowledge database. Image similarity is assessed with information theoretic measures (e.g., mutual information) derived directly from the image histograms. A previous study suggested that the diagnostic performance of the system steadily improves as the knowledge database is initially enriched with more templates. However, as the database increases in size, an exhaustive comparison of the query case with each stored templatemore » becomes computationally burdensome. Furthermore, blind storing of new templates may result in redundancies that do not necessarily improve diagnostic performance. To address these concerns we investigated an entropy-based indexing scheme for improving the speed of analysis and for satisfying database storage restrictions without compromising the overall diagnostic performance of our KB-CADe system. The indexing scheme was evaluated on two different datasets as (i) a search mechanism to sort through the knowledge database, and (ii) a selection mechanism to build a smaller, concise knowledge database that is easier to maintain but still effective. There were two important findings in the study. First, entropy-based indexing is an effective strategy to identify fast a subset of templates that are most relevant to a given query. Only this subset could be analyzed in more detail using mutual information for optimized decision making regarding the query. Second, a selective entropy-based deposit strategy may be preferable where only high entropy cases are maintained in the knowledge database. Overall, the proposed entropy-based indexing scheme was shown to reduce the computational cost of our KB-CADe system by 55% to 80% while maintaining the system's diagnostic performance.« less

Assessing telemedicine: a systematic review of the literature.

PubMed

Roine, R; Ohinmaa, A; Hailey, D

2001-09-18

To clarify the current status of telemedicine, we carried out a systematic review of the literature. We identified controlled assessment studies of telemedicine that reported patient outcomes, administrative changes or economic assessments and assessed the quality of that literature. We carried out a systematic electronic search for articles published from 1966 to early 2000 using the MEDLINE (1966-April 2000), HEALTHSTAR (1975-January 2000), EMBASE (1988-February 2000) and CINALH (1982-January 2000) databases. In addition, the HSTAT database (Health Services/Technology Assessment Text, US National Library of Medicine), the Database of Abstracts of Reviews of Effectiveness (DARE, NHS Centre for Reviews and Dissemination, United Kingdom), the NHS Economic Evaluation Database and the Cochrane Controlled Trials Register were searched. We consulted experts in the field and did a manual search of the reference lists of review articles. A total of 1124 studies were identified. Based on a review of the abstracts, 133 full-text articles were obtained for closer inspection. Of these, 50 were deemed to represent assessment studies fulfilling the inclusion criteria of the review. Thirty-four of the articles assessed at least some clinical outcomes; the remaining 16 were mainly economic analyses. Most of the available literature referred only to pilot projects and short-term outcomes, and most of the studies were of low quality. Relatively convincing evidence of effectiveness was found only for teleradiology, teleneurosurgery, telepsychiatry, transmission of echocardiographic images, and the use of electronic referrals enabling e-mail consultations and video conferencing between primary and secondary health care providers. Economic analyses suggested that teleradiology, especially transmission of CT images, can be cost-saving. Evidence regarding the effectiveness or cost-effectiveness of telemedicine is still limited. Based on current scientific evidence, only a few telemedicine applications can be recommended for broader use.

Texture-based CAD improves diagnosis for low-dose CT colonography

NASA Astrophysics Data System (ADS)

Liang, Zhengrong; Cohen, Harris; Posniak, Erica; Fiore, Eddie; Wang, Zigang; Li, Bin; Andersen, Joseph; Harrington, Donald

2008-03-01

Computed tomography (CT)-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast solutions to tag the colonic fluid and possibly residual stool for differentiation from the colon wall and polyps. The enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon wall as well as the lumen space (filled with air or CO II). The PV effect on the colon wall can "bury" polyps of size as large as 5mm by increasing their image densities to a noticeable level, resulting in false negatives. It can also create false positives when PV effect goes into the lumen space. We have been modeling the PV effect for mixture-based image segmentation and developing text-based computer-aided detection of polyp (CADpolyp) by utilizing the PV mixture-based image segmentation. This work presents some preliminary results of developing and applying texture-based CADpolyp technique to low-dose CTC studies. A total of 114 studies of asymptomatic patients older than 50, who underwent CTC and then optical colonoscopy (OC) on the same day, were selected from a database, which was accumulated in the past decade and contains various bowel preparations and CT scanning protocols. The participating radiologists found ten polyps of greater than 5 mm from a total of 16 OC proved polyps, i.e., a detection sensitivity of 63%. They scored 23 false positives from the database, i.e., a 20% false positive rate. Approximately 70% of the datasets were marked as imperfect bowel cleansing and/or presence of image artifacts. The impact of imperfect bowel cleansing and image artifacts on VC performance is significant. The texture-based CADpolyp detected all the polyps with an average of 2.68 false positives per patient. This indicates that texture-based CADpolyp can improve the CTC performance in the cases of imperfect cleansed bowels and presence of image artifacts.

A systematic review and economic evaluation of new-generation computed tomography scanners for imaging in coronary artery disease and congenital heart disease: Somatom Definition Flash, Aquilion ONE, Brilliance iCT and Discovery CT750 HD.

PubMed

Westwood, M; Al, M; Burgers, L; Redekop, K; Lhachimi, S; Armstrong, N; Raatz, H; Misso, K; Severens, J; Kleijnen, J

2013-01-01

Computed tomography (CT) is important in diagnosing and managing many conditions, including coronary artery disease (CAD) and congenital heart disease. Current CT scanners can very accurately diagnose CAD requiring revascularisation in most patients. However, imaging technologies have developed rapidly and new-generation computed tomography (NGCCT) scanners may benefit patients who are difficult to image (e.g. obese patients, patients with high or irregular heart beats and patients who have high levels of coronary calcium or a previous stent or bypass graft). To assess the clinical effectiveness and cost-effectiveness of NGCCT for diagnosing clinically significant CAD in patients who are difficult to image using 64-slice computed tomography and treatment planning in complex congenital heart disease. Bibliographic databases were searched from 2000 to February/March 2011, including MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Reviews of Effects (DARE), NHS Economic Evaluation Database (NHS EED), Health Technology Assessment (HTA) database and Science Citation Index (SCI). Trial registers and conference proceedings were searched. Systematic review methods followed published guidance. Risk of bias was assessed using QUADAS-2. Results were stratified by patient group. Summary sensitivity and specificity were calculated using a bivariate summary receiver operating characteristic, or random effects model. Heterogeneity was assessed using the chi-squared statistic and I(2)-statistic. Cost-effectiveness of NGCCT was modelled separately for suspected and known CAD, evaluating invasive coronary angiography (ICA) only, ICA after positive NGCCT (NGCCT-ICA), and NGCCT only. The cost-effectiveness of NGCCT, compared with 64-slice CT, in reducing imaging-associated radiation in congenital heart disease was assessed. Twenty-four studies reported accuracy of NGCCT for diagnosing CAD in difficult-to-image patients. No clinical effectiveness studies of NGCCT in congenital heart disease were identified. The pooled per-patient estimates of sensitivity were 97.7% [95% confidence interval (CI) 88.0% to 99.9%], 97.7% (95% CI 93.2% to 99.3%) and 96.0% (95% CI 88.8% to 99.2%) for patients with arrhythmias, high heart rates and previous stent, respectively. The corresponding estimates of specificity were 81.7% (95% CI 71.6% to 89.4%), 86.3% (95% CI 80.2% to 90.7%) and 81.6% (95% CI 74.7% to 87.3%), respectively. In patients with high coronary calcium scores, previous bypass grafts or obesity, only per-segment or per-artery data were available. Sensitivity estimates remained high (> 90% in all but one study). In patients with suspected CAD, the NGCCT-only strategy appeared most cost-effective; the incremental cost-effectiveness ratio (ICER) of NGCCT-ICA compared with NGCCT only was £71,000. In patients with known CAD, the most cost-effective strategy was NGCCT-ICA (highest cost saving, dominates ICA only). The ICER of NGCCT only compared with NGCCT-ICA was £726,230. For radiation exposure only, the ICER for NGCCT compared with 64-slice CT in congenital heart disease ranged from £521,000 for the youngest patients to £90,000 for adults. Available data were limited, particularly for obese patients and patients with previous bypass grafts. All studies of the accuracy of NGCCT assume that the reference standard (ICA) is 100% sensitive and specific; however, there is some evidence that ICA may sometimes underestimate the extent and severity of stenosis. Patients with more than one criterion that could contribute to difficulty in imaging were often excluded from studies; the effect on test accuracy of multiple difficult to image criteria remains uncertain. NGCCT may be sufficiently accurate to diagnose clinically significant CAD in some or all difficult-to-image patient groups. Economic analyses suggest that NGCCT is likely to be considered cost-effective for difficult-to-image patients with CAD, at current levels of willingness to pay in the NHS. For patients with suspected CAD, NGCCT only would be most favourable; for patients with known CAD, NGCCT-ICA would be most favourable. No studies assessing the effects of NGCCT on therapeutic decision making, or subsequent patient outcomes, were identified. The ideal study to address these questions would be a large multi-centre RCT. However, one possible alternative might be to establish a multicentre tracker study. High-quality test accuracy studies, particularly in obese patients, patients with high coronary calcium, and those with previous bypass grafts are needed to confirm the findings of our systematic review. These studies should include patients with multiple difficult to image criteria. The National Institute for Health Research Health Technology Assessment programme. This project was funded by the HTA programme, on behalf of NICE, as project number 10/107/01.

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.

Unique identification code for medical fundus images using blood vessel pattern for tele-ophthalmology applications.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; Sharma, Dilip Kumar

2016-10-01

Identification of fundus images during transmission and storage in database for tele-ophthalmology applications is an important issue in modern era. The proposed work presents a novel accurate method for generation of unique identification code for identification of fundus images for tele-ophthalmology applications and storage in databases. Unlike existing methods of steganography and watermarking, this method does not tamper the medical image as nothing is embedded in this approach and there is no loss of medical information. Strategic combination of unique blood vessel pattern and patient ID is considered for generation of unique identification code for the digital fundus images. Segmented blood vessel pattern near the optic disc is strategically combined with patient ID for generation of a unique identification code for the image. The proposed method of medical image identification is tested on the publically available DRIVE and MESSIDOR database of fundus image and results are encouraging. Experimental results indicate the uniqueness of identification code and lossless recovery of patient identity from unique identification code for integrity verification of fundus images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Depth image enhancement using perceptual texture priors

NASA Astrophysics Data System (ADS)

Bang, Duhyeon; Shim, Hyunjung

2015-03-01

A depth camera is widely used in various applications because it provides a depth image of the scene in real time. However, due to the limited power consumption, the depth camera presents severe noises, incapable of providing the high quality 3D data. Although the smoothness prior is often employed to subside the depth noise, it discards the geometric details so to degrade the distance resolution and hinder achieving the realism in 3D contents. In this paper, we propose a perceptual-based depth image enhancement technique that automatically recovers the depth details of various textures, using a statistical framework inspired by human mechanism of perceiving surface details by texture priors. We construct the database composed of the high quality normals. Based on the recent studies in human visual perception (HVP), we select the pattern density as a primary feature to classify textures. Upon the classification results, we match and substitute the noisy input normals with high quality normals in the database. As a result, our method provides the high quality depth image preserving the surface details. We expect that our work is effective to enhance the details of depth image from 3D sensors and to provide a high-fidelity virtual reality experience.

Novel algorithm by low complexity filter on retinal vessel segmentation

NASA Astrophysics Data System (ADS)

Rostampour, Samad

2011-10-01

This article shows a new method to detect blood vessels in the retina by digital images. Retinal vessel segmentation is important for detection of side effect of diabetic disease, because diabetes can form new capillaries which are very brittle. The research has been done in two phases: preprocessing and processing. Preprocessing phase consists to apply a new filter that produces a suitable output. It shows vessels in dark color on white background and make a good difference between vessels and background. The complexity is very low and extra images are eliminated. The second phase is processing and used the method is called Bayesian. It is a built-in in supervision classification method. This method uses of mean and variance of intensity of pixels for calculate of probability. Finally Pixels of image are divided into two classes: vessels and background. Used images are related to the DRIVE database. After performing this operation, the calculation gives 95 percent of efficiency average. The method also was performed from an external sample DRIVE database which has retinopathy, and perfect result was obtained

Exploring the feasibility of traditional image querying tasks for industrial radiographs

NASA Astrophysics Data System (ADS)

Bray, Iliana E.; Tsai, Stephany J.; Jimenez, Edward S.

2015-08-01

Although there have been great strides in object recognition with optical images (photographs), there has been comparatively little research into object recognition for X-ray radiographs. Our exploratory work contributes to this area by creating an object recognition system designed to recognize components from a related database of radiographs. Object recognition for radiographs must be approached differently than for optical images, because radiographs have much less color-based information to distinguish objects, and they exhibit transmission overlap that alters perceived object shapes. The dataset used in this work contained more than 55,000 intermixed radiographs and photographs, all in a compressed JPEG form and with multiple ways of describing pixel information. For this work, a robust and efficient system is needed to combat problems presented by properties of the X-ray imaging modality, the large size of the given database, and the quality of the images contained in said database. We have explored various pre-processing techniques to clean the cluttered and low-quality images in the database, and we have developed our object recognition system by combining multiple object detection and feature extraction methods. We present the preliminary results of the still-evolving hybrid object recognition system.

Semi-automatic feedback using concurrence between mixture vectors for general databases

NASA Astrophysics Data System (ADS)

Larabi, Mohamed-Chaker; Richard, Noel; Colot, Olivier; Fernandez-Maloigne, Christine

2001-12-01

This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).

VIEWCACHE: An incremental pointer-based access method for autonomous interoperable databases

NASA Technical Reports Server (NTRS)

Roussopoulos, N.; Sellis, Timos

1993-01-01

One of the biggest problems facing NASA today is to provide scientists efficient access to a large number of distributed databases. Our pointer-based incremental data base access method, VIEWCACHE, provides such an interface for accessing distributed datasets and directories. VIEWCACHE allows database browsing and search performing inter-database cross-referencing with no actual data movement between database sites. This organization and processing is especially suitable for managing Astrophysics databases which are physically distributed all over the world. Once the search is complete, the set of collected pointers pointing to the desired data are cached. VIEWCACHE includes spatial access methods for accessing image datasets, which provide much easier query formulation by referring directly to the image and very efficient search for objects contained within a two-dimensional window. We will develop and optimize a VIEWCACHE External Gateway Access to database management systems to facilitate database search.

An end to end secure CBIR over encrypted medical database.

PubMed

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

2016-08-01

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

NASA Image eXchange (NIX)

NASA Technical Reports Server (NTRS)

vonOfenheim. William H. C.; Heimerl, N. Lynn; Binkley, Robert L.; Curry, Marty A.; Slater, Richard T.; Nolan, Gerald J.; Griswold, T. Britt; Kovach, Robert D.; Corbin, Barney H.; Hewitt, Raymond W.

1998-01-01

This paper discusses the technical aspects of and the project background for the NASA Image exchange (NIX). NIX, which provides a single entry point to search selected image databases at the NASA Centers, is a meta-search engine (i.e., a search engine that communicates with other search engines). It uses these distributed digital image databases to access photographs, animations, and their associated descriptive information (meta-data). NIX is available for use at the following URL: http://nix.nasa.gov./NIX, which was sponsored by NASAs Scientific and Technical Information (STI) Program, currently serves images from seven NASA Centers. Plans are under way to link image databases from three additional NASA Centers. images and their associated meta-data, which are accessible by NIX, reside at the originating Centers, and NIX utilizes a virtual central site that communicates with each of these sites. Incorporated into the virtual central site are several protocols to support searches from a diverse collection of database engines. The searches are performed in parallel to ensure optimization of response times. To augment the search capability, browse functionality with pre-defined categories has been built into NIX, thereby ensuring dissemination of 'best-of-breed' imagery. As a final recourse, NIX offers access to a help desk via an on-line form to help locate images and information either within the scope of NIX or from available external sources.

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

The International Outer Planets Watch atmospheres node database of giant-planet images

NASA Astrophysics Data System (ADS)

Hueso, R.; Legarreta, J.; Sánchez-Lavega, A.; Rojas, J. F.; Gómez-Forrellad, J. M.

2011-10-01

The Atmospheres Node of the International Outer Planets Watch (IOPW) is aimed to encourage the observations and study of the atmospheres of the Giant Planets. One of its main activities is to provide an interaction between the professional and amateur astronomical communities maintaining an online and fully searchable database of images of the giant planets obtained from amateur astronomers and available to both professional and amateurs [1]. The IOPW database contains about 13,000 image observations of Jupiter and Saturn obtained in the visible range with a few contributions of Uranus and Neptune. We describe the organization and structure of the database as posted in the Internet and in particular the PVOL software (Planetary Virtual Observatory & Laboratory) designed to manage the site and based in concepts from Virtual Observatory projects.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

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

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

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

Digital hand atlas and computer-aided bone age assessment via the Web

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente

1999-07-01

A frequently used assessment method of bone age is atlas matching by a radiological examination of a hand image against a reference set of atlas patterns of normal standards. We are in a process of developing a digital hand atlas with a large standard set of normal hand and wrist images that reflect the skeletal maturity, race and sex difference, and current child development. The digital hand atlas will be used for a computer-aided bone age assessment via Web. We have designed and partially implemented a computer-aided diagnostic (CAD) system for Web-based bone age assessment. The system consists of a digital hand atlas, a relational image database and a Web-based user interface. The digital atlas is based on a large standard set of normal hand an wrist images with extracted bone objects and quantitative features. The image database uses a content- based indexing to organize the hand images and their attributes and present to users in a structured way. The Web-based user interface allows users to interact with the hand image database from browsers. Users can use a Web browser to push a clinical hand image to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, will be extracted and compared with patterns from the atlas database to assess the bone age. The relevant reference imags and the final assessment report will be sent back to the user's browser via Web. The digital atlas will remove the disadvantages of the currently out-of-date one and allow the bone age assessment to be computerized and done conveniently via Web. In this paper, we present the system design and Web-based client-server model for computer-assisted bone age assessment and our initial implementation of the digital atlas database.

Effectiveness of Using Mobile Phone Image Capture for Collecting Secondary Data: A Case Study on Immunization History Data Among Children in Remote Areas of Thailand.

PubMed

Jandee, Kasemsak; Kaewkungwal, Jaranit; Khamsiriwatchara, Amnat; Lawpoolsri, Saranath; Wongwit, Waranya; Wansatid, Peerawat

2015-07-20

Entering data onto paper-based forms, then digitizing them, is a traditional data-management method that might result in poor data quality, especially when the secondary data are incomplete, illegible, or missing. Transcription errors from source documents to case report forms (CRFs) are common, and subsequently the errors pass from the CRFs to the electronic database. This study aimed to demonstrate the usefulness and to evaluate the effectiveness of mobile phone camera applications in capturing health-related data, aiming for data quality and completeness as compared to current routine practices exercised by government officials. In this study, the concept of "data entry via phone image capture" (DEPIC) was introduced and developed to capture data directly from source documents. This case study was based on immunization history data recorded in a mother and child health (MCH) logbook. The MCH logbooks (kept by parents) were updated whenever parents brought their children to health care facilities for immunization. Traditionally, health providers are supposed to key in duplicate information of the immunization history of each child; both on the MCH logbook, which is returned to the parents, and on the individual immunization history card, which is kept at the health care unit to be subsequently entered into the electronic health care information system (HCIS). In this study, DEPIC utilized the photographic functionality of mobile phones to capture images of all immunization-history records on logbook pages and to transcribe these records directly into the database using a data-entry screen corresponding to logbook data records. DEPIC data were then compared with HCIS data-points for quality, completeness, and consistency. As a proof-of-concept, DEPIC captured immunization history records of 363 ethnic children living in remote areas from their MCH logbooks. Comparison of the 2 databases, DEPIC versus HCIS, revealed differences in the percentage of completeness and consistency of immunization history records. Comparing the records of each logbook in the DEPIC and HCIS databases, 17.3% (63/363) of children had complete immunization history records in the DEPIC database, but no complete records were reported in the HCIS database. Regarding the individual's actual vaccination dates, comparison of records taken from MCH logbook and those in the HCIS found that 24.2% (88/363) of the children's records were absolutely inconsistent. In addition, statistics derived from the DEPIC records showed a higher immunization coverage and much more compliance to immunization schedule by age group when compared to records derived from the HCIS database. DEPIC, or the concept of collecting data via image capture directly from their primary sources, has proven to be a useful data collection method in terms of completeness and consistency. In this study, DEPIC was implemented in data collection of a single survey. The DEPIC concept, however, can be easily applied in other types of survey research, for example, collecting data on changes or trends based on image evidence over time. With its image evidence and audit trail features, DEPIC has the potential for being used even in clinical studies since it could generate improved data integrity and more reliable statistics for use in both health care and research settings.

Interactive Scene Analysis Module - A sensor-database fusion system for telerobotic environments

NASA Technical Reports Server (NTRS)

Cooper, Eric G.; Vazquez, Sixto L.; Goode, Plesent W.

1992-01-01

Accomplishing a task with telerobotics typically involves a combination of operator control/supervision and a 'script' of preprogrammed commands. These commands usually assume that the location of various objects in the task space conform to some internal representation (database) of that task space. The ability to quickly and accurately verify the task environment against the internal database would improve the robustness of these preprogrammed commands. In addition, the on-line initialization and maintenance of a task space database is difficult for operators using Cartesian coordinates alone. This paper describes the Interactive Scene' Analysis Module (ISAM) developed to provide taskspace database initialization and verification utilizing 3-D graphic overlay modelling, video imaging, and laser radar based range imaging. Through the fusion of taskspace database information and image sensor data, a verifiable taskspace model is generated providing location and orientation data for objects in a task space. This paper also describes applications of the ISAM in the Intelligent Systems Research Laboratory (ISRL) at NASA Langley Research Center, and discusses its performance relative to representation accuracy and operator interface efficiency.

Development of educational image databases and e-books for medical physics training.

PubMed

Tabakov, S; Roberts, V C; Jonsson, B-A; Ljungberg, M; Lewis, C A; Wirestam, R; Strand, S-E; Lamm, I-L; Milano, F; Simmons, A; Deane, C; Goss, D; Aitken, V; Noel, A; Giraud, J-Y; Sherriff, S; Smith, P; Clarke, G; Almqvist, M; Jansson, T

2005-09-01

Medical physics education and training requires the use of extensive imaging material and specific explanations. These requirements provide an excellent background for application of e-Learning. The EU projects Consortia EMERALD and EMIT developed five volumes of such materials, now used in 65 countries. EMERALD developed e-Learning materials in three areas of medical physics (X-ray diagnostic radiology, nuclear medicine and radiotherapy). EMIT developed e-Learning materials in two further areas: ultrasound and magnetic resonance imaging. This paper describes the development of these e-Learning materials (consisting of e-books and educational image databases). The e-books include tasks helping studying of various equipment and methods. The text of these PDF e-books is hyperlinked with respective images. The e-books are used through the readers' own Internet browser. Each Image Database (IDB) includes a browser, which displays hundreds of images of equipment, block diagrams and graphs, image quality examples, artefacts, etc. Both the e-books and IDB are engraved on five separate CD-ROMs. Demo of these materials can be taken from www.emerald2.net.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

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.

Image-based query-by-example for big databases of galaxy images

NASA Astrophysics Data System (ADS)

Shamir, Lior; Kuminski, Evan

2017-01-01

Very large astronomical databases containing millions or even billions of galaxy images have been becoming increasingly important tools in astronomy research. However, in many cases the very large size makes it more difficult to analyze these data manually, reinforcing the need for computer algorithms that can automate the data analysis process. An example of such task is the identification of galaxies of a certain morphology of interest. For instance, if a rare galaxy is identified it is reasonable to expect that more galaxies of similar morphology exist in the database, but it is virtually impossible to manually search these databases to identify such galaxies. Here we describe computer vision and pattern recognition methodology that receives a galaxy image as an input, and searches automatically a large dataset of galaxies to return a list of galaxies that are visually similar to the query galaxy. The returned list is not necessarily complete or clean, but it provides a substantial reduction of the original database into a smaller dataset, in which the frequency of objects visually similar to the query galaxy is much higher. Experimental results show that the algorithm can identify rare galaxies such as ring galaxies among datasets of 10,000 astronomical objects.

[Research report of experimental database establishment of digitized virtual Chinese No.1 female].

PubMed

Zhong, Shi-zhen; Yuan, Lin; Tang, Lei; Huang, Wen-hua; Dai, Jing-xing; Li, Jian-yi; Liu, Chang; Wang, Xing-hai; Li, Hua; Luo, Shu-qian; Qin, Dulie; Zeng, Shao-qun; Wu, Tao; Zhang, Mei-chao; Wu, Kun-cheng; Jiao, Pei-feng; Lu, Yun-tao; Chen, Hao; Li, Pei-liang; Gao, Yuan; Wang, Tong; Fan, Ji-hong

2003-03-01

To establish digitized virtual Chinese No.1 female (VCH-F1) image database. A 19 years old female cadaver was scanned by CT, MRI, and perfused with red filling material through formal artery before freezing and em- bedding. The whole body was cut by JZ1500A vertical milling machine with a 0.2 mm inter-spacing. All the images was produced by Fuji FinePix S2 Pro camera. The body index of VCH-F1 was 94%. We cut 8 556 sections of the whole body, and each image was 17.5 MB in size and the whole database reached 149.7 GB. We have totally 6 versions of the database for different applications. Compared with other databases, VCH-F1 has good representation of the Chinese body shape, colorful filling material in blood vessels providing enough information for future registration and segmentation. Vertical embedding and cutting helped to retain normal human physiological posture, and the image quality and operation efficiency were improved by using various techniques such as one-time freezing and fixation, double-temperature icehouse, large-diameter milling disc and whole body cutting.

A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images

NASA Astrophysics Data System (ADS)

Wang, Zhihua; Yang, Xiaomei; Lu, Chen; Yang, Fengshuo

2018-07-01

Automatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts' intervention. And the samples transferred from historical LUCC also need experts' intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts' intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.

A facial expression image database and norm for Asian population: a preliminary report

NASA Astrophysics Data System (ADS)

Chen, Chien-Chung; Cho, Shu-ling; Horszowska, Katarzyna; Chen, Mei-Yen; Wu, Chia-Ching; Chen, Hsueh-Chih; Yeh, Yi-Yu; Cheng, Chao-Min

2009-01-01

We collected 6604 images of 30 models in eight types of facial expression: happiness, anger, sadness, disgust, fear, surprise, contempt and neutral. Among them, 406 most representative images from 12 models were rated by more than 200 human raters for perceived emotion category and intensity. Such large number of emotion categories, models and raters is sufficient for most serious expression recognition research both in psychology and in computer science. All the models and raters are of Asian background. Hence, this database can also be used when the culture background is a concern. In addition, 43 landmarks each of the 291 rated frontal view images were identified and recorded. This information should facilitate feature based research of facial expression. Overall, the diversity in images and richness in information should make our database and norm useful for a wide range of research.

Basic level scene understanding: categories, attributes and structures

PubMed Central

Xiao, Jianxiong; Hays, James; Russell, Bryan C.; Patterson, Genevieve; Ehinger, Krista A.; Torralba, Antonio; Oliva, Aude

2013-01-01

A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image. PMID:24009590

An online database for plant image analysis software tools.

PubMed

Lobet, Guillaume; Draye, Xavier; Périlleux, Claire

2013-10-09

Recent years have seen an increase in methods for plant phenotyping using image analyses. These methods require new software solutions for data extraction and treatment. These solutions are instrumental in supporting various research pipelines, ranging from the localisation of cellular compounds to the quantification of tree canopies. However, due to the variety of existing tools and the lack of central repository, it is challenging for researchers to identify the software that is best suited for their research. We present an online, manually curated, database referencing more than 90 plant image analysis software solutions. The website, plant-image-analysis.org, presents each software in a uniform and concise manner enabling users to identify the available solutions for their experimental needs. The website also enables user feedback, evaluations and new software submissions. The plant-image-analysis.org database provides an overview of existing plant image analysis software. The aim of such a toolbox is to help users to find solutions, and to provide developers a way to exchange and communicate about their work.

Programmed database system at the Chang Gung Craniofacial Center: part II--digitizing photographs.

PubMed

Chuang, Shiow-Shuh; Hung, Kai-Fong; de Villa, Glenda H; Chen, Philip K T; Lo, Lun-Jou; Chang, Sophia C N; Yu, Chung-Chih; Chen, Yu-Ray

2003-07-01

The archival tools used for digital images in advertising are not to fulfill the clinic requisition and are just beginning to develop. The storage of a large amount of conventional photographic slides needs a lot of space and special conditions. In spite of special precautions, degradation of the slides still occurs. The most common degradation is the appearance of fungus flecks. With the recent advances in digital technology, it is now possible to store voluminous numbers of photographs on a computer hard drive and keep them for a long time. A self-programmed interface has been developed to integrate database and image browser system that can build and locate needed files archive in a matter of seconds with the click of a button. This system requires hardware and software were market provided. There are 25,200 patients recorded in the database that involve 24,331 procedures. In the image files, there are 6,384 patients with 88,366 digital pictures files. From 1999 through 2002, NT400,000 dollars have been saved using the new system. Photographs can be managed with the integrating Database and Browse software for database archiving. This allows labeling of the individual photographs with demographic information and browsing. Digitized images are not only more efficient and economical than the conventional slide images, but they also facilitate clinical studies.

Exploitation of realistic computational anthropomorphic phantoms for the optimization of nuclear imaging acquisition and processing protocols.

PubMed

Loudos, George K; Papadimitroulas, Panagiotis G; Kagadis, George C

2014-01-01

Monte Carlo (MC) simulations play a crucial role in nuclear medical imaging since they can provide the ground truth for clinical acquisitions, by integrating and quantifing all physical parameters that affect image quality. The last decade a number of realistic computational anthropomorphic models have been developed to serve imaging, as well as other biomedical engineering applications. The combination of MC techniques with realistic computational phantoms can provide a powerful tool for pre and post processing in imaging, data analysis and dosimetry. This work aims to create a global database for simulated Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) exams and the methodology, as well as the first elements are presented. Simulations are performed using the well validated GATE opensource toolkit, standard anthropomorphic phantoms and activity distribution of various radiopharmaceuticals, derived from literature. The resulting images, projections and sinograms of each study are provided in the database and can be further exploited to evaluate processing and reconstruction algorithms. Patient studies using different characteristics are included in the database and different computational phantoms were tested for the same acquisitions. These include the XCAT, Zubal and the Virtual Family, which some of which are used for the first time in nuclear imaging. The created database will be freely available and our current work is towards its extension by simulating additional clinical pathologies.

Database Development for Ocean Impacts: Imaging, Outreach, and Rapid Response

DTIC Science & Technology

2012-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Database Development for Ocean Impacts: Imaging, Outreach...Development for Ocean Impacts: Imaging, Outreach, and Rapid Response 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...hoses ( Applied Ocean Physics & Engineering department, WHOI, to evaluate wear and locate in mooring optical cables used in the Right Whale monitoring

Image

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

Marsh, Amber; Harsch, Tim; Pitt, Julie

2007-08-31

The computer side of the IMAGE project consists of a collection of Perl scripts that perform a variety of tasks; scripts are available to insert, update and delete data from the underlying Oracle database, download data from NCBI's Genbank and other sources, and generate data files for download by interested parties. Web scripts make up the tracking interface, and various tools available on the project web-site (image.llnl.gov) that provide a search interface to the database.

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

PubMed

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

2014-08-01

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

Evaluation of Acoustic Propagation Paths into the Human Head

DTIC Science & Technology

2005-07-25

paths. A 3D finite-element solid mesh was constructed using a digital image database of an adult male head. Finite-element analysis was used to model the...air-borne sound pressure amplitude) via the alternate propagation paths. A 3D finite-element solid mesh was constructed using a digital image database ... database of an adult male head Coupled acoustic-mechanical finite-element analysis (FEA) was used to model the wave propagation through the fluid-solid

An embedded face-classification system for infrared images on an FPGA

NASA Astrophysics Data System (ADS)

Soto, Javier E.; Figueroa, Miguel

2014-10-01

We present a face-classification architecture for long-wave infrared (IR) images implemented on a Field Programmable Gate Array (FPGA). The circuit is fast, compact and low power, can recognize faces in real time and be embedded in a larger image-processing and computer vision system operating locally on an IR camera. The algorithm uses Local Binary Patterns (LBP) to perform feature extraction on each IR image. First, each pixel in the image is represented as an LBP pattern that encodes the similarity between the pixel and its neighbors. Uniform LBP codes are then used to reduce the number of patterns to 59 while preserving more than 90% of the information contained in the original LBP representation. Then, the image is divided into 64 non-overlapping regions, and each region is represented as a 59-bin histogram of patterns. Finally, the algorithm concatenates all 64 regions to create a 3,776-bin spatially enhanced histogram. We reduce the dimensionality of this histogram using Linear Discriminant Analysis (LDA), which improves clustering and enables us to store an entire database of 53 subjects on-chip. During classification, the circuit applies LBP and LDA to each incoming IR image in real time, and compares the resulting feature vector to each pattern stored in the local database using the Manhattan distance. We implemented the circuit on a Xilinx Artix-7 XC7A100T FPGA and tested it with the UCHThermalFace database, which consists of 28 81 x 150-pixel images of 53 subjects in indoor and outdoor conditions. The circuit achieves a 98.6% hit ratio, trained with 16 images and tested with 12 images of each subject in the database. Using a 100 MHz clock, the circuit classifies 8,230 images per second, and consumes only 309mW.

MetPetDB: A database for metamorphic geochemistry

NASA Astrophysics Data System (ADS)

Spear, Frank S.; Hallett, Benjamin; Pyle, Joseph M.; Adalı, Sibel; Szymanski, Boleslaw K.; Waters, Anthony; Linder, Zak; Pearce, Shawn O.; Fyffe, Matthew; Goldfarb, Dennis; Glickenhouse, Nickolas; Buletti, Heather

2009-12-01

We present a data model for the initial implementation of MetPetDB, a geochemical database specific to metamorphic rock samples. The database is designed around the concept of preservation of spatial relationships, at all scales, of chemical analyses and their textural setting. Objects in the database (samples) represent physical rock samples; each sample may contain one or more subsamples with associated geochemical and image data. Samples, subsamples, geochemical data, and images are described with attributes (some required, some optional); these attributes also serve as search delimiters. All data in the database are classified as published (i.e., archived or published data), public or private. Public and published data may be freely searched and downloaded. All private data is owned; permission to view, edit, download and otherwise manipulate private data may be granted only by the data owner; all such editing operations are recorded by the database to create a data version log. The sharing of data permissions among a group of collaborators researching a common sample is done by the sample owner through the project manager. User interaction with MetPetDB is hosted by a web-based platform based upon the Java servlet application programming interface, with the PostgreSQL relational database. The database web portal includes modules that allow the user to interact with the database: registered users may save and download public and published data, upload private data, create projects, and assign permission levels to project collaborators. An Image Viewer module provides for spatial integration of image and geochemical data. A toolkit consisting of plotting and geochemical calculation software for data analysis and a mobile application for viewing the public and published data is being developed. Future issues to address include population of the database, integration with other geochemical databases, development of the analysis toolkit, creation of data models for derivative data, and building a community-wide user base. It is believed that this and other geochemical databases will enable more productive collaborations, generate more efficient research efforts, and foster new developments in basic research in the field of solid earth geochemistry.

Storage and retrieval of digital images in dermatology.

PubMed

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

1995-11-01

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

A Medical Image Backup Architecture Based on a NoSQL Database and Cloud Computing Services.

PubMed

Santos Simões de Almeida, Luan Henrique; Costa Oliveira, Marcelo

2015-01-01

The use of digital systems for storing medical images generates a huge volume of data. Digital images are commonly stored and managed on a Picture Archiving and Communication System (PACS), under the DICOM standard. However, PACS is limited because it is strongly dependent on the server's physical space. Alternatively, Cloud Computing arises as an extensive, low cost, and reconfigurable resource. However, medical images contain patient information that can not be made available in a public cloud. Therefore, a mechanism to anonymize these images is needed. This poster presents a solution for this issue by taking digital images from PACS, converting the information contained in each image file to a NoSQL database, and using cloud computing to store digital images.

Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

NASA Astrophysics Data System (ADS)

Cui, Chen; Asari, Vijayan K.

2014-03-01

Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Four databases were used for testing the performance of the proposed system: Yale Face database, Extended Yale Face database B, Japanese Female Facial Expression database, and CMU AMP Facial Expression database. The experimental results in all four databases show the effectiveness of the proposed system. Also, the computation cost is lower because of the simplified calculation steps. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose-varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.

Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy.

PubMed

Sil Kar, Sudeshna; Maity, Santi P

2016-09-01

Extraction of blood vessels on retinal images plays a significant role for screening of different opthalmologic diseases. However, accurate extraction of the entire and individual type of vessel silhouette from the noisy images with poorly illuminated background is a complicated task. To this aim, an integrated system design platform is suggested in this work for vessel extraction using a sequential bandpass filter followed by fuzzy conditional entropy maximization on matched filter response. At first noise is eliminated from the image under consideration through curvelet based denoising. To include the fine details and the relatively less thick vessel structures, the image is passed through a bank of sequential bandpass filter structure optimized for contrast enhancement. Fuzzy conditional entropy on matched filter response is then maximized to find the set of multiple optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to determine the optimal gain in bandpass filter and the combination of the fuzzy parameters. Using the multiple thresholds, retinal image is classified as the thick, the medium and the thin vessels including neovascularization. Performance evaluated on different publicly available retinal image databases shows that the proposed method is very efficient in identifying the diverse types of vessels. Proposed method is also efficient in extracting the abnormal and the thin blood vessels in pathological retinal images. The average values of true positive rate, false positive rate and accuracy offered by the method is 76.32%, 1.99% and 96.28%, respectively for the DRIVE database and 72.82%, 2.6% and 96.16%, respectively for the STARE database. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the various types of vessels and the neovascularization structures. The combination of curvelet transform and tunable bandpass filter is found to be very much effective in edge enhancement whereas fuzzy conditional entropy efficiently distinguishes vessels of different widths. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

New learning based super-resolution: use of DWT and IGMRF prior.

PubMed

Gajjar, Prakash P; Joshi, Manjunath V

2010-05-01

In this paper, we propose a new learning-based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a database consisting of low and high spatial resolution images, we obtain super-resolution for the test image. We first obtain an initial high-resolution (HR) estimate by learning the high-frequency details from the available database. A new discrete wavelet transform (DWT) based approach is proposed for learning that uses a set of low-resolution (LR) images and their corresponding HR versions. Since the super-resolution is an ill-posed problem, we obtain the final solution using a regularization framework. The LR image is modeled as the aliased and noisy version of the corresponding HR image, and the aliasing matrix entries are estimated using the test image and the initial HR estimate. The prior model for the super-resolved image is chosen as an Inhomogeneous Gaussian Markov random field (IGMRF) and the model parameters are estimated using the same initial HR estimate. A maximum a posteriori (MAP) estimation is used to arrive at the cost function which is minimized using a simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting the experiments on gray scale as well as on color images. The method is compared with the standard interpolation technique and also with existing learning-based approaches. The proposed approach can be used in applications such as wildlife sensor networks, remote surveillance where the memory, the transmission bandwidth, and the camera cost are the main constraints.

Study on Big Database Construction and its Application of Sample Data Collected in CHINA'S First National Geographic Conditions Census Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Cheng, T.; Zhou, X.; Jia, Y.; Yang, G.; Bai, J.

2018-04-01

In the project of China's First National Geographic Conditions Census, millions of sample data have been collected all over the country for interpreting land cover based on remote sensing images, the quantity of data files reaches more than 12,000,000 and has grown in the following project of National Geographic Conditions Monitoring. By now, using database such as Oracle for storing the big data is the most effective method. However, applicable method is more significant for sample data's management and application. This paper studies a database construction method which is based on relational database with distributed file system. The vector data and file data are saved in different physical location. The key issues and solution method are discussed. Based on this, it studies the application method of sample data and analyzes some kinds of using cases, which could lay the foundation for sample data's application. Particularly, sample data locating in Shaanxi province are selected for verifying the method. At the same time, it takes 10 first-level classes which defined in the land cover classification system for example, and analyzes the spatial distribution and density characteristics of all kinds of sample data. The results verify that the method of database construction which is based on relational database with distributed file system is very useful and applicative for sample data's searching, analyzing and promoted application. Furthermore, sample data collected in the project of China's First National Geographic Conditions Census could be useful in the earth observation and land cover's quality assessment.

Real-time magnetic resonance imaging and electromagnetic articulography database for speech production research (TC)

PubMed Central

Narayanan, Shrikanth; Toutios, Asterios; Ramanarayanan, Vikram; Lammert, Adam; Kim, Jangwon; Lee, Sungbok; Nayak, Krishna; Kim, Yoon-Chul; Zhu, Yinghua; Goldstein, Louis; Byrd, Dani; Bresch, Erik; Ghosh, Prasanta; Katsamanis, Athanasios; Proctor, Michael

2014-01-01

USC-TIMIT is an extensive database of multimodal speech production data, developed to complement existing resources available to the speech research community and with the intention of being continuously refined and augmented. The database currently includes real-time magnetic resonance imaging data from five male and five female speakers of American English. Electromagnetic articulography data have also been presently collected from four of these speakers. The two modalities were recorded in two independent sessions while the subjects produced the same 460 sentence corpus used previously in the MOCHA-TIMIT database. In both cases the audio signal was recorded and synchronized with the articulatory data. The database and companion software are freely available to the research community. PMID:25190403

Handwritten text line segmentation by spectral clustering

NASA Astrophysics Data System (ADS)

Han, Xuecheng; Yao, Hui; Zhong, Guoqiang

2017-02-01

Since handwritten text lines are generally skewed and not obviously separated, text line segmentation of handwritten document images is still a challenging problem. In this paper, we propose a novel text line segmentation algorithm based on the spectral clustering. Given a handwritten document image, we convert it to a binary image first, and then compute the adjacent matrix of the pixel points. We apply spectral clustering on this similarity metric and use the orthogonal kmeans clustering algorithm to group the text lines. Experiments on Chinese handwritten documents database (HIT-MW) demonstrate the effectiveness of the proposed method.

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

NASA Technical Reports Server (NTRS)

Gladkova, Irina; Grossberg, Michael; Bonev, George; Romanov, Peter; Riggs, George; Hall, Dorothy

2013-01-01

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

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.

Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database

PubMed Central

Keator, David B.; Wei, Dingying; Fennema-Notestine, Christine; Pease, Karen R.; Bockholt, Jeremy; Grethe, Jeffrey S.

2010-01-01

Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site. PMID:20567938

77 FR 40268 - Deposit Requirements for Registration of Automated Databases That Predominantly Consist of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-09

... Registration of Automated Databases That Predominantly Consist of Photographs AGENCY: Copyright Office, Library... the deposit requirements for applications for automated databases that consist predominantly of... authorship, the deposits for such databases include the image of each photograph in which copyright is...

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.

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.

Fish Karyome: A karyological information network database of Indian Fishes.

PubMed

Nagpure, Naresh Sahebrao; Pathak, Ajey Kumar; Pati, Rameshwar; Singh, Shri Prakash; Singh, Mahender; Sarkar, Uttam Kumar; Kushwaha, Basdeo; Kumar, Ravindra

2012-01-01

'Fish Karyome', a database on karyological information of Indian fishes have been developed that serves as central source for karyotype data about Indian fishes compiled from the published literature. Fish Karyome has been intended to serve as a liaison tool for the researchers and contains karyological information about 171 out of 2438 finfish species reported in India and is publically available via World Wide Web. The database provides information on chromosome number, morphology, sex chromosomes, karyotype formula and cytogenetic markers etc. Additionally, it also provides the phenotypic information that includes species name, its classification, and locality of sample collection, common name, local name, sex, geographical distribution, and IUCN Red list status. Besides, fish and karyotype images, references for 171 finfish species have been included in the database. Fish Karyome has been developed using SQL Server 2008, a relational database management system, Microsoft's ASP.NET-2008 and Macromedia's FLASH Technology under Windows 7 operating environment. The system also enables users to input new information and images into the database, search and view the information and images of interest using various search options. Fish Karyome has wide range of applications in species characterization and identification, sex determination, chromosomal mapping, karyo-evolution and systematics of fishes.

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.

Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers

PubMed Central

Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M.; Mascetti, Sergio

2016-01-01

In this paper we propose a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm could also be complemented by a final crowdsourcing validation stage for increased accuracy. PMID:26824080

The visible human project®: From body to bits.

PubMed

Ackerman, Michael J

2016-08-01

In the middle 1990's the U.S. National Library sponsored the acquisition and development of the Visible Human Project® data base. This image database contains anatomical cross-sectional images which allow the reconstruction of three dimensional male and female anatomy to an accuracy of less than 1.0 mm. The male anatomy is contained in a 15 gigabyte database, the female in a 39 gigabyte database. This talk will describe why and how this project was accomplished and demonstrate some of the products which the Visible Human dataset has made possible. I will conclude by describing how the Visible Human Project, completed over 20 years ago, has led the National Library of Medicine to a series of image research projects including an open source image processing toolkit which is included in several commercial products.

SU-E-T-255: Development of a Michigan Quality Assurance (MQA) Database for Clinical Machine Operations

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

Roberts, D

Purpose: A unified database system was developed to allow accumulation, review and analysis of quality assurance (QA) data for measurement, treatment, imaging and simulation equipment in our department. Recording these data in a database allows a unified and structured approach to review and analysis of data gathered using commercial database tools. Methods: A clinical database was developed to track records of quality assurance operations on linear accelerators, a computed tomography (CT) scanner, high dose rate (HDR) afterloader and imaging systems such as on-board imaging (OBI) and Calypso in our department. The database was developed using Microsoft Access database and visualmore » basic for applications (VBA) programming interface. Separate modules were written for accumulation, review and analysis of daily, monthly and annual QA data. All modules were designed to use structured query language (SQL) as the basis of data accumulation and review. The SQL strings are dynamically re-written at run time. The database also features embedded documentation, storage of documents produced during QA activities and the ability to annotate all data within the database. Tests are defined in a set of tables that define test type, specific value, and schedule. Results: Daily, Monthly and Annual QA data has been taken in parallel with established procedures to test MQA. The database has been used to aggregate data across machines to examine the consistency of machine parameters and operations within the clinic for several months. Conclusion: The MQA application has been developed as an interface to a commercially available SQL engine (JET 5.0) and a standard database back-end. The MQA system has been used for several months for routine data collection.. The system is robust, relatively simple to extend and can be migrated to a commercial SQL server.« less

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Small PACS implementation using publicly available software

NASA Astrophysics Data System (ADS)

Passadore, Diego J.; Isoardi, Roberto A.; Gonzalez Nicolini, Federico J.; Ariza, P. P.; Novas, C. V.; Omati, S. A.

1998-07-01

Building cost effective PACS solutions is a main concern in developing countries. Hardware and software components are generally much more expensive than in developed countries and also more tightened financial constraints are the main reasons contributing to a slow rate of implementation of PACS. The extensive use of Internet for sharing resources and information has brought a broad number of freely available software packages to an ever-increasing number of users. In the field of medical imaging is possible to find image format conversion packages, DICOM compliant servers for all kinds of service classes, databases, web servers, image visualization, manipulation and analysis tools, etc. This paper describes a PACS implementation for review and storage built on freely available software. It currently integrates four diagnostic modalities (PET, CT, MR and NM), a Radiotherapy Treatment Planning workstation and several computers in a local area network, for image storage, database management and image review, processing and analysis. It also includes a web-based application that allows remote users to query the archive for studies from any workstation and to view the corresponding images and reports. We conclude that the advantage of using this approach is twofold. It allows a full understanding of all the issues involved in the implementation of a PACS and also contributes to keep costs down while enabling the development of a functional system for storage, distribution and review that can prove to be helpful for radiologists and referring physicians.

Automated Bulk Uploading of Images and Metadata to Flickr

ERIC Educational Resources Information Center

Michel, Jason Paul; Tzoc, Elias

2010-01-01

The Digital Initiatives department at Miami University, like most digital initiatives and special collections departments, has a large number of rich digital image collections, stored primarily in a third-party database. Typically, these databases are not findable to the average Web user. From a desire to expose these collections to the wider Web…

Common hyperspectral image database design

NASA Astrophysics Data System (ADS)

Tian, Lixun; Liao, Ningfang; Chai, Ali

2009-11-01

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

Reducing radiation dose to the female breast during conventional and dedicated breast computed tomography

NASA Astrophysics Data System (ADS)

Rupcich, Franco John

The purpose of this study was to quantify the effectiveness of techniques intended to reduce dose to the breast during CT coronary angiography (CTCA) scans with respect to task-based image quality, and to evaluate the effectiveness of optimal energy weighting in improving contrast-to-noise ratio (CNR), and thus the potential for reducing breast dose, during energy-resolved dedicated breast CT. A database quantifying organ dose for several radiosensitive organs irradiated during CTCA, including the breast, was generated using Monte Carlo simulations. This database facilitates estimation of organ-specific dose deposited during CTCA protocols using arbitrary x-ray spectra or tube-current modulation schemes without the need to run Monte Carlo simulations. The database was used to estimate breast dose for simulated CT images acquired for a reference protocol and five protocols intended to reduce breast dose. For each protocol, the performance of two tasks (detection of signals with unknown locations) was compared over a range of breast dose levels using a task-based, signal-detectability metric: the estimator of the area under the exponential free-response relative operating characteristic curve, AFE. For large-diameter/medium-contrast signals, when maintaining equivalent AFE, the 80 kV partial, 80 kV, 120 kV partial, and 120 kV tube-current modulated protocols reduced breast dose by 85%, 81%, 18%, and 6%, respectively, while the shielded protocol increased breast dose by 68%. Results for the small-diameter/high-contrast signal followed similar trends, but with smaller magnitude of the percent changes in dose. The 80 kV protocols demonstrated the greatest reduction to breast dose, however, the subsequent increase in noise may be clinically unacceptable. Tube output for these protocols can be adjusted to achieve more desirable noise levels with lesser dose reduction. The improvement in CNR of optimally projection-based and image-based weighted images relative to photon-counting was investigated for six different energy bin combinations using a bench-top energy-resolving CT system with a cadmium zinc telluride (CZT) detector. The non-ideal spectral response reduced the CNR for the projection-based weighted images, while image-based weighting improved CNR for five out of the six investigated bin combinations, despite this non-ideal response, indicating potential for image-based weighting to reduce breast dose during dedicated breast CT.

Applying manifold learning techniques to the CAESAR database

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Patrick, James; Arnold, Gregory; Ferrara, Matthew

2010-04-01

Understanding and organizing data is the first step toward exploiting sensor phenomenology for dismount tracking. What image features are good for distinguishing people and what measurements, or combination of measurements, can be used to classify the dataset by demographics including gender, age, and race? A particular technique, Diffusion Maps, has demonstrated the potential to extract features that intuitively make sense [1]. We want to develop an understanding of this tool by validating existing results on the Civilian American and European Surface Anthropometry Resource (CAESAR) database. This database, provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International, is a rich dataset which includes 40 traditional, anthropometric measurements of 4400 human subjects. If we could specifically measure the defining features for classification, from this database, then the future question will then be to determine a subset of these features that can be measured from imagery. This paper briefly describes the Diffusion Map technique, shows potential for dimension reduction of the CAESAR database, and describes interesting problems to be further explored.

Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

NASA Astrophysics Data System (ADS)

Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

2017-01-01

This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

An architecture for a brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.

2000-01-01

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

GPCALMA: A Tool For Mammography With A GRID-Connected Distributed Database

NASA Astrophysics Data System (ADS)

Bottigli, U.; Cerello, P.; Cheran, S.; Delogu, P.; Fantacci, M. E.; Fauci, F.; Golosio, B.; Lauria, A.; Lopez Torres, E.; Magro, R.; Masala, G. L.; Oliva, P.; Palmiero, R.; Raso, G.; Retico, A.; Stumbo, S.; Tangaro, S.

2003-09-01

The GPCALMA (Grid Platform for Computer Assisted Library for MAmmography) collaboration involves several departments of physics, INFN (National Institute of Nuclear Physics) sections, and italian hospitals. The aim of this collaboration is developing a tool that can help radiologists in early detection of breast cancer. GPCALMA has built a large distributed database of digitised mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) software which is integrated in a station that can also be used to acquire new images, as archive and to perform statistical analysis. The images (18×24 cm2, digitised by a CCD linear scanner with a 85 μm pitch and 4096 gray levels) are completely described: pathological ones have a consistent characterization with radiologist's diagnosis and histological data, non pathological ones correspond to patients with a follow up at least three years. The distributed database is realized throught the connection of all the hospitals and research centers in GRID tecnology. In each hospital local patients digital images are stored in the local database. Using GRID connection, GPCALMA will allow each node to work on distributed database data as well as local database data. Using its database the GPCALMA tools perform several analysis. A texture analysis, i.e. an automated classification on adipose, dense or glandular texture, can be provided by the system. GPCALMA software also allows classification of pathological features, in particular massive lesions (both opacities and spiculated lesions) analysis and microcalcification clusters analysis. The detection of pathological features is made using neural network software that provides a selection of areas showing a given "suspicion level" of lesion occurrence. The performance of the GPCALMA system will be presented in terms of the ROC (Receiver Operating Characteristic) curves. The results of GPCALMA system as "second reader" will also be presented.

Improving resolution of MR images with an adversarial network incorporating images with different contrast.

PubMed

Kim, Ki Hwan; Do, Won-Joon; Park, Sung-Hong

2018-05-04

The routine MRI scan protocol consists of multiple pulse sequences that acquire images of varying contrast. Since high frequency contents such as edges are not significantly affected by image contrast, down-sampled images in one contrast may be improved by high resolution (HR) images acquired in another contrast, reducing the total scan time. In this study, we propose a new deep learning framework that uses HR MR images in one contrast to generate HR MR images from highly down-sampled MR images in another contrast. The proposed convolutional neural network (CNN) framework consists of two CNNs: (a) a reconstruction CNN for generating HR images from the down-sampled images using HR images acquired with a different MRI sequence and (b) a discriminator CNN for improving the perceptual quality of the generated HR images. The proposed method was evaluated using a public brain tumor database and in vivo datasets. The performance of the proposed method was assessed in tumor and no-tumor cases separately, with perceptual image quality being judged by a radiologist. To overcome the challenge of training the network with a small number of available in vivo datasets, the network was pretrained using the public database and then fine-tuned using the small number of in vivo datasets. The performance of the proposed method was also compared to that of several compressed sensing (CS) algorithms. Incorporating HR images of another contrast improved the quantitative assessments of the generated HR image in reference to ground truth. Also, incorporating a discriminator CNN yielded perceptually higher image quality. These results were verified in regions of normal tissue as well as tumors for various MRI sequences from pseudo k-space data generated from the public database. The combination of pretraining with the public database and fine-tuning with the small number of real k-space datasets enhanced the performance of CNNs in in vivo application compared to training CNNs from scratch. The proposed method outperformed the compressed sensing methods. The proposed method can be a good strategy for accelerating routine MRI scanning. © 2018 American Association of Physicists in Medicine.

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

Shimizu, Y; Yoon, Y; Iwase, K

Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints thatmore » could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprints for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system because they are less affected by positioning differences during imaging.« less

Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection

NASA Technical Reports Server (NTRS)

Burl, M. C.; Fayyad, U. M.; Perona, P.; Smyth, P.

1995-01-01

Users of digital image libraries are often not interested in image data per se but in derived products such as catalogs of objects of interest. Converting an image database into a usable catalog is typically carried out manually at present. For many larger image databases the purely manual approach is completely impractical. In this paper we describe the development of a trainable cataloging system: the user indicates the location of the objects of interest for a number of training images and the system learns to detect and catalog these objects in the rest of the database. In particular we describe the application of this system to the cataloging of small volcanoes in radar images of Venus. The volcano problem is of interest because of the scale (30,000 images, order of 1 million detectable volcanoes), technical difficulty (the variability of the volcanoes in appearance) and the scientific importance of the problem. The problem of uncertain or subjective ground truth is of fundamental importance in cataloging problems of this nature and is discussed in some detail. Experimental results are presented which quantify and compare the detection performance of the system relative to human detection performance. The paper concludes by discussing the limitations of the proposed system and the lessons learned of general relevance to the development of digital image libraries.

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

PubMed Central

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

2014-01-01

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

Database integration of protocol-specific neurological imaging datasets

PubMed Central

Pacurar, Emil E.; Sethi, Sean K.; Habib, Charbel; Laze, Marius O.; Martis-Laze, Rachel; Haacke, E. Mark

2016-01-01

For many years now, Magnetic Resonance Innovations (MR Innovations), a magnetic resonance imaging (MRI) software development, technology, and research company, has been aggregating a multitude of MRI data from different scanning sites through its collaborations and research contracts. The majority of the data has adhered to neuroimaging protocols developed by our group which has helped ensure its quality and consistency. The protocols involved include the study of: traumatic brain injury, extracranial venous imaging for multiple sclerosis and Parkinson's disease, and stroke. The database has proven invaluable in helping to establish disease biomarkers, validate findings across multiple data sets, develop and refine signal processing algorithms, and establish both public and private research collaborations. Myriad Masters and PhD dissertations have been possible thanks to the availability of this database. As an example of a project that cuts across diseases, we have used the data and specialized software to develop new guidelines for detecting cerebral microbleeds. Ultimately, the database has been vital in our ability to provide tools and information for researchers and radiologists in diagnosing their patients, and we encourage collaborations and welcome sharing of similar data in this database. PMID:25959660

Creating a histology-embryology free digital image database using high-end microscopy and computer techniques for on-line biomedical education.

PubMed

Silva-Lopes, Victor W; Monteiro-Leal, Luiz H

2003-07-01

The development of new technology and the possibility of fast information delivery by either Internet or Intranet connections are changing education. Microanatomy education depends basically on the correct interpretation of microscopy images by students. Modern microscopes coupled to computers enable the presentation of these images in a digital form by creating image databases. However, the access to this new technology is restricted entirely to those living in cities and towns with an Information Technology (IT) infrastructure. This study describes the creation of a free Internet histology database composed by high-quality images and also presents an inexpensive way to supply it to a greater number of students through Internet/Intranet connections. By using state-of-the-art scientific instruments, we developed a Web page (http://www2.uerj.br/~micron/atlas/atlasenglish/index.htm) that, in association with a multimedia microscopy laboratory, intends to help in the reduction of the IT educational gap between developed and underdeveloped regions. Copyright 2003 Wiley-Liss, Inc.

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

NASA Technical Reports Server (NTRS)

Worrall, Diana M. (Editor); Biemesderfer, Chris (Editor); Barnes, Jeannette (Editor)

1992-01-01

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

Exploring Human Cognition Using Large Image Databases.

PubMed

Griffiths, Thomas L; Abbott, Joshua T; Hsu, Anne S

2016-07-01

Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories. Copyright © 2016 Cognitive Science Society, Inc.

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

PubMed

Seoud, Lama; Hurtut, Thomas; Chelbi, Jihed; Cheriet, Farida; Langlois, J M Pierre

2016-04-01

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

Building a medical image processing algorithm verification database

NASA Astrophysics Data System (ADS)

Brown, C. Wayne

2000-06-01

The design of a database containing head Computed Tomography (CT) studies is presented, along with a justification for the database's composition. The database will be used to validate software algorithms that screen normal head CT studies from studies that contain pathology. The database is designed to have the following major properties: (1) a size sufficient for statistical viability, (2) inclusion of both normal (no pathology) and abnormal scans, (3) inclusion of scans due to equipment malfunction, technologist error, and uncooperative patients, (4) inclusion of data sets from multiple scanner manufacturers, (5) inclusion of data sets from different gender and age groups, and (6) three independent diagnosis of each data set. Designed correctly, the database will provide a partial basis for FDA (United States Food and Drug Administration) approval of image processing algorithms for clinical use. Our goal for the database is the proof of viability of screening head CT's for normal anatomy using computer algorithms. To put this work into context, a classification scheme for 'computer aided diagnosis' systems is proposed.

Study of Automatic Image Rectification and Registration of Scanned Historical Aerial Photographs

NASA Astrophysics Data System (ADS)

Chen, H. R.; Tseng, Y. H.

2016-06-01

Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS) of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform) for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus) to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

The reference ballistic imaging database revisited.

PubMed

De Ceuster, Jan; Dujardin, Sylvain

2015-03-01

A reference ballistic image database (RBID) contains images of cartridge cases fired in firearms that are in circulation: a ballistic fingerprint database. The performance of an RBID was investigated a decade ago by De Kinder et al. using IBIS(®) Heritage™ technology. The results of that study were published in this journal, issue 214. Since then, technologies have evolved quite significantly and novel apparatus have become available on the market. The current research article investigates the efficiency of another automated ballistic imaging system, Evofinder(®) using the same database as used by De Kinder et al. The results demonstrate a significant increase in correlation efficiency: 38% of all matches were on first position of the Evofinder correlation list in comparison to IBIS(®) Heritage™ where only 19% were on the first position. Average correlation times are comparable to the IBIS(®) Heritage™ system. While Evofinder(®) demonstrates specific improvement for mutually correlating different ammunition brands, ammunition dependence of the markings is still strongly influencing the correlation result because the markings may vary considerably. As a consequence a great deal of potential hits (36%) was still far down in the correlation lists (positions 31 and lower). The large database was used to examine the probability of finding a match as a function of correlation list verification. As an example, the RBID study on Evofinder(®) demonstrates that to find at least 90% of all potential matches, at least 43% of the items in the database need to be compared on screen and this for breech face markings and firing pin impression separately. These results, although a clear improvement to the original RBID study, indicate that the implementation of such a database should still not be considered nowadays. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Observational Mishaps - a Database

NASA Astrophysics Data System (ADS)

von Braun, K.; Chiboucas, K.; Hurley-Keller, D.

1999-05-01

We present a World-Wide-Web-accessible database of astronomical images which suffer from a variety of observational problems. These problems range from common phenomena, such as dust grains on filters and/or dewar window, to more exotic cases like, for instance, deflated support airbags underneath the primary mirror. The purpose of this database is to enable astronomers at telescopes to save telescope time by discovering the nature of the trouble they might be experiencing with the help of this online catalog. Every observational mishap contained in this collection is presented in the form of a GIF image, a brief explanation of the problem, and, to the extent possible, a suggestion of what might be done to solve the problem and improve the image quality.

Comparison of conventional and cadmium-zinc-telluride single-photon emission computed tomography for analysis of thallium-201 myocardial perfusion imaging: an exploratory study in normal databases for different ethnicities.

PubMed

Ishihara, Masaru; Onoguchi, Masahisa; Taniguchi, Yasuyo; Shibutani, Takayuki

2017-12-01

The aim of this study was to clarify the differences in thallium-201-chloride (thallium-201) myocardial perfusion imaging (MPI) scans evaluated by conventional anger-type single-photon emission computed tomography (conventional SPECT) versus cadmium-zinc-telluride SPECT (CZT SPECT) imaging in normal databases for different ethnic groups. MPI scans from 81 consecutive Japanese patients were examined using conventional SPECT and CZT SPECT and analyzed with the pre-installed quantitative perfusion SPECT (QPS) software. We compared the summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) for the two SPECT devices. For a normal MPI reference, we usually use Japanese databases for MPI created by the Japanese Society of Nuclear Medicine, which can be used with conventional SPECT but not with CZT SPECT. In this study, we used new Japanese normal databases constructed in our institution to compare conventional and CZT SPECT. Compared with conventional SPECT, CZT SPECT showed lower SSS (p < 0.001), SRS (p = 0.001), and SDS (p = 0.189) using the pre-installed SPECT database. In contrast, CZT SPECT showed no significant difference from conventional SPECT in QPS analysis using the normal databases from our institution. Myocardial perfusion analyses by CZT SPECT should be evaluated using normal databases based on the ethnic group being evaluated.

Assistive technology for ultrasound-guided central venous catheter placement.

PubMed

Ikhsan, Mohammad; Tan, Kok Kiong; Putra, Andi Sudjana

2018-01-01

This study evaluated the existing technology used to improve the safety and ease of ultrasound-guided central venous catheterization. Electronic database searches were conducted in Scopus, IEEE, Google Patents, and relevant conference databases (SPIE, MICCAI, and IEEE conferences) for related articles on assistive technology for ultrasound-guided central venous catheterization. A total of 89 articles were examined and pointed to several fields that are currently the focus of improvements to ultrasound-guided procedures. These include improving needle visualization, needle guides and localization technology, image processing algorithms to enhance and segment important features within the ultrasound image, robotic assistance using probe-mounted manipulators, and improving procedure ergonomics through in situ projections of important information. Probe-mounted robotic manipulators provide a promising avenue for assistive technology developed for freehand ultrasound-guided percutaneous procedures. However, there is currently a lack of clinical trials to validate the effectiveness of these devices.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, X.

2013-07-01

The China Near Earth Object Survey Telescope (CNEOST) is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgradation of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

Design and Implementation of CNEOST Image Database Based on NoSQL System

NASA Astrophysics Data System (ADS)

Wang, Xin

2014-04-01

The China Near Earth Object Survey Telescope is the largest Schmidt telescope in China, and it has acquired more than 3 TB astronomical image data since it saw the first light in 2006. After the upgrade of the CCD camera in 2013, over 10 TB data will be obtained every year. The management of the massive images is not only an indispensable part of data processing pipeline but also the basis of data sharing. Based on the analysis of requirement, an image management system is designed and implemented by employing the non-relational database.

CB Database: A change blindness database for objects in natural indoor scenes.

PubMed

Sareen, Preeti; Ehinger, Krista A; Wolfe, Jeremy M

2016-12-01

Change blindness has been a topic of interest in cognitive sciences for decades. Change detection experiments are frequently used for studying various research topics such as attention and perception. However, creating change detection stimuli is tedious and there is no open repository of such stimuli using natural scenes. We introduce the Change Blindness (CB) Database with object changes in 130 colored images of natural indoor scenes. The size and eccentricity are provided for all the changes as well as reaction time data from a baseline experiment. In addition, we have two specialized satellite databases that are subsets of the 130 images. In one set, changes are seen in rooms or in mirrors in those rooms (Mirror Change Database). In the other, changes occur in a room or out a window (Window Change Database). Both the sets have controlled background, change size, and eccentricity. The CB Database is intended to provide researchers with a stimulus set of natural scenes with defined stimulus parameters that can be used for a wide range of experiments. The CB Database can be found at http://search.bwh.harvard.edu/new/CBDatabase.html .

Unified framework for automated iris segmentation using distantly acquired face images.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2012-09-01

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data

NASA Astrophysics Data System (ADS)

Ali, Abder-Rahman A.; Deserno, Thomas M.

2012-02-01

Malignant melanoma is the third most frequent type of skin cancer and one of the most malignant tumors, accounting for 79% of skin cancer deaths. Melanoma is highly curable if diagnosed early and treated properly as survival rate varies between 15% and 65% from early to terminal stages, respectively. So far, melanoma diagnosis is depending subjectively on the dermatologist's expertise. Computer-aided diagnosis (CAD) systems based on epiluminescense light microscopy can provide an objective second opinion on pigmented skin lesions (PSL). This work systematically analyzes the evidence of the effectiveness of automated melanoma detection in images from a dermatoscopic device. Automated CAD applications were analyzed to estimate their diagnostic outcome. Searching online databases for publication dates between 1985 and 2011, a total of 182 studies on dermatoscopic CAD were found. With respect to the systematic selection criterions, 9 studies were included, published between 2002 and 2011. Those studies formed databases of 14,421 dermatoscopic images including both malignant "melanoma" and benign "nevus", with 8,110 images being available ranging in resolution from 150 x 150 to 1568 x 1045 pixels. Maximum and minimum of sensitivity and specificity are 100.0% and 80.0% as well as 98.14% and 61.6%, respectively. Area under the receiver operator characteristics (AUC) and pooled sensitivity, specificity and diagnostics odds ratio are respectively 0.87, 0.90, 0.81, and 15.89. So, although that automated melanoma detection showed good accuracy in terms of sensitivity, specificity, and AUC, but diagnostic performance in terms of DOR was found to be poor. This might be due to the lack of dermatoscopic image resources (ground truth) that are needed for comprehensive assessment of diagnostic performance. In future work, we aim at testing this hypothesis by joining dermatoscopic images into a unified database that serves as a standard reference for dermatology related research in PSL classification.

Saada: A Generator of Astronomical Database

NASA Astrophysics Data System (ADS)

Michel, L.

2011-11-01

Saada transforms a set of heterogeneous FITS files or VOtables of various categories (images, tables, spectra, etc.) in a powerful database deployed on the Web. Databases are located on your host and stay independent of any external server. This job doesn’t require writing code. Saada can mix data of various categories in multiple collections. Data collections can be linked each to others making relevant browsing paths and allowing data-mining oriented queries. Saada supports 4 VO services (Spectra, images, sources and TAP) . Data collections can be published immediately after the deployment of the Web interface.

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.

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.

Doctoral theses in diagnostic imaging: a study of Spanish production between 1976 and 2011.

PubMed

Machan, K; Sendra Portero, F

2018-05-15

To analyze the production of doctoral theses in diagnostic imaging in Spain in the period comprising 1976 through 2011 with the aim of a) determining the number of theses and their distribution over time, b) describing the production in terms of universities and directors, and c) analyzing the content of the theses according to the imaging technique, anatomic site, and type of research used. The TESEO database was searched for "radiología" and/or "diagnóstico por imagen" and for terms related to diagnostic imaging in the title of the thesis. A total of 1036 theses related to diagnostic imaging were produced in 37 Spanish universities (mean, 29.6 theses/year; range, 4-59). A total of 963 thesis directors were identified; 10 of these supervised 10 or more theses. Most candidates and directors were men, although since the 2000-2001 academic year the number of male and female candidates has been similar. The anatomic regions most often included in diagnostic imaging theses were the abdomen (22.5%), musculoskeletal system (21.8%), central nervous system (16.4%), and neck and face (15.6%). The imaging techniques most often included were ultrasonography in the entire period (25.5%) and magnetic resonance imaging in the last 5 years. Most theses (63.8%) were related to clinical research. Despite certain limitations, the TESEO database makes it possible to analyze the production of doctoral theses in Spain effectively. The annual mean production of theses in diagnostic imaging is higher than in other medical specialties. This analysis reflects the historic evolution of imaging techniques and research in radiology as well as the development of Spanish universities. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

NASA IMAGESEER: NASA IMAGEs for Science, Education, Experimentation and Research

NASA Technical Reports Server (NTRS)

Le Moigne, Jacqueline; Grubb, Thomas G.; Milner, Barbara C.

2012-01-01

A number of web-accessible databases, including medical, military or other image data, offer universities and other users the ability to teach or research new Image Processing techniques on relevant and well-documented data. However, NASA images have traditionally been difficult for researchers to find, are often only available in hard-to-use formats, and do not always provide sufficient context and background for a non-NASA Scientist user to understand their content. The new IMAGESEER (IMAGEs for Science, Education, Experimentation and Research) database seeks to address these issues. Through a graphically-rich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image data for teaching or validating new Image Processing algorithms. As such, IMAGESEER fosters collaboration between NASA and research organizations while simultaneously encouraging development of new and enhanced Image Processing algorithms. The first prototype includes a representative sampling of NASA multispectral and hyperspectral images from several Earth Science instruments, along with a few small tutorials. Image processing techniques are currently represented with cloud detection, image registration, and map cover/classification. For each technique, corresponding data are selected from four different geographic regions, i.e., mountains, urban, water coastal, and agriculture areas. Satellite images have been collected from several instruments - Landsat-5 and -7 Thematic Mappers, Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). After geo-registration, these images are available in simple common formats such as GeoTIFF and raw formats, along with associated benchmark data.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

Word-addressable holographic memory using symbolic substitution and SLRs

NASA Astrophysics Data System (ADS)

McAulay, Alastair D.; Wang, Junqing

1990-12-01

A heteroassociative memory is proposed that allows a key word in a dictionary of key words to be used to recall an associated holographic image in a database of images. Symbolic substitution search finds the word sought in a dictionary of key words and generates a beam that selects the corresponding holographic image from a directory of images. In this case, symbolic substitution is used to orthogonalize the key words. Spatial light rebroadcasters are proposed for the key word database. Experimental results demonstrate that symbolic substitution will enable a holographic image to be selected and reconstructed. In the case considered, a holographic image having over 40,000-bits is selected out of eight by using a key word from a dictionary of eight words.

Microvax-based data management and reduction system for the regional planetary image facilities

NASA Technical Reports Server (NTRS)

Arvidson, R.; Guinness, E.; Slavney, S.; Weiss, B.

1987-01-01

Presented is a progress report for the Regional Planetary Image Facilities (RPIF) prototype image data management and reduction system being jointly implemented by Washington University and the USGS, Flagstaff. The system will consist of a MicroVAX with a high capacity (approx 300 megabyte) disk drive, a compact disk player, an image display buffer, a videodisk player, USGS image processing software, and SYSTEM 1032 - a commercial relational database management package. The USGS, Flagstaff, will transfer their image processing software including radiometric and geometric calibration routines, to the MicroVAX environment. Washington University will have primary responsibility for developing the database management aspects of the system and for integrating the various aspects into a working system.

Recent Trends in Imaging Use in Hospital Settings: Implications for Future Planning.

PubMed

Levin, David C; Parker, Laurence; Rao, Vijay M

2017-03-01

To compare trends in utilization rates of imaging in the three hospital-based settings where imaging is conducted. The nationwide Medicare Part B databases for 2004-2014 were used. All discretionary noninvasive diagnostic imaging (NDI) CPT codes were selected and grouped by modality. Procedure volumes of each code were available from the databases and converted to utilization rates per 1,000 Medicare enrollees. Medicare's place-of-service codes were used to identify imaging examinations done in hospital inpatients, hospital outpatient departments (HOPDs), and emergency departments (EDs). Trends were observed over the life of the study. Trendlines were strongly affected by code bundling in echocardiography in 2009, nuclear imaging in 2010, and CT in 2011. However, even aside from these artifactual effects, important trends could be discerned. Inpatient imaging utilization rates of all modalities are trending downward. In HOPDs, the utilization rate of conventional radiographic examinations (CREs) is declining but rates of CT, MRI, echocardiography, and noncardiac ultrasound (US) are increasing. In EDs, utilization rates of CREs, CT, and US are increasing. In the 3 years after 2011, when no further code bundling occurred, the total inpatient NDI utilization rate dropped 15%, whereas the rate in EDs increased 12% and that in HOPDs increased 1%. The trends in utilization of NDI in the three hospital-based settings where imaging occurs are distinctly different. Radiologists and others who are involved in deciding what kinds of equipment to purchase and where to locate it should be cognizant of these trends in making their decisions. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

ScienceDirect through SciVerse: a new way to approach Elsevier.

PubMed

Bengtson, Jason

2011-01-01

SciVerse is the new combined portal from Elsevier that services their ScienceDirect collection, SciTopics, and their Scopus database. Using SciVerse to access ScienceDirect is the specific focus of this review. Along with advanced keyword searching and citation searching options, SciVerse also incorporates a very useful image search feature. The aim seems to be not only to create an interface that provides broad functionality on par with other database search tools that many searchers use regularly but also to create an open platform that could be changed to respond effectively to the needs of customers.

Providing image management and communication functionality as an integral part of an existing hospital information system

NASA Astrophysics Data System (ADS)

Dayhoff, Ruth E.; Maloney, Daniel L.

1990-08-01

The effective delivery of health care has become increasingly dependent on a wide range of medical data which includes a variety of images. Manual and computer-based medical records ordinarily do not contain image data, leaving the physician to deal with a fragmented patient record widely scattered throughout the hospital. The Department of Veterans Affairs (VA) is currently installing a prototype hospital information system (HIS) workstation network to demonstrate the feasibility of providing image management and communications (IMAC) functionality as an integral part of an existing hospital information system. The core of this system is a database management system adapted to handle images as a new data type. A general model for this integration is discussed and specifics of the hospital-wide network of image display workstations are given.

Empirical study on neural network based predictive techniques for automatic number plate recognition

NASA Astrophysics Data System (ADS)

Shashidhara, M. S.; Indrakumar, S. S.

2011-10-01

The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

Wavelet domain textual coding of Ottoman script images

NASA Astrophysics Data System (ADS)

Gerek, Oemer N.; Cetin, Enis A.; Tewfik, Ahmed H.

1996-02-01

Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.

Image-based computer-assisted diagnosis system for benign paroxysmal positional vertigo

NASA Astrophysics Data System (ADS)

Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu

2005-04-01

We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.

An effective model for store and retrieve big health data in cloud computing.

PubMed

Goli-Malekabadi, Zohreh; Sargolzaei-Javan, Morteza; Akbari, Mohammad Kazem

2016-08-01

The volume of healthcare data including different and variable text types, sounds, and images is increasing day to day. Therefore, the storage and processing of these data is a necessary and challenging issue. Generally, relational databases are used for storing health data which are not able to handle the massive and diverse nature of them. This study aimed at presenting the model based on NoSQL databases for the storage of healthcare data. Despite different types of NoSQL databases, document-based DBs were selected by a survey on the nature of health data. The presented model was implemented in the Cloud environment for accessing to the distribution properties. Then, the data were distributed on the database by applying the Shard property. The efficiency of the model was evaluated in comparison with the previous data model, Relational Database, considering query time, data preparation, flexibility, and extensibility parameters. The results showed that the presented model approximately performed the same as SQL Server for "read" query while it acted more efficiently than SQL Server for "write" query. Also, the performance of the presented model was better than SQL Server in the case of flexibility, data preparation and extensibility. Based on these observations, the proposed model was more effective than Relational Databases for handling health data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Gong, Yihong; Zhang, HongJiang

1994-03-01

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

Image superresolution of cytology images using wavelet based patch search

NASA Astrophysics Data System (ADS)

Vargas, Carlos; García-Arteaga, Juan D.; Romero, Eduardo

2015-01-01

Telecytology is a new research area that holds the potential of significantly reducing the number of deaths due to cervical cancer in developing countries. This work presents a novel super-resolution technique that couples high and low frequency information in order to reduce the bandwidth consumption of cervical image transmission. The proposed approach starts by decomposing into wavelets the high resolution images and transmitting only the lower frequency coefficients. The transmitted coefficients are used to reconstruct an image of the original size. Additional details are added by iteratively replacing patches of the wavelet reconstructed image with equivalent high resolution patches from a previously acquired image database. Finally, the original transmitted low frequency coefficients are used to correct the final image. Results show a higher signal to noise ratio in the proposed method over simply discarding high frequency wavelet coefficients or replacing directly down-sampled patches from the image-database.

Passenger baggage object database (PBOD)

NASA Astrophysics Data System (ADS)

Gittinger, Jaxon M.; Suknot, April N.; Jimenez, Edward S.; Spaulding, Terry W.; Wenrich, Steve A.

2018-04-01

Detection of anomalies of interest in x-ray images is an ever-evolving problem that requires the rapid development of automatic detection algorithms. Automatic detection algorithms are developed using machine learning techniques, which would require developers to obtain the x-ray machine that was used to create the images being trained on, and compile all associated metadata for those images by hand. The Passenger Baggage Object Database (PBOD) and data acquisition application were designed and developed for acquiring and persisting 2-D and 3-D x-ray image data and associated metadata. PBOD was specifically created to capture simulated airline passenger "stream of commerce" luggage data, but could be applied to other areas of x-ray imaging to utilize machine-learning methods.

Image Display in Local Database Networks

NASA Astrophysics Data System (ADS)

List, James S.; Olson, Frederick R.

1989-05-01

Dearchival of image data in the form of x-ray film provides a major challenge for radiology departments. In highly active referral environments such as tertiary care hospitals, patients may be referred to multiple clinical subspecialists within a very short time. Each clinical subspecialist frequently requires diagnostic image data to complete the diagnosis. This need for image access often interferes with the normal process of film handling and interpretation, subsequently reducing the efficiency of the department. The concept of creating a local image database on individual nursing stations utilizing the AT&T CommView Results Viewing Station (RVS) is being evaluated. Initial physician acceptance has been favorable. Objective measurements of operational productivity enhancements are in progress.

Life After Press: The Role of the Picture Library in Communicating Astronomy to the Public

NASA Astrophysics Data System (ADS)

Evans, G. S.

2005-12-01

Science communication is increasingly led by the image, providing opportunities for 'visual' disciplines such as astronomy to receive greater public exposure. In consequence, there is a huge demand for good and exciting images within the publishing media. The picture library is a conduit linking image makers of all kinds to image buyers of all kinds. The image maker benefits from the exposure of their pictures to the people who want to use them, with minimal time investment, and with the safeguards of effective rights management. The image buyer benefits from a wide choice of images available from a single point of contact, stored in a database that offers a choice between subject-based and conceptual searches. By forming this link between astronomer, professional or amateur, and the publishing media, the picture library helps to make the wonders of astronomy visible to a wider public audience.

FCDD: A Database for Fruit Crops Diseases.

PubMed

Chauhan, Rupal; Jasrai, Yogesh; Pandya, Himanshu; Chaudhari, Suman; Samota, Chand Mal

2014-01-01

Fruit Crops Diseases Database (FCDD) requires a number of biotechnology and bioinformatics tools. The FCDD is a unique bioinformatics resource that compiles information about 162 details on fruit crops diseases, diseases type, its causal organism, images, symptoms and their control. The FCDD contains 171 phytochemicals from 25 fruits, their 2D images and their 20 possible sequences. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, textbooks and scientific journals. FCDD is fully searchable and supports extensive text search. The main focus of the FCDD is on providing possible information of fruit crops diseases, which will help in discovery of potential drugs from one of the common bioresource-fruits. The database was developed using MySQL. The database interface is developed in PHP, HTML and JAVA. FCDD is freely available. http://www.fruitcropsdd.com/

The NOAO Data Lab PHAT Photometry Database

NASA Astrophysics Data System (ADS)

Olsen, Knut; Williams, Ben; Fitzpatrick, Michael; PHAT Team

2018-01-01

We present a database containing both the combined photometric object catalog and the single epoch measurements from the Panchromatic Hubble Andromeda Treasury (PHAT). This database is hosted by the NOAO Data Lab (http://datalab.noao.edu), and as such exposes a number of data services to the PHAT photometry, including access through a Table Access Protocol (TAP) service, direct PostgreSQL queries, web-based and programmatic query interfaces, remote storage space for personal database tables and files, and a JupyterHub-based Notebook analysis environment, as well as image access through a Simple Image Access (SIA) service. We show how the Data Lab database and Jupyter Notebook environment allow for straightforward and efficient analyses of PHAT catalog data, including maps of object density, depth, and color, extraction of light curves of variable objects, and proper motion exploration.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

The Tromso Infant Faces Database (TIF): Development, Validation and Application to Assess Parenting Experience on Clarity and Intensity Ratings.

PubMed

Maack, Jana K; Bohne, Agnes; Nordahl, Dag; Livsdatter, Lina; Lindahl, Åsne A W; Øvervoll, Morten; Wang, Catharina E A; Pfuhl, Gerit

2017-01-01

Newborns and infants are highly depending on successfully communicating their needs; e.g., through crying and facial expressions. Although there is a growing interest in the mechanisms of and possible influences on the recognition of facial expressions in infants, heretofore there exists no validated database of emotional infant faces. In the present article we introduce a standardized and freely available face database containing Caucasian infant face images from 18 infants 4 to 12 months old. The development and validation of the Tromsø Infant Faces (TIF) database is presented in Study 1. Over 700 adults categorized the photographs by seven emotion categories (happy, sad, disgusted, angry, afraid, surprised, neutral) and rated intensity, clarity and their valance. In order to examine the relevance of TIF, we then present its first application in Study 2, investigating differences in emotion recognition across different stages of parenthood. We found a small gender effect in terms of women giving higher intensity and clarity ratings than men. Moreover, parents of young children rated the images as clearer than all the other groups, and parents rated "neutral" expressions as more clearly and more intense. Our results suggest that caretaking experience provides an implicit advantage in the processing of emotional expressions in infant faces, especially for the more difficult, ambiguous expressions.

Effect of microstructure on the elasto-viscoplastic deformation of dual phase titanium structures

NASA Astrophysics Data System (ADS)

Ozturk, Tugce; Rollett, Anthony D.

2018-02-01

The present study is devoted to the creation of a process-structure-property database for dual phase titanium alloys, through a synthetic microstructure generation method and a mesh-free fast Fourier transform based micromechanical model that operates on a discretized image of the microstructure. A sensitivity analysis is performed as a precursor to determine the statistically representative volume element size for creating 3D synthetic microstructures based on additively manufactured Ti-6Al-4V characteristics, which are further modified to expand the database for features of interest, e.g., lath thickness. Sets of titanium hardening parameters are extracted from literature, and The relative effect of the chosen microstructural features is quantified through comparisons of average and local field distributions.

Multimodality medical image database for temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

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

2003-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2003-01-01

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

The Cardiac Atlas Project--an imaging database for computational modeling and statistical atlases of the heart.

PubMed

Fonseca, Carissa G; Backhaus, Michael; Bluemke, David A; Britten, Randall D; Chung, Jae Do; Cowan, Brett R; Dinov, Ivo D; Finn, J Paul; Hunter, Peter J; Kadish, Alan H; Lee, Daniel C; Lima, Joao A C; Medrano-Gracia, Pau; Shivkumar, Kalyanam; Suinesiaputra, Avan; Tao, Wenchao; Young, Alistair A

2011-08-15

Integrative mathematical and statistical models of cardiac anatomy and physiology can play a vital role in understanding cardiac disease phenotype and planning therapeutic strategies. However, the accuracy and predictive power of such models is dependent upon the breadth and depth of noninvasive imaging datasets. The Cardiac Atlas Project (CAP) has established a large-scale database of cardiac imaging examinations and associated clinical data in order to develop a shareable, web-accessible, structural and functional atlas of the normal and pathological heart for clinical, research and educational purposes. A goal of CAP is to facilitate collaborative statistical analysis of regional heart shape and wall motion and characterize cardiac function among and within population groups. Three main open-source software components were developed: (i) a database with web-interface; (ii) a modeling client for 3D + time visualization and parametric description of shape and motion; and (iii) open data formats for semantic characterization of models and annotations. The database was implemented using a three-tier architecture utilizing MySQL, JBoss and Dcm4chee, in compliance with the DICOM standard to provide compatibility with existing clinical networks and devices. Parts of Dcm4chee were extended to access image specific attributes as search parameters. To date, approximately 3000 de-identified cardiac imaging examinations are available in the database. All software components developed by the CAP are open source and are freely available under the Mozilla Public License Version 1.1 (http://www.mozilla.org/MPL/MPL-1.1.txt). http://www.cardiacatlas.org a.young@auckland.ac.nz Supplementary data are available at Bioinformatics online.

An adaptive block-based fusion method with LUE-SSIM for multi-focus images

NASA Astrophysics Data System (ADS)

Zheng, Jianing; Guo, Yongcai; Huang, Yukun

2016-09-01

Because of the lenses' limited depth of field, digital cameras are incapable of acquiring an all-in-focus image of objects at varying distances in a scene. Multi-focus image fusion technique can effectively solve this problem. Aiming at the block-based multi-focus image fusion methods, the problem that blocking-artifacts often occurs. An Adaptive block-based fusion method based on lifting undistorted-edge structural similarity (LUE-SSIM) is put forward. In this method, image quality metrics LUE-SSIM is firstly proposed, which utilizes the characteristics of human visual system (HVS) and structural similarity (SSIM) to make the metrics consistent with the human visual perception. Particle swarm optimization(PSO) algorithm which selects LUE-SSIM as the object function is used for optimizing the block size to construct the fused image. Experimental results on LIVE image database shows that LUE-SSIM outperform SSIM on Gaussian defocus blur images quality assessment. Besides, multi-focus image fusion experiment is carried out to verify our proposed image fusion method in terms of visual and quantitative evaluation. The results show that the proposed method performs better than some other block-based methods, especially in reducing the blocking-artifact of the fused image. And our method can effectively preserve the undistorted-edge details in focus region of the source images.

Normal values and standardization of parameters in nuclear cardiology: Japanese Society of Nuclear Medicine working group database.

PubMed

Nakajima, Kenichi; Matsumoto, Naoya; Kasai, Tokuo; Matsuo, Shinro; Kiso, Keisuke; Okuda, Koichi

2016-04-01

As a 2-year project of the Japanese Society of Nuclear Medicine working group activity, normal myocardial imaging databases were accumulated and summarized. Stress-rest with gated and non-gated image sets were accumulated for myocardial perfusion imaging and could be used for perfusion defect scoring and normal left ventricular (LV) function analysis. For single-photon emission computed tomography (SPECT) with multi-focal collimator design, databases of supine and prone positions and computed tomography (CT)-based attenuation correction were created. The CT-based correction provided similar perfusion patterns between genders. In phase analysis of gated myocardial perfusion SPECT, a new approach for analyzing dyssynchrony, normal ranges of parameters for phase bandwidth, standard deviation and entropy were determined in four software programs. Although the results were not interchangeable, dependency on gender, ejection fraction and volumes were common characteristics of these parameters. Standardization of (123)I-MIBG sympathetic imaging was performed regarding heart-to-mediastinum ratio (HMR) using a calibration phantom method. The HMRs from any collimator types could be converted to the value with medium-energy comparable collimators. Appropriate quantification based on common normal databases and standard technology could play a pivotal role for clinical practice and researches.

Self-aligning and compressed autosophy video databases

NASA Astrophysics Data System (ADS)

Holtz, Klaus E.

1993-04-01

Autosophy, an emerging new science, explains `self-assembling structures,' such as crystals or living trees, in mathematical terms. This research provides a new mathematical theory of `learning' and a new `information theory' which permits the growing of self-assembling data network in a computer memory similar to the growing of `data crystals' or `data trees' without data processing or programming. Autosophy databases are educated very much like a human child to organize their own internal data storage. Input patterns, such as written questions or images, are converted to points in a mathematical omni dimensional hyperspace. The input patterns are then associated with output patterns, such as written answers or images. Omni dimensional information storage will result in enormous data compression because each pattern fragment is only stored once. Pattern recognition in the text or image files is greatly simplified by the peculiar omni dimensional storage method. Video databases will absorb input images from a TV camera and associate them with textual information. The `black box' operations are totally self-aligning where the input data will determine their own hyperspace storage locations. Self-aligning autosophy databases may lead to a new generation of brain-like devices.

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 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.

Matching shapes with self-intersections: application to leaf classification.

PubMed

Mokhtarian, Farzin; Abbasi, Sadegh

2004-05-01

We address the problem of two-dimensional (2-D) shape representation and matching in presence of self-intersection for large image databases. This may occur when part of an object is hidden behind another part and results in a darker section in the gray level image of the object. The boundary contour of the object must include the boundary of this part which is entirely inside the outline of the object. The Curvature Scale Space (CSS) image of a shape is a multiscale organization of its inflection points as it is smoothed. The CSS-based shape representation method has been selected for MPEG-7 standardization. We study the effects of contour self-intersection on the Curvature Scale Space image. When there is no self-intersection, the CSS image contains several arch shape contours, each related to a concavity or a convexity of the shape. Self intersections create contours with minima as well as maxima in the CSS image. An efficient shape representation method has been introduced in this paper which describes a shape using the maxima as well as the minima of its CSS contours. This is a natural generalization of the conventional method which only includes the maxima of the CSS image contours. The conventional matching algorithm has also been modified to accommodate the new information about the minima. The method has been successfully used in a real world application to find, for an unknown leaf, similar classes from a database of classified leaf images representing different varieties of chrysanthemum. For many classes of leaves, self-intersection is inevitable during the scanning of the image. Therefore the original contributions of this paper is the generalization of the Curvature Scale Space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves.

Stereoscopic wide field of view imaging system

NASA Technical Reports Server (NTRS)

Prechtl, Eric F. (Inventor); Sedwick, Raymond J. (Inventor); Jonas, Eric M. (Inventor)

2011-01-01

A stereoscopic imaging system incorporates a plurality of imaging devices or cameras to generate a high resolution, wide field of view image database from which images can be combined in real time to provide wide field of view or panoramic or omni-directional still or video images.

DBMap: a TreeMap-based framework for data navigation and visualization of brain research registry

NASA Astrophysics Data System (ADS)

Zhang, Ming; Zhang, Hong; Tjandra, Donny; Wong, Stephen T. C.

2003-05-01

The purpose of this study is to investigate and apply a new, intuitive and space-conscious visualization framework to facilitate efficient data presentation and exploration of large-scale data warehouses. We have implemented the DBMap framework for the UCSF Brain Research Registry. Such a novel utility would facilitate medical specialists and clinical researchers in better exploring and evaluating a number of attributes organized in the brain research registry. The current UCSF Brain Research Registry consists of a federation of disease-oriented database modules, including Epilepsy, Brain Tumor, Intracerebral Hemorrphage, and CJD (Creuzfeld-Jacob disease). These database modules organize large volumes of imaging and non-imaging data to support Web-based clinical research. While the data warehouse supports general information retrieval and analysis, there lacks an effective way to visualize and present the voluminous and complex data stored. This study investigates whether the TreeMap algorithm can be adapted to display and navigate categorical biomedical data warehouse or registry. TreeMap is a space constrained graphical representation of large hierarchical data sets, mapped to a matrix of rectangles, whose size and color represent interested database fields. It allows the display of a large amount of numerical and categorical information in limited real estate of computer screen with an intuitive user interface. The paper will describe, DBMap, the proposed new data visualization framework for large biomedical databases. Built upon XML, Java and JDBC technologies, the prototype system includes a set of software modules that reside in the application server tier and provide interface to backend database tier and front-end Web tier of the brain registry.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

PubMed

Kostopoulos, Spiros A; Asvestas, Pantelis A; Kalatzis, Ioannis K; Sakellaropoulos, George C; Sakkis, Theofilos H; Cavouras, Dionisis A; Glotsos, Dimitris T

2017-09-01

The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical organelle dissection of Arabidopsis guard cells using image database LIPS.

PubMed

Higaki, Takumi; Kutsuna, Natsumaro; Hosokawa, Yoichiroh; Akita, Kae; Ebine, Kazuo; Ueda, Takashi; Kondo, Noriaki; Hasezawa, Seiichiro

2012-01-01

To comprehensively grasp cell biological events in plant stomatal movement, we have captured microscopic images of guard cells with various organelles markers. The 28,530 serial optical sections of 930 pairs of Arabidopsis guard cells have been released as a new image database, named Live Images of Plant Stomata (LIPS). We visualized the average organellar distributions in guard cells using probabilistic mapping and image clustering techniques. The results indicated that actin microfilaments and endoplasmic reticulum (ER) are mainly localized to the dorsal side and connection regions of guard cells. Subtractive images of open and closed stomata showed distribution changes in intracellular structures, including the ER, during stomatal movement. Time-lapse imaging showed that similar ER distribution changes occurred during stomatal opening induced by light irradiation or femtosecond laser shots on neighboring epidermal cells, indicating that our image analysis approach has identified a novel ER relocation in stomatal opening.

Geometric processing of digital images of the planets

NASA Technical Reports Server (NTRS)

Edwards, Kathleen

1987-01-01

New procedures and software have been developed for geometric transformation of images to support digital cartography of the planets. The procedures involve the correction of spacecraft camera orientation of each image with the use of ground control and the transformation of each image to a Sinusoidal Equal-Area map projection with an algorithm which allows the number of transformation calculations to vary as the distortion varies within the image. When the distortion is low in an area of an image, few transformation computations are required, and most pixels can be interpolated. When distortion is extreme, the location of each pixel is computed. Mosaics are made of these images and stored as digital databases. Completed Sinusoidal databases may be used for digital analysis and registration with other spatial data. They may also be reproduced as published image maps by digitally transforming them to appropriate map projections.

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Magnetic Resonance Imaging as an Adjunct to Mammography for Breast Cancer Screening in Women at Less Than High Risk for Breast Cancer: A Health Technology Assessment

PubMed Central

Nikitovic-Jokic, Milica; Holubowich, Corinne

2016-01-01

Background Screening with mammography can detect breast cancer early, before clinical symptoms appear. Some cancers, however, are not captured with mammography screening alone. Among women at high risk for breast cancer, magnetic resonance imaging (MRI) has been suggested as a safe adjunct (supplemental) screening tool that can detect breast cancers missed on screening mammography, potentially reducing the number of deaths associated with the disease. However, the use of adjunct screening tests may also increase the number of false-positive test results, which may lead to unnecessary follow-up testing, as well as patient stress and anxiety. We investigated the benefits and harms of MRI as an adjunct to mammography compared with mammography alone for screening women at less than high risk (average or higher than average risk) for breast cancer. Methods We searched Ovid MEDLINE, Ovid Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE), Centre for Reviews and Dissemination (CRD) Health Technology Assessment Database, and National Health Service (NHS) Economic Evaluation Database, from January 2002 to January 2016, for evidence of effectiveness, harms, and diagnostic accuracy. Only studies evaluating the use of screening breast MRI as an adjunct to mammography in the specified populations were included. Results No studies in women at less than high risk for breast cancer met our inclusion criteria. Conclusions It remains uncertain if the use of adjunct screening breast MRI in women at less than high risk (average or higher than average risk) for breast cancer will reduce breast cancer–related mortality without significant increases in unnecessary follow-up testing and treatment. PMID:27990198

Dual energy CT: How well can pseudo-monochromatic imaging reduce metal artifacts?

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

Kuchenbecker, Stefan, E-mail: stefan.kuchenbecker@dkfz.de; Faby, Sebastian; Sawall, Stefan

2015-02-15

Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient- and slice-dependent linear combination weights, e.g., E = 160 keV corresponds to α = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data-based processing. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energymore » to that linear combination. Those pseudo-monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data-based artifact correction methods. To quantify the artifact reduction potential of pseudo-monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140 kV Sn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data-based material decomposition in case of simulation, to raw data-based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast-to-noise ratio (CNR) was assessed. Results: In the simulation, the pseudo-monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact-reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data-based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo-monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo-monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data-based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible.« less

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

PubMed

Margolies, Laurie R; Pandey, Gaurav; Horowitz, Eliot R; Mendelson, David S

2016-02-01

The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging. The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

Automatic image database generation from CAD for 3D object recognition

NASA Astrophysics Data System (ADS)

Sardana, Harish K.; Daemi, Mohammad F.; Ibrahim, Mohammad K.

1993-06-01

The development and evaluation of Multiple-View 3-D object recognition systems is based on a large set of model images. Due to the various advantages of using CAD, it is becoming more and more practical to use existing CAD data in computer vision systems. Current PC- level CAD systems are capable of providing physical image modelling and rendering involving positional variations in cameras, light sources etc. We have formulated a modular scheme for automatic generation of various aspects (views) of the objects in a model based 3-D object recognition system. These views are generated at desired orientations on the unit Gaussian sphere. With a suitable network file sharing system (NFS), the images can directly be stored on a database located on a file server. This paper presents the image modelling solutions using CAD in relation to multiple-view approach. Our modular scheme for data conversion and automatic image database storage for such a system is discussed. We have used this approach in 3-D polyhedron recognition. An overview of the results, advantages and limitations of using CAD data and conclusions using such as scheme are also presented.

Image Databases.

ERIC Educational Resources Information Center

Pettersson, Rune

Different kinds of pictorial databases are described with respect to aims, user groups, search possibilities, storage, and distribution. Some specific examples are given for databases used for the following purposes: (1) labor markets for artists; (2) document management; (3) telling a story; (4) preservation (archives and museums); (5) research;…

Towards building high performance medical image management system for clinical trials

NASA Astrophysics Data System (ADS)

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

2011-03-01

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

False match elimination for face recognition based on SIFT algorithm

NASA Astrophysics Data System (ADS)

Gu, Xuyuan; Shi, Ping; Shao, Meide

2011-06-01

The SIFT (Scale Invariant Feature Transform) is a well known algorithm used to detect and describe local features in images. It is invariant to image scale, rotation and robust to the noise and illumination. In this paper, a novel method used for face recognition based on SIFT is proposed, which combines the optimization of SIFT, mutual matching and Progressive Sample Consensus (PROSAC) together and can eliminate the false matches of face recognition effectively. Experiments on ORL face database show that many false matches can be eliminated and better recognition rate is achieved.

Biometric Challenges for Future Deployments: A Study of the Impact of Geography, Climate, Culture, and Social Conditions on the Effective Collection of Biometrics

DTIC Science & Technology

2011-04-01

to iris or face recognition. In addition, breathing the dust is prevented with something covering the mouth , which will cause problems with face...headgear that “obscures the hair or hairline”. [58] One database of face images has images of people with and without scarves over their mouth . [59... Electronic Systems Magazine. Vol 19, Number 9, September 2004. [58] U.S. Department of State. How to Apply [for a Passport] in Person. [On-line

Soft computing approach to 3D lung nodule segmentation in CT.

PubMed

Badura, P; Pietka, E

2014-10-01

This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database. Copyright © 2014 Elsevier Ltd. All rights reserved.

LONI visualization environment.

PubMed

Dinov, Ivo D; Valentino, Daniel; Shin, Bae Cheol; Konstantinidis, Fotios; Hu, Guogang; MacKenzie-Graham, Allan; Lee, Erh-Fang; Shattuck, David; Ma, Jeff; Schwartz, Craig; Toga, Arthur W

2006-06-01

Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.

Digital Image Quality And Interpretability: Database And Hardcopy Studies

NASA Astrophysics Data System (ADS)

Snyder, H. L.; Maddox, M. E.; Shedivy, D. I.; Turpin, J. A.; Burke, J. J.; Strickland, R. N.

1982-02-01

Two hundred fifty transparencies, displaying a new digital database consisting of 25 degraded versions (5 blur levels x 5 noise levels) of each of 10 digitized, first-generation positive transparencies, were used in two experiments involving 15 trained military photointer-preters. Each image is 86 mm square and represents 40962 8-bit pixels. In the "interpretation" experiment, each photointerpreter (judge) spent approximately two days extracting essential elements of information (EEls) from one degraded version of each scene at a constant Gaussian blur level (FWHM = 40, 84, or 322 Am). In the scaling experiment, each judge assigned a numerical value to each of the 250 images, according to its perceived position on a 10-point NATO-standardized scale (0 = useless through 9 = nearly perfect), to the nearest 0.1 unit. Eighty-eight of the 100 possible values were used by the judges, indicating that 62 categories, based on the Shannon-Wiener measure of information, are needed to scale these hardcopy images. The overall correlation between the scaling and interpretation results was 0.9. Though the main effect of blur was not statistically significant in the interpretation experiment, that of noise was significant, and all main factors (blur, noise, scene, order of battle) and most interactions were statistically significant in the scaling experiment.

FaceWarehouse: a 3D facial expression database for visual computing.

PubMed

Cao, Chen; Weng, Yanlin; Zhou, Shun; Tong, Yiying; Zhou, Kun

2014-03-01

We present FaceWarehouse, a database of 3D facial expressions for visual computing applications. We use Kinect, an off-the-shelf RGBD camera, to capture 150 individuals aged 7-80 from various ethnic backgrounds. For each person, we captured the RGBD data of her different expressions, including the neutral expression and 19 other expressions such as mouth-opening, smile, kiss, etc. For every RGBD raw data record, a set of facial feature points on the color image such as eye corners, mouth contour, and the nose tip are automatically localized, and manually adjusted if better accuracy is required. We then deform a template facial mesh to fit the depth data as closely as possible while matching the feature points on the color image to their corresponding points on the mesh. Starting from these fitted face meshes, we construct a set of individual-specific expression blendshapes for each person. These meshes with consistent topology are assembled as a rank-3 tensor to build a bilinear face model with two attributes: identity and expression. Compared with previous 3D facial databases, for every person in our database, there is a much richer matching collection of expressions, enabling depiction of most human facial actions. We demonstrate the potential of FaceWarehouse for visual computing with four applications: facial image manipulation, face component transfer, real-time performance-based facial image animation, and facial animation retargeting from video to image.

Compound image segmentation of published biomedical figures.

PubMed

Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra; Shatkay, Hagit

2018-04-01

Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.

Success of HIS DICOM interfaces in the integration of the healthcare enterprise at the Department of Veterans Affairs

NASA Astrophysics Data System (ADS)

Kuzmak, Peter M.; Dayhoff, Ruth E.

1999-07-01

The US Department of Veterans Affairs (VA) is integrating imaging into the healthcare enterprise using the Digital Imaging and Communication in Medicine (DICOM) standard protocols. Image management is directly integrated into the VistA Hospital Information System (HIS) software and the clinical database. Radiology images are acquired via DICOM, and are stored directly in the HIS database. Images can be displayed on low-cost clinician's workstations throughout the medical center. High-resolution diagnostic quality multi-monitor VistA workstations with specialized viewing software can be used for reading radiology images. Two approaches are used to acquire and handle imags within the radiology department. Some sties have a commercial Picture Archiving and Communications System (PACS) interfaced to the VistA HIS, while other sites use the direct image acquisition and integrated diagnostic reading capabilities of VistA itself. A small set of DICOM services have been implemented by VistA to allow patient and study text data to be transmitted to image producing modalities and the commercial PACS, and to enable images and study data to be transferred back. The VistA DICOM capabilities are now used to interface seven different commercial PACS products and over twenty different radiology modalities. The communications capabilities of DICOM and the VA wide area network are begin used to support reading of radiology images form remote sites. DICOM has been the cornerstone in the ability to integrate imaging functionality into the Healthcare Enterprise. Because of its openness, it allows the integration of system component from commercial and non- commercial sources to work together to provide functional cost-effective solutions. As DICOM expands to non-radiology devices, integration must occur with the specialty information subsystems that handle orders and reports, their associated DICOM image capture systems, and the computer- based patient record. The mode and concepts of the DICOM standard can be extended to these other areas, but some adjustments may be required.

Development of a mobile emergency patient information and imaging communication system based on CDMA-1X EVDO

NASA Astrophysics Data System (ADS)

Yang, Keon Ho; Jung, Haijo; Kang, Won-Suk; Jang, Bong Mun; Kim, Joong Il; Han, Dong Hoon; Yoo, Sun-Kook; Yoo, Hyung-Sik; Kim, Hee-Joung

2006-03-01

The wireless mobile service with a high bit rate using CDMA-1X EVDO is now widely used in Korea. Mobile devices are also increasingly being used as the conventional communication mechanism. We have developed a web-based mobile system that communicates patient information and images, using CDMA-1X EVDO for emergency diagnosis. It is composed of a Mobile web application system using the Microsoft Windows 2003 server and an internet information service. Also, a mobile web PACS used for a database managing patient information and images was developed by using Microsoft access 2003. A wireless mobile emergency patient information and imaging communication system is developed by using Microsoft Visual Studio.NET, and JPEG 2000 ActiveX control for PDA phone was developed by using the Microsoft Embedded Visual C++. Also, the CDMA-1X EVDO is used for connections between mobile web servers and the PDA phone. This system allows fast access to the patient information database, storing both medical images and patient information anytime and anywhere. Especially, images were compressed into a JPEG2000 format and transmitted from a mobile web PACS inside the hospital to the radiologist using a PDA phone located outside the hospital. Also, this system shows radiological images as well as physiological signal data, including blood pressure, vital signs and so on, in the web browser of the PDA phone so radiologists can diagnose more effectively. Also, we acquired good results using an RW-6100 PDA phone used in the university hospital system of the Sinchon Severance Hospital in Korea.

Spatial and symbolic queries for 3D image data

NASA Astrophysics Data System (ADS)

Benson, Daniel C.; Zick, Gregory L.

1992-04-01

We present a query system for an object-oriented biomedical imaging database containing 3-D anatomical structures and their corresponding 2-D images. The graphical interface facilitates the formation of spatial queries, nonspatial or symbolic queries, and combined spatial/symbolic queries. A query editor is used for the creation and manipulation of 3-D query objects as volumes, surfaces, lines, and points. Symbolic predicates are formulated through a combination of text fields and multiple choice selections. Query results, which may include images, image contents, composite objects, graphics, and alphanumeric data, are displayed in multiple views. Objects returned by the query may be selected directly within the views for further inspection or modification, or for use as query objects in subsequent queries. Our image database query system provides visual feedback and manipulation of spatial query objects, multiple views of volume data, and the ability to combine spatial and symbolic queries. The system allows for incremental enhancement of existing objects and the addition of new objects and spatial relationships. The query system is designed for databases containing symbolic and spatial data. This paper discuses its application to data acquired in biomedical 3- D image reconstruction, but it is applicable to other areas such as CAD/CAM, geographical information systems, and computer vision.

Advanced Land Imager Assessment System

NASA Technical Reports Server (NTRS)

Chander, Gyanesh; Choate, Mike; Christopherson, Jon; Hollaren, Doug; Morfitt, Ron; Nelson, Jim; Nelson, Shar; Storey, James; Helder, Dennis; Ruggles, Tim;

Multimedia Database at National Museum of Ethnology

NASA Astrophysics Data System (ADS)

Sugita, Shigeharu

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

Analysis and Exchange of Multimedia Laboratory Data Using the Brain Database

PubMed Central

Wertheim, Steven L.

1990-01-01

Two principal goals of the Brain Database are: 1) to support laboratory data collection and analysis of multimedia information about the nervous system and 2) to support exchange of these data among researchers and clinicians who may be physically distant. This has been achieved by an implementation of experimental and clinical records within a relational database. An Image Series Editor has been created that provides a graphical interface to these data for the purposes of annotation, quantification and other analyses. Cooperating laboratories each maintain their own copies of the Brain Database to which they may add private data. Although the data in a given experimental or patient record will be distributed among many tables and external image files, the user can treat each record as a unit that can be extracted from the local database and sent to a distant colleague.

LDEF meteoroid and debris database

NASA Technical Reports Server (NTRS)

Dardano, C. B.; See, Thomas H.; Zolensky, Michael E.

1994-01-01

The Long Duration Exposure Facility (LDEF) Meteoroid and Debris Special Investigation Group (M&D SIG) database is maintained at the Johnson Space Center (JSC), Houston, Texas, and consists of five data tables containing information about individual features, digitized images of selected features, and LDEF hardware (i.e., approximately 950 samples) archived at JSC. About 4000 penetrations (greater than 300 micron in diameter) and craters (greater than 500 micron in diameter) were identified and photodocumented during the disassembly of LDEF at the Kennedy Space Center (KSC), while an additional 4500 or so have subsequently been characterized at JSC. The database also contains some data that have been submitted by various PI's, yet the amount of such data is extremely limited in its extent, and investigators are encouraged to submit any and all M&D-type data to JSC for inclusion within the M&D database. Digitized stereo-image pairs are available for approximately 4500 features through the database.

PhamDB: a web-based application for building Phamerator databases.

PubMed

Lamine, James G; DeJong, Randall J; Nelesen, Serita M

2016-07-01

PhamDB is a web application which creates databases of bacteriophage genes, grouped by gene similarity. It is backwards compatible with the existing Phamerator desktop software while providing an improved database creation workflow. Key features include a graphical user interface, validation of uploaded GenBank files, and abilities to import phages from existing databases, modify existing databases and queue multiple jobs. Source code and installation instructions for Linux, Windows and Mac OSX are freely available at https://github.com/jglamine/phage PhamDB is also distributed as a docker image which can be managed via Kitematic. This docker image contains the application and all third party software dependencies as a pre-configured system, and is freely available via the installation instructions provided. snelesen@calvin.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The Alaska Volcano Observatory Website a Tool for Information Management and Dissemination

NASA Astrophysics Data System (ADS)

Snedigar, S. F.; Cameron, C. E.; Nye, C. J.

2006-12-01

The Alaska Volcano Observatory's (AVO's) website served as a primary information management tool during the 2006 eruption of Augustine Volcano. The AVO website is dynamically generated from a database back- end. This system enabled AVO to quickly and easily update the website, and provide content based on user- queries to the database. During the Augustine eruption, the new AVO website was heavily used by members of the public (up to 19 million hits per day), and this was largely because the AVO public pages were an excellent source of up-to-date information. There are two different, yet fully integrated parts of the website. An external, public site (www.avo.alaska.edu) allows the general public to track eruptive activity by viewing the latest photographs, webcam images, webicorder graphs, and official information releases about activity at the volcano, as well as maps, previous eruption information, bibliographies, and rich information about other Alaska volcanoes. The internal half of the website hosts diverse geophysical and geological data (as browse images) in a format equally accessible by AVO staff in different locations. In addition, an observation log allows users to enter information about anything from satellite passes to seismic activity to ash fall reports into a searchable database. The individual(s) on duty at the watch office use forms on the internal website to post a summary of the latest activity directly to the public website, ensuring that the public website is always up to date. The internal website also serves as a starting point for monitoring Alaska's volcanoes. AVO's extensive image database allows AVO personnel to upload many photos, diagrams, and videos which are then available to be browsed by anyone in the AVO community. Selected images are viewable from the public page. The primary webserver is housed at the University of Alaska Fairbanks, and holds a MySQL database with over 200 tables and several thousand lines of php code gluing the database and website together. The database currently holds 95 GB of data. Webcam images and webicorder graphs are pulled from servers in Anchorage every few minutes. Other servers in Fairbanks generate earthquake location plots and spectrograms.

Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.

PubMed

Wang, Jingjing; Sun, Tao; Gao, Ni; Menon, Desmond Dev; Luo, Yanxia; Gao, Qi; Li, Xia; Wang, Wei; Zhu, Huiping; Lv, Pingxin; Liang, Zhigang; Tao, Lixin; Liu, Xiangtong; Guo, Xiuhua

2014-01-01

To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.

Reduced reference image quality assessment via sub-image similarity based redundancy measurement

NASA Astrophysics Data System (ADS)

Mou, Xuanqin; Xue, Wufeng; Zhang, Lei

2012-03-01

The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.

Organization and dissemination of multimedia medical databases on the WWW.

PubMed

Todorovski, L; Ribaric, S; Dimec, J; Hudomalj, E; Lunder, T

1999-01-01

In the paper, we focus on the problem of building and disseminating multimedia medical databases on the World Wide Web (WWW). The current results of the ongoing project of building a prototype dermatology images database and its WWW presentation are presented. The dermatology database is part of an ambitious plan concerning an organization of a network of medical institutions building distributed and federated multimedia databases of a much wider scale.

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.

NVST Data Archiving System Based On FastBit NoSQL Database

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

High-fidelity data embedding for image annotation.

PubMed

He, Shan; Kirovski, Darko; Wu, Min

2009-02-01

High fidelity is a demanding requirement for data hiding, especially for images with artistic or medical value. This correspondence proposes a high-fidelity image watermarking for annotation with robustness to moderate distortion. To achieve the high fidelity of the embedded image, we introduce a visual perception model that aims at quantifying the local tolerance to noise for arbitrary imagery. Based on this model, we embed two kinds of watermarks: a pilot watermark that indicates the existence of the watermark and an information watermark that conveys a payload of several dozen bits. The objective is to embed 32 bits of metadata into a single image in such a way that it is robust to JPEG compression and cropping. We demonstrate the effectiveness of the visual model and the application of the proposed annotation technology using a database of challenging photographic and medical images that contain a large amount of smooth regions.

[Implications of mental image processing in the deficits of verbal information coding during normal aging].

PubMed

Plaie, Thierry; Thomas, Delphine

2008-06-01

Our study specifies the contributions of image generation and image maintenance processes occurring at the time of imaginal coding of verbal information in memory during normal aging. The memory capacities of 19 young adults (average age of 24 years) and 19 older adults (average age of 75 years) were assessed using recall tasks according to the imagery value of the stimuli to learn. The mental visual imagery capacities are assessed using tasks of image generation and temporary storage of mental imagery. The variance analysis indicates a more important decrease with age of the concretness effect. The major contribution of our study rests on the fact that the decline with age of dual coding of verbal information in memory would result primarily from the decline of image maintenance capacities and from a slowdown in image generation. (PsycINFO Database Record (c) 2008 APA, all rights reserved).

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

Exploring the Effect of Illumination on Automatic Expression Recognition using the ICT-3DRFE Database

DTIC Science & Technology

2011-11-04

environmen- tal lighting conditions that one can actually come across. L7 and L8 are also cases of low illumination intensity. To produce our experimental...Graphics (Proceedings of ACM SIGGRAPH), 26(3). [9] Riklin- Raviv T., Shashua A., (1999). The quotient image: class based recognition and synthesis under

High-dynamic-range imaging for cloud segmentation

NASA Astrophysics Data System (ADS)

Dev, Soumyabrata; Savoy, Florian M.; Lee, Yee Hui; Winkler, Stefan

2018-04-01

Sky-cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg - an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.

Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review

PubMed Central

Lv, Zhuowen; Xing, Xianglei; Wang, Kejun; Guan, Donghai

2015-01-01

Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach. PMID:25574935

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.; Megalooikonomou, V.; Davatzikos, C.; Chen, A.; Bryan, R. N.; Gerring, J. P.

1999-01-01

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

Food-pics: an image database for experimental research on eating and appetite.

PubMed

Blechert, Jens; Meule, Adrian; Busch, Niko A; Ohla, Kathrin

2014-01-01

Our current environment is characterized by the omnipresence of food cues. The sight and smell of real foods, but also graphically depictions of appetizing foods, can guide our eating behavior, for example, by eliciting food craving and influencing food choice. The relevance of visual food cues on human information processing has been demonstrated by a growing body of studies employing food images across the disciplines of psychology, medicine, and neuroscience. However, currently used food image sets vary considerably across laboratories and image characteristics (contrast, brightness, etc.) and food composition (calories, macronutrients, etc.) are often unspecified. These factors might have contributed to some of the inconsistencies of this research. To remedy this, we developed food-pics, a picture database comprising 568 food images and 315 non-food images along with detailed meta-data. A total of N = 1988 individuals with large variance in age and weight from German speaking countries and North America provided normative ratings of valence, arousal, palatability, desire to eat, recognizability and visual complexity. Furthermore, data on macronutrients (g), energy density (kcal), and physical image characteristics (color composition, contrast, brightness, size, complexity) are provided. The food-pics image database is freely available under the creative commons license with the hope that the set will facilitate standardization and comparability across studies and advance experimental research on the determinants of eating behavior.

Food-pics: an image database for experimental research on eating and appetite

PubMed Central

Blechert, Jens; Meule, Adrian; Busch, Niko A.; Ohla, Kathrin

2014-01-01

Our current environment is characterized by the omnipresence of food cues. The sight and smell of real foods, but also graphically depictions of appetizing foods, can guide our eating behavior, for example, by eliciting food craving and influencing food choice. The relevance of visual food cues on human information processing has been demonstrated by a growing body of studies employing food images across the disciplines of psychology, medicine, and neuroscience. However, currently used food image sets vary considerably across laboratories and image characteristics (contrast, brightness, etc.) and food composition (calories, macronutrients, etc.) are often unspecified. These factors might have contributed to some of the inconsistencies of this research. To remedy this, we developed food-pics, a picture database comprising 568 food images and 315 non-food images along with detailed meta-data. A total of N = 1988 individuals with large variance in age and weight from German speaking countries and North America provided normative ratings of valence, arousal, palatability, desire to eat, recognizability and visual complexity. Furthermore, data on macronutrients (g), energy density (kcal), and physical image characteristics (color composition, contrast, brightness, size, complexity) are provided. The food-pics image database is freely available under the creative commons license with the hope that the set will facilitate standardization and comparability across studies and advance experimental research on the determinants of eating behavior. PMID:25009514

Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

NASA Astrophysics Data System (ADS)

Stranieri, Andrew; Yearwood, John; Pham, Binh

1999-07-01

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research.

PubMed

Horst, Jessica S; Hout, Michael C

2016-12-01

Many experimental research designs require images of novel objects. Here we introduce the Novel Object and Unusual Name (NOUN) Database. This database contains 64 primary novel object images and additional novel exemplars for ten basic- and nine global-level object categories. The objects' novelty was confirmed by both self-report and a lack of consensus on questions that required participants to name and identify the objects. We also found that object novelty correlated with qualifying naming responses pertaining to the objects' colors. The results from a similarity sorting task (and a subsequent multidimensional scaling analysis on the similarity ratings) demonstrated that the objects are complex and distinct entities that vary along several featural dimensions beyond simply shape and color. A final experiment confirmed that additional item exemplars comprised both sub- and superordinate categories. These images may be useful in a variety of settings, particularly for developmental psychology and other research in the language, categorization, perception, visual memory, and related domains.

Noise Estimation and Quality Assessment of Gaussian Noise Corrupted Images

NASA Astrophysics Data System (ADS)

Kamble, V. M.; Bhurchandi, K.

2018-03-01

Evaluating the exact quantity of noise present in an image and quality of an image in the absence of reference image is a challenging task. We propose a near perfect noise estimation method and a no reference image quality assessment method for images corrupted by Gaussian noise. The proposed methods obtain initial estimate of noise standard deviation present in an image using the median of wavelet transform coefficients and then obtains a near to exact estimate using curve fitting. The proposed noise estimation method provides the estimate of noise within average error of +/-4%. For quality assessment, this noise estimate is mapped to fit the Differential Mean Opinion Score (DMOS) using a nonlinear function. The proposed methods require minimum training and yields the noise estimate and image quality score. Images from Laboratory for image and Video Processing (LIVE) database and Computational Perception and Image Quality (CSIQ) database are used for validation of the proposed quality assessment method. Experimental results show that the performance of proposed quality assessment method is at par with the existing no reference image quality assessment metric for Gaussian noise corrupted images.

Possible costs associated with investigating and mitigating geologic hazards in rural areas of western San Mateo County, California with a section on using the USGS website to determine the cost of developing property for residences in rural parts of San Mateo County, California

USGS Publications Warehouse

Brabb, Earl E.; Roberts, Sebastian; Cotton, William R.; Kropp, Alan L.; Wright, Robert H.; Zinn, Erik N.; Digital database by Roberts, Sebastian; Mills, Suzanne K.; Barnes, Jason B.; Marsolek, Joanna E.

2000-01-01

This publication consists of a digital map database on a geohazards web site, http://kaibab.wr.usgs.gov/geohazweb/intro.htm, this text, and 43 digital map images available for downloading at this site. The report is stored as several digital files, in ARC export (uncompressed) format for the database, and Postscript and PDF formats for the map images. Several of the source data layers for the images have already been released in other publications by the USGS and are available for downloading on the Internet. These source layers are not included in this digital database, but rather a reference is given for the web site where the data can be found in digital format. The exported ARC coverages and grids lie in UTM zone 10 projection. The pamphlet, which only describes the content and character of the digital map database, is included as Postscript, PDF, and ASCII text files and is also available on paper as USGS Open-File Report 00-127. The full versatility of the spatial database is realized by importing the ARC export files into ARC/INFO or an equivalent GIS. Other GIS packages, including MapInfo and ARCVIEW, can also use the ARC export files. The Postscript map image can be used for viewing or plotting in computer systems with sufficient capacity, and the considerably smaller PDF image files can be viewed or plotted in full or in part from Adobe ACROBAT software running on Macintosh, PC, or UNIX platforms.

Context indexing of digital cardiac ultrasound records in PACS

NASA Astrophysics Data System (ADS)

Lobodzinski, S. Suave; Meszaros, Georg N.

1998-07-01

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

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

PubMed

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

1995-01-01

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

BNU-LSVED: a multimodal spontaneous expression database in educational environment

NASA Astrophysics Data System (ADS)

Sun, Bo; Wei, Qinglan; He, Jun; Yu, Lejun; Zhu, Xiaoming

2016-09-01

In the field of pedagogy or educational psychology, emotions are treated as very important factors, which are closely associated with cognitive processes. Hence, it is meaningful for teachers to analyze students' emotions in classrooms, thus adjusting their teaching activities and improving students ' individual development. To provide a benchmark for different expression recognition algorithms, a large collection of training and test data in classroom environment has become an acute problem that needs to be resolved. In this paper, we present a multimodal spontaneous database in real learning environment. To collect the data, students watched seven kinds of teaching videos and were simultaneously filmed by a camera. Trained coders made one of the five learning expression labels for each image sequence extracted from the captured videos. This subset consists of 554 multimodal spontaneous expression image sequences (22,160 frames) recorded in real classrooms. There are four main advantages in this database. 1) Due to recorded in the real classroom environment, viewer's distance from the camera and the lighting of the database varies considerably between image sequences. 2) All the data presented are natural spontaneous responses to teaching videos. 3) The multimodal database also contains nonverbal behavior including eye movement, head posture and gestures to infer a student ' s affective state during the courses. 4) In the video sequences, there are different kinds of temporal activation patterns. In addition, we have demonstrated the labels for the image sequences are in high reliability through Cronbach's alpha method.

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

NASA Astrophysics Data System (ADS)

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

1999-10-01

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

An open access thyroid ultrasound image database

NASA Astrophysics Data System (ADS)

Pedraza, Lina; Vargas, Carlos; Narváez, Fabián.; Durán, Oscar; Muñoz, Emma; Romero, Eduardo

2015-01-01

Computer aided diagnosis systems (CAD) have been developed to assist radiologists in the detection and diagnosis of abnormalities and a large number of pattern recognition techniques have been proposed to obtain a second opinion. Most of these strategies have been evaluated using different datasets making their performance incomparable. In this work, an open access database of thyroid ultrasound images is presented. The dataset consists of a set of B-mode Ultrasound images, including a complete annotation and diagnostic description of suspicious thyroid lesions by expert radiologists. Several types of lesions as thyroiditis, cystic nodules, adenomas and thyroid cancers were included while an accurate lesion delineation is provided in XML format. The diagnostic description of malignant lesions was confirmed by biopsy. The proposed new database is expected to be a resource for the community to assess different CAD systems.

Point spread function engineering for iris recognition system design.

PubMed

Ashok, Amit; Neifeld, Mark A

2010-04-01

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

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.

An Animated Introduction to Relational Databases for Many Majors

ERIC Educational Resources Information Center

Dietrich, Suzanne W.; Goelman, Don; Borror, Connie M.; Crook, Sharon M.

2015-01-01

Database technology affects many disciplines beyond computer science and business. This paper describes two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is for educators in diverse academic disciplines to incorporate the…

Digital food photography: Dietary surveillance and beyond

USDA-ARS?s Scientific Manuscript database

The method used for creating a database of approximately 20,000 digital images of multiple portion sizes of foods linked to the USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is presented. The creation of this database began in 2002, and its development has spanned 10 years. Initially...

BioImaging Database

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

David Nix, Lisa Simirenko

2006-10-25

The Biolmaging Database (BID) is a relational database developed to store the data and meta-data for the 3D gene expression in early Drosophila embryo development on a cellular level. The schema was written to be used with the MySQL DBMS but with minor modifications can be used on any SQL compliant relational DBMS.

Physiology-based face recognition in the thermal infrared spectrum.

PubMed

Buddharaju, Pradeep; Pavlidis, Ioannis T; Tsiamyrtzis, Panagiotis; Bazakos, Mike

2007-04-01

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

Visual search for emotional expressions: Effect of stimulus set on anger and happiness superiority.

PubMed

Savage, Ruth A; Becker, Stefanie I; Lipp, Ottmar V

2016-01-01

Prior reports of preferential detection of emotional expressions in visual search have yielded inconsistent results, even for face stimuli that avoid obvious expression-related perceptual confounds. The current study investigated inconsistent reports of anger and happiness superiority effects using face stimuli drawn from the same database. Experiment 1 excluded procedural differences as a potential factor, replicating a happiness superiority effect in a procedure that previously yielded an anger superiority effect. Experiments 2a and 2b confirmed that image colour or poser gender did not account for prior inconsistent findings. Experiments 3a and 3b identified stimulus set as the critical variable, revealing happiness or anger superiority effects for two partially overlapping sets of face stimuli. The current results highlight the critical role of stimulus selection for the observation of happiness or anger superiority effects in visual search even for face stimuli that avoid obvious expression related perceptual confounds and are drawn from a single database.

Low-level image properties in facial expressions.

PubMed

Menzel, Claudia; Redies, Christoph; Hayn-Leichsenring, Gregor U

2018-06-04

We studied low-level image properties of face photographs and analyzed whether they change with different emotional expressions displayed by an individual. Differences in image properties were measured in three databases that depicted a total of 167 individuals. Face images were used either in their original form, cut to a standard format or superimposed with a mask. Image properties analyzed were: brightness, redness, yellowness, contrast, spectral slope, overall power and relative power in low, medium and high spatial frequencies. Results showed that image properties differed significantly between expressions within each individual image set. Further, specific facial expressions corresponded to patterns of image properties that were consistent across all three databases. In order to experimentally validate our findings, we equalized the luminance histograms and spectral slopes of three images from a given individual who showed two expressions. Participants were significantly slower in matching the expression in an equalized compared to an original image triad. Thus, existing differences in these image properties (i.e., spectral slope, brightness or contrast) facilitate emotion detection in particular sets of face images. Copyright © 2018. Published by Elsevier B.V.

Navigation integrity monitoring and obstacle detection for enhanced-vision systems

NASA Astrophysics Data System (ADS)

Korn, Bernd; Doehler, Hans-Ullrich; Hecker, Peter

2001-08-01

Typically, Enhanced Vision (EV) systems consist of two main parts, sensor vision and synthetic vision. Synthetic vision usually generates a virtual out-the-window view using databases and accurate navigation data, e. g. provided by differential GPS (DGPS). The reliability of the synthetic vision highly depends on both, the accuracy of the used database and the integrity of the navigation data. But especially in GPS based systems, the integrity of the navigation can't be guaranteed. Furthermore, only objects that are stored in the database can be displayed to the pilot. Consequently, unexpected obstacles are invisible and this might cause severe problems. Therefore, additional information has to be extracted from sensor data to overcome these problems. In particular, the sensor data analysis has to identify obstacles and has to monitor the integrity of databases and navigation. Furthermore, if a lack of integrity arises, navigation data, e.g. the relative position of runway and aircraft, has to be extracted directly from the sensor data. The main contribution of this paper is about the realization of these three sensor data analysis tasks within our EV system, which uses the HiVision 35 GHz MMW radar of EADS, Ulm as the primary EV sensor. For the integrity monitoring, objects extracted from radar images are registered with both database objects and objects (e. g. other aircrafts) transmitted via data link. This results in a classification into known and unknown radar image objects and consequently, in a validation of the integrity of database and navigation. Furthermore, special runway structures are searched for in the radar image where they should appear. The outcome of this runway check contributes to the integrity analysis, too. Concurrent to this investigation a radar image based navigation is performed without using neither precision navigation nor detailed database information to determine the aircraft's position relative to the runway. The performance of our approach is demonstrated with real data acquired during extensive flight tests to several airports in Northern Germany.

An automated system for terrain database construction

NASA Technical Reports Server (NTRS)

Johnson, L. F.; Fretz, R. K.; Logan, T. L.; Bryant, N. A.

1987-01-01

An automated Terrain Database Preparation System (TDPS) for the construction and editing of terrain databases used in computerized wargaming simulation exercises has been developed. The TDPS system operates under the TAE executive, and it integrates VICAR/IBIS image processing and Geographic Information System software with CAD/CAM data capture and editing capabilities. The terrain database includes such features as roads, rivers, vegetation, and terrain roughness.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

A framework for farmland parcels extraction based on image classification

NASA Astrophysics Data System (ADS)

Liu, Guoying; Ge, Wenying; Song, Xu; Zhao, Hongdan

2018-03-01

It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.

Building Extraction Based on Openstreetmap Tags and Very High Spatial Resolution Image in Urban Area

NASA Astrophysics Data System (ADS)

Kang, L.; Wang, Q.; Yan, H. W.

2018-04-01

How to derive contour of buildings from VHR images is the essential problem for automatic building extraction in urban area. To solve this problem, OSM data is introduced to offer vector contour information of buildings which is hard to get from VHR images. First, we import OSM data into database. The line string data of OSM with tags of building, amenity, office etc. are selected and combined into completed contours; Second, the accuracy of contours of buildings is confirmed by comparing with the real buildings in Google Earth; Third, maximum likelihood classification is conducted with the confirmed building contours, and the result demonstrates that the proposed approach is effective and accurate. The approach offers a new way for automatic interpretation of VHR images.

Kruskal-Wallis-based computationally efficient feature selection for face recognition.

PubMed

Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz

2014-01-01

Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

Leveraging Cognitive Context for Object Recognition

DTIC Science & Technology

2014-06-01

learned from large image databases. We build upon this concept by exploring cognitive context, demonstrating how rich dynamic context provided by...context that people rely upon as they perceive the world. Context in ACT-R/E takes the form of associations between related concepts that are learned ...and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by

Dual Resolution Images from Paired Fingerprint Cards

National Institute of Standards and Technology Data Gateway

NIST Dual Resolution Images from Paired Fingerprint Cards (Web, free access)   NIST Special Database 30 is being distributed for use in development and testing of fingerprint compression and fingerprint matching systems. The database allows the user to develop and evaluate data compression algorithms for fingerprint images scanned at both 19.7 ppmm (500 dpi) and 39.4 ppmm (1000 dpi). The data consist of 36 ten-print paired cards with both the rolled and plain images scanned at 19.7 and 39.4 pixels per mm. A newer version of the compression/decompression software on the CDROM can be found at the website http://www.nist.gov/itl/iad/ig/nigos.cfm as part of the NBIS package.

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

NASA Technical Reports Server (NTRS)

Jack, R. F.

1984-01-01

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

Image databases: Problems and perspectives

NASA Technical Reports Server (NTRS)

Gudivada, V. Naidu

1989-01-01

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

Comparative study of palm print authentication system using geometric features

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajeev, Srijith; Panetta, Karen; Agaian, Sos S.

2017-05-01

Biometrics, particularly palm print authentication has been a stimulating research area due to its abundance of features. Stable features and effective matching are the most crucial steps for an authentication system. In conventional palm print authentication systems, matching is based on flexion creases, friction ridges, and minutiae points. Currently, contactless palm print imaging is an emerging technology. However, they tend to involve fluctuations in the image quality and texture loss due to factors such as varying illumination conditions, occlusions, noise, pose, and ghosting. These variations decrease the performance of the authentication systems. Furthermore, real-time palm print authentication in large databases continue to be a challenging task. In order to effectively solve these problems, features which are invariant to these anomalies are required. This paper proposes a robust palm print matching framework by making a comparative study of different local geometric features such as Difference-of-Gaussian, Hessian, Hessian-Laplace, Harris-Laplace, and Multiscale Harris for feature detection. These detectors are coupled with Scale Invariant Feature Transformation (SIFT) descriptor to describe the identified features. Additionally, a two-stage refinement process is carried out to obtain the best stable matches. Computer simulations demonstrate that the accuracy of the system has increased effectively with an EER of 0.86% when Harris-Laplace detector is used on IITD database.

Federated web-accessible clinical data management within an extensible neuroimaging database.

PubMed

Ozyurt, I Burak; Keator, David B; Wei, Dingying; Fennema-Notestine, Christine; Pease, Karen R; Bockholt, Jeremy; Grethe, Jeffrey S

2010-12-01

Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site.

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.

PubMed

Harrigan, Robert L; Yvernault, Benjamin C; Boyd, Brian D; Damon, Stephen M; Gibney, Kyla David; Conrad, Benjamin N; Phillips, Nicholas S; Rogers, Baxter P; Gao, Yurui; Landman, Bennett A

2016-01-01

The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers. Copyright © 2015 Elsevier Inc. All rights reserved.

Integrating image quality in 2nu-SVM biometric match score fusion.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2007-10-01

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.

Atmospheric turbulence and sensor system effects on biometric algorithm performance

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Leonard, Kevin R.; Byrd, Kenneth A.; Potvin, Guy

2015-05-01

Biometric technologies composed of electro-optical/infrared (EO/IR) sensor systems and advanced matching algorithms are being used in various force protection/security and tactical surveillance applications. To date, most of these sensor systems have been widely used in controlled conditions with varying success (e.g., short range, uniform illumination, cooperative subjects). However the limiting conditions of such systems have yet to be fully studied for long range applications and degraded imaging environments. Biometric technologies used for long range applications will invariably suffer from the effects of atmospheric turbulence degradation. Atmospheric turbulence causes blur, distortion and intensity fluctuations that can severely degrade image quality of electro-optic and thermal imaging systems and, for the case of biometrics technology, translate to poor matching algorithm performance. In this paper, we evaluate the effects of atmospheric turbulence and sensor resolution on biometric matching algorithm performance. We use a subset of the Facial Recognition Technology (FERET) database and a commercial algorithm to analyze facial recognition performance on turbulence degraded facial images. The goal of this work is to understand the feasibility of long-range facial recognition in degraded imaging conditions, and the utility of camera parameter trade studies to enable the design of the next generation biometrics sensor systems.

Digital Image Support in the ROADNet Real-time Monitoring Platform

NASA Astrophysics Data System (ADS)

Lindquist, K. G.; Hansen, T. S.; Newman, R. L.; Vernon, F. L.; Nayak, A.; Foley, S.; Fricke, T.; Orcutt, J.; Rajasekar, A.

2004-12-01

The ROADNet real-time monitoring infrastructure has allowed researchers to integrate geophysical monitoring data from a wide variety of signal domains. Antelope-based data transport, relational-database buffering and archiving, backup/replication/archiving through the Storage Resource Broker, and a variety of web-based distribution tools create a powerful monitoring platform. In this work we discuss our use of the ROADNet system for the collection and processing of digital image data. Remote cameras have been deployed at approximately 32 locations as of September 2004, including the SDSU Santa Margarita Ecological Reserve, the Imperial Beach pier, and the Pinon Flats geophysical observatory. Fire monitoring imagery has been obtained through a connection to the HPWREN project. Near-real-time images obtained from the R/V Roger Revelle include records of seafloor operations by the JASON submersible, as part of a maintenance mission for the H2O underwater seismic observatory. We discuss acquisition mechanisms and the packet architecture for image transport via Antelope orbservers, including multi-packet support for arbitrarily large images. Relational database storage supports archiving of timestamped images, image-processing operations, grouping of related images and cameras, support for motion-detect triggers, thumbnail images, pre-computed video frames, support for time-lapse movie generation and storage of time-lapse movies. Available ROADNet monitoring tools include both orbserver-based display of incoming real-time images and web-accessible searching and distribution of images and movies driven by the relational database (http://mercali.ucsd.edu/rtapps/rtimbank.php). An extension to the Kepler Scientific Workflow System also allows real-time image display via the Ptolemy project. Custom time-lapse movies may be made from the ROADNet web pages.

Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework.

PubMed

Herrera, Jose L; Del-Blanco, Carlos R; Garcia, Narciso

2018-07-01

There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.

Exudate detection in color retinal images for mass screening of diabetic retinopathy.

PubMed

Zhang, Xiwei; Thibault, Guillaume; Decencière, Etienne; Marcotegui, Beatriz; Laÿ, Bruno; Danno, Ronan; Cazuguel, Guy; Quellec, Gwénolé; Lamard, Mathieu; Massin, Pascale; Chabouis, Agnès; Victor, Zeynep; Erginay, Ali

2014-10-01

The automatic detection of exudates in color eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to automatically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Digital hand atlas for web-based bone age assessment: system design and implementation

NASA Astrophysics Data System (ADS)

Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente

2000-04-01

A frequently used assessment method of skeletal age is atlas matching by a radiological examination of a hand image against a small set of Greulich-Pyle patterns of normal standards. The method however can lead to significant deviation in age assessment, due to a variety of observers with different levels of training. The Greulich-Pyle atlas based on middle upper class white populations in the 1950s, is also not fully applicable for children of today, especially regarding the standard development in other racial groups. In this paper, we present our system design and initial implementation of a digital hand atlas and computer-aided diagnostic (CAD) system for Web-based bone age assessment. The digital atlas will remove the disadvantages of the currently out-of-date one and allow the bone age assessment to be computerized and done conveniently via Web. The system consists of a hand atlas database, a CAD module and a Java-based Web user interface. The atlas database is based on a large set of clinically normal hand images of diverse ethnic groups. The Java-based Web user interface allows users to interact with the hand image database form browsers. Users can use a Web browser to push a clinical hand image to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, is then extracted and compared with patterns from the atlas database to assess the bone age.

Normal Databases for the Relative Quantification of Myocardial Perfusion

PubMed Central

Rubeaux, Mathieu; Xu, Yuan; Germano, Guido; Berman, Daniel S.; Slomka, Piotr J.

2016-01-01

Purpose of review Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used. Recent findings New equipment and new software reconstruction algorithms have been introduced which require the development of new normal limits. The appearance and regional count variations of normal MPI scan may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique. Summary Throughout this review, we emphasize the importance of the different normal databases and the need for specific databases relative to distinct imaging procedures. use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied. PMID:28138354

Toward More Accurate Iris Recognition Using Cross-Spectral Matching.

PubMed

Nalla, Pattabhi Ramaiah; Kumar, Ajay

2017-01-01

Iris recognition systems are increasingly deployed for large-scale applications such as national ID programs, which continue to acquire millions of iris images to establish identity among billions. However, with the availability of variety of iris sensors that are deployed for the iris imaging under different illumination/environment, significant performance degradation is expected while matching such iris images acquired under two different domains (either sensor-specific or wavelength-specific). This paper develops a domain adaptation framework to address this problem and introduces a new algorithm using Markov random fields model to significantly improve cross-domain iris recognition. The proposed domain adaptation framework based on the naive Bayes nearest neighbor classification uses a real-valued feature representation, which is capable of learning domain knowledge. Our approach to estimate corresponding visible iris patterns from the synthesis of iris patches in the near infrared iris images achieves outperforming results for the cross-spectral iris recognition. In this paper, a new class of bi-spectral iris recognition system that can simultaneously acquire visible and near infra-red images with pixel-to-pixel correspondences is proposed and evaluated. This paper presents experimental results from three publicly available databases; PolyU cross-spectral iris image database, IIITD CLI and UND database, and achieve outperforming results for the cross-sensor and cross-spectral iris matching.

Shuttle Return To Flight Experimental Results: Protuberance Effects on Boundary Layer Transition

NASA Technical Reports Server (NTRS)

Liechty, Derek S.; Berry, Scott A.; Horvath, Thomas J.

2006-01-01

The effect of isolated roughness elements on the windward boundary layer of the Shuttle Orbiter has been experimentally examined in the Langley Aerothermodynamic Laboratory in support of an agency-wide effort to prepare the Shuttle Orbiter for return to flight. This experimental effort was initiated to provide a roughness effects database for developing transition criteria to support on-orbit decisions to repair damage to the thermal protection system. Boundary layer transition results were obtained using trips of varying heights and locations along the centerline and attachment lines of 0.0075-scale models. Global heat transfer images using phosphor thermography of the Orbiter windward surface and the corresponding heating distributions were used to infer the state of the boundary layer (laminar, transitional, or turbulent). The database contained within this report will be used to formulate protuberance-induced transition correlations using predicted boundary layer edge parameters.

Combining multiple features for color texture classification

NASA Astrophysics Data System (ADS)

Cusano, Claudio; Napoletano, Paolo; Schettini, Raimondo

2016-11-01

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

Increasing Receipt of High-Tech/High-Cost Imaging and Its Determinants in the Last Month of Taiwanese Patients With Metastatic Cancer, 2001-2010: A Retrospective Cohort Study.

PubMed

Liu, Tsang-Wu; Hung, Yen-Ni; Soong, Thomas C; Tang, Siew Tzuh

2015-08-01

One strategy for controlling the skyrocketing costs of cancer care may be to target high-tech/high-cost imaging at the end of life (EOL). This population-based study investigated receipt of high-tech/high-cost imaging and its determinants for Taiwanese patients with metastatic cancer in their last month of life.Individual patient-level data were linked with encrypted identification numbers from computerized administrative data in Taiwan, that is, the National Register of Deaths Database, Cancer Registration System database, and National Health Insurance claims datasets, Database of Medical Care Institutions Status, and national census statistics (population/household income). We identified receipt of computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and radionuclide bone scans (BSs) for 236,911 Taiwanese cancer decedents with metastatic disease, 2001 to 2010. Associations of patient, physician, hospital, and regional factors with receiving CT, MRI, and bone scan in the last month of life were evaluated by multilevel generalized linear-mixed models.Over one-third (average [range]: 36.11% [33.07%-37.31%]) of patients with metastatic cancer received at least 1 high-tech/high-cost imaging modality in their last month (usage rates for CT, MRI, PET, and BS were 31.05%, 5.81%, 0.25%, and 8.15%, respectively). In 2001 to 2010, trends of receipt increased for CT (27.96-32.22%), MRI (4.34-6.70%), and PET (0.00-0.62%), but decreased for BS (9.47-6.57%). Facilitative determinants with consistent trends for at least 2 high-tech/high-cost imaging modalities were male gender, younger age, married, rural residence, lung cancer diagnosis, dying within 1 to 2 years of diagnosis, not under medical oncology care, and receiving care at a teaching hospital with a larger volume of terminally ill cancer patients and greater EOL care intensity. Undergoing high-tech/high-cost imaging at EOL generally was not associated with regional characteristics, healthcare resources, and EOL care intensity.To more effectively use high-tech/high-cost imaging at EOL, clinical and financial interventions should target nonmedical oncologists/hematologists affiliated with teaching hospitals that tend to aggressively treat high volumes of terminally ill cancer patients, thereby avoiding unnecessary EOL care spending and transforming healthcare systems into affordable high-quality cancer care delivery systems.

LIRIS flight database and its use toward noncooperative rendezvous

NASA Astrophysics Data System (ADS)

Mongrard, O.; Ankersen, F.; Casiez, P.; Cavrois, B.; Donnard, A.; Vergnol, A.; Southivong, U.

2018-06-01

ESA's fifth and last Automated Transfer Vehicle, ATV Georges Lemaître, tested new rendezvous technology before docking with the International Space Station (ISS) in August 2014. The technology demonstration called Laser Infrared Imaging Sensors (LIRIS) provides an unseen view of the ISS. During Georges Lemaître's rendezvous, LIRIS sensors, composed of two infrared cameras, one visible camera, and a scanning LIDAR (Light Detection and Ranging), were turned on two and a half hours and 3500 m from the Space Station. All sensors worked as expected and a large amount of data was recorded and stored within ATV-5's cargo hold before being returned to Earth with the Soyuz flight 38S in September 2014. As a part of the LIRIS postflight activities, the information gathered by all sensors is collected inside a flight database together with the reference ATV trajectory and attitude estimated by ATV main navigation sensors. Although decoupled from the ATV main computer, the LIRIS data were carefully synchronized with ATV guidance, navigation, and control (GNC) data. Hence, the LIRIS database can be used to assess the performance of various image processing algorithms to provide range and line-of-sight (LoS) navigation at long/medium range but also 6 degree-of-freedom (DoF) navigation at short range. The database also contains information related to the overall ATV position with respect to Earth and the Sun direction within ATV frame such that the effect of the environment on the sensors can also be investigated. This paper introduces the structure of the LIRIS database and provides some example of applications to increase the technology readiness level of noncooperative rendezvous.

Design and development of a multimedia database for emergency telemedicine.

PubMed

Pavlopoulos, S; Berler, A; Kyriacou, E; Koutsouris, D

1998-09-01

Recent studies conclude that early and specialised pre-hospital patient management contributes to emergency cases survival. Recent developments in telecommunication and medical informatics by means of telemedicine can be extremely useful to accomplish such tasks in a cost-effective manner. Along that direction, we have designed a portable device for emergency telemedicine. This device is able to telematically "bring" the expert doctor at the emergency site, have him perform an accurate diagnosis, and subsequently direct the Emergency Medical Technicians on how to treat the patient until he arrives to the hospital. The need for storing and archiving all data being interchanged during the telemedicine sessions is very crucial for clinical, legal and administrative purposes. For this, we have developed a multimedia database able to store and manage the data collected by the AMBULANCE system. The database was equipped with a user-friendly graphical interface to enable use from computer naive users. Furthermore, the database has the possibility to display, in an standard way, ECG's, X-ray, CT and MRI images. The application is password protected with a three-level hierarchy access for users with different privileges. The scope of this application is to enhance the capabilities of the doctor on duty for a more precise and prompt diagnosis. The application has the ability to store audio files related to each emergency case and still images of the scene. Finally, this database can become a useful multimedia tool which will work together with the AMBULANCE portable device, the HIS and the PACS of the hospital. The system has been validated in selected non-critical cases and proved to be functional and successful in enhancing the ability of the doctor's on duty for prompt and accurate diagnosis and specialised pre-hospital treatment.

Manchester visual query language

NASA Astrophysics Data System (ADS)

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

1993-04-01

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

Memory color assisted illuminant estimation through pixel clustering

NASA Astrophysics Data System (ADS)

Zhang, Heng; Quan, Shuxue

2010-01-01

The under constrained nature of illuminant estimation determines that in order to resolve the problem, certain assumptions are needed, such as the gray world theory. Including more constraints in this process may help explore the useful information in an image and improve the accuracy of the estimated illuminant, providing that the constraints hold. Based on the observation that most personal images have contents of one or more of the following categories: neutral objects, human beings, sky, and plants, we propose a method for illuminant estimation through the clustering of pixels of gray and three dominant memory colors: skin tone, sky blue, and foliage green. Analysis shows that samples of the above colors cluster around small areas under different illuminants and their characteristics can be used to effectively detect pixels falling into each of the categories. The algorithm requires the knowledge of the spectral sensitivity response of the camera, and a spectral database consisted of the CIE standard illuminants and reflectance or radiance database of samples of the above colors.

MagnaportheDB: a federated solution for integrating physical and genetic map data with BAC end derived sequences for the rice blast fungus Magnaporthe grisea.

PubMed

Martin, Stanton L; Blackmon, Barbara P; Rajagopalan, Ravi; Houfek, Thomas D; Sceeles, Robert G; Denn, Sheila O; Mitchell, Thomas K; Brown, Douglas E; Wing, Rod A; Dean, Ralph A

2002-01-01

We have created a federated database for genome studies of Magnaporthe grisea, the causal agent of rice blast disease, by integrating end sequence data from BAC clones, genetic marker data and BAC contig assembly data. A library of 9216 BAC clones providing >25-fold coverage of the entire genome was end sequenced and fingerprinted by HindIII digestion. The Image/FPC software package was then used to generate an assembly of 188 contigs covering >95% of the genome. The database contains the results of this assembly integrated with hybridization data of genetic markers to the BAC library. AceDB was used for the core database engine and a MySQL relational database, populated with numerical representations of BAC clones within FPC contigs, was used to create appropriately scaled images. The database is being used to facilitate sequencing efforts. The database also allows researchers mapping known genes or other sequences of interest, rapid and easy access to the fundamental organization of the M.grisea genome. This database, MagnaportheDB, can be accessed on the web at http://www.cals.ncsu.edu/fungal_genomics/mgdatabase/int.htm.

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

NASA Astrophysics Data System (ADS)

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

1998-02-01

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

SenseLab

PubMed Central

Crasto, Chiquito J.; Marenco, Luis N.; Liu, Nian; Morse, Thomas M.; Cheung, Kei-Hoi; Lai, Peter C.; Bahl, Gautam; Masiar, Peter; Lam, Hugo Y.K.; Lim, Ernest; Chen, Huajin; Nadkarni, Prakash; Migliore, Michele; Miller, Perry L.; Shepherd, Gordon M.

2009-01-01

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab’s unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources. PMID:17510162

Systematic review, critical appraisal, and analysis of the quality of economic evaluations in stroke imaging.

PubMed

Burton, Kirsteen R; Perlis, Nathan; Aviv, Richard I; Moody, Alan R; Kapral, Moira K; Krahn, Murray D; Laupacis, Andreas

2014-03-01

This study reviews the quality of economic evaluations of imaging after acute stroke and identifies areas for improvement. We performed full-text searches of electronic databases that included Medline, Econlit, the National Health Service Economic Evaluation Database, and the Tufts Cost Effectiveness Analysis Registry through July 2012. Search strategy terms included the following: stroke*; cost*; or cost-benefit analysis*; and imag*. Inclusion criteria were empirical studies published in any language that reported the results of economic evaluations of imaging interventions for patients with stroke symptoms. Study quality was assessed by a commonly used checklist (with a score range of 0% to 100%). Of 568 unique potential articles identified, 5 were included in the review. Four of 5 articles were explicit in their analysis perspectives, which included healthcare system payers, hospitals, and stroke services. Two studies reported results during a 5-year time horizon, and 3 studies reported lifetime results. All included the modified Rankin Scale score as an outcome measure. The median quality score was 84.4% (range=71.9%-93.5%). Most studies did not consider the possibility that patients could not tolerate contrast media or could incur contrast-induced nephropathy. Three studies compared perfusion computed tomography with unenhanced computed tomography but assumed that outcomes guided by the results of perfusion computed tomography were equivalent to outcomes guided by the results of magnetic resonance imaging or noncontrast computed tomography. Economic evaluations of imaging modalities after acute ischemic stroke were generally of high methodological quality. However, important radiology-specific clinical components were missing from all of these analyses.

Assessing natural hazard risk using images and data

NASA Astrophysics Data System (ADS)

Mccullough, H. L.; Dunbar, P. K.; Varner, J. D.; Mungov, G.

2012-12-01

Photographs and other visual media provide valuable pre- and post-event data for natural hazard assessment. Scientific research, mitigation, and forecasting rely on visual data for risk analysis, inundation mapping and historic records. Instrumental data only reveal a portion of the whole story; photographs explicitly illustrate the physical and societal impacts from the event. Visual data is rapidly increasing as the availability of portable high resolution cameras and video recorders becomes more attainable. Incorporating these data into archives ensures a more complete historical account of events. Integrating natural hazards data, such as tsunami, earthquake and volcanic eruption events, socio-economic information, and tsunami deposits and runups along with images and photographs enhances event comprehension. Global historic databases at NOAA's National Geophysical Data Center (NGDC) consolidate these data, providing the user with easy access to a network of information. NGDC's Natural Hazards Image Database (ngdc.noaa.gov/hazardimages) was recently improved to provide a more efficient and dynamic user interface. It uses the Google Maps API and Keyhole Markup Language (KML) to provide geographic context to the images and events. Descriptive tags, or keywords, have been applied to each image, enabling easier navigation and discovery. In addition, the Natural Hazards Map Viewer (maps.ngdc.noaa.gov/viewers/hazards) provides the ability to search and browse data layers on a Mercator-projection globe with a variety of map backgrounds. This combination of features creates a simple and effective way to enhance our understanding of hazard events and risks using imagery.

The neurobiology of addiction: the perspective from magnetic resonance imaging present and future

PubMed Central

Nestor, Liam J.

2016-01-01

Abstract Background and Aims Addiction is associated with severe economic and social consequences and personal tragedies, the scientific exploration of which draws upon investigations at the molecular, cellular and systems levels with a wide variety of technologies. Magnetic resonance imaging (MRI) has been key to mapping effects observed at the microscopic and mesoscopic scales. The range of measurements from this apparatus has opened new avenues linking neurobiology to behaviour. This review considers the role of MRI in addiction research, and what future technological improvements might offer. Methods A hermeneutic strategy supplemented by an expansive, systematic search of PubMed, Scopus and Web of Science databases, covering from database inception to October 2015, with a conjunction of search terms relevant to addiction and MRI. Formal meta‐analyses were prioritized. Results Results from methods that probe brain structure and function suggest frontostriatal circuitry disturbances within specific cognitive domains, some of which predict drug relapse and treatment response. New methods of processing imaging data are opening opportunities for understanding the role of cerebral vasculature, a global view of brain communication and the complex topology of the cortical surface and drug action. Future technological advances include increases in MRI field strength, with concomitant improvements in image quality. Conclusions The magnetic resonance imaging literature provides a limited but convergent picture of the neurobiology of addiction as global changes to brain structure and functional disturbances to frontostriatal circuitry, accompanied by changes in anterior white matter. PMID:27452960

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes.

PubMed

Crone, Damien L; Bode, Stefan; Murawski, Carsten; Laham, Simon M

2018-01-01

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

Word naming times and psycholinguistic norms for Italian nouns.

PubMed

Barca, Laura; Burani, Cristina; Arduino, Lisa S

2002-08-01

The present study describes normative measures for 626 Italian simple nouns. The database (LEXVAR.XLS) is freely available for down-loading on the Web site http://wwwistc.ip.rm.cnr.it/materia/database/. For each of the 626 nouns, values for the following variables are reported: age of acquisition, familiarity, imageability, concreteness, adult written frequency, child written frequency, adult spoken frequency, number of orthographic neighbors, mean bigram frequency, length in syllables, and length in letters. A classification of lexical stress and of the type of word-initial phoneme is also provided. The intercorrelations among the variables, a factor analysis, and the effects of variables and of the extracted factors on word naming are reported. Naming latencies were affected primarily by a factor including word length and neighborhood size and by a word frequency factor. Neither a semantic factor including imageability, concreteness, and age of acquisition nor a factor defined by mean bigram frequency had significant effects on pronunciation times. These results hold for a language with shallow orthography, like Italian, for which lexical nonsemantic properties have been shown to affect reading aloud. These norms are useful in a variety of research areas involving the manipulation and control of stimulus attributes.

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.

Blind image quality assessment via probabilistic latent semantic analysis.

PubMed

Yang, Xichen; Sun, Quansen; Wang, Tianshu

2016-01-01

We propose a blind image quality assessment that is highly unsupervised and training free. The new method is based on the hypothesis that the effect caused by distortion can be expressed by certain latent characteristics. Combined with probabilistic latent semantic analysis, the latent characteristics can be discovered by applying a topic model over a visual word dictionary. Four distortion-affected features are extracted to form the visual words in the dictionary: (1) the block-based local histogram; (2) the block-based local mean value; (3) the mean value of contrast within a block; (4) the variance of contrast within a block. Based on the dictionary, the latent topics in the images can be discovered. The discrepancy between the frequency of the topics in an unfamiliar image and a large number of pristine images is applied to measure the image quality. Experimental results for four open databases show that the newly proposed method correlates well with human subjective judgments of diversely distorted images.

Ocean Optical Database

DTIC Science & Technology

1992-05-01

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

Three Dimensional Visualization of GOES Cloud Data Using Octress

DTIC Science & Technology

1993-06-01

structure for CAD of integrated circuits that can subdivide the cubes into more complex polyhedrons . Medical imaging is also taking advantage of the...CIGOES 501 FORMAT(A) CALL OPENDBCPARAM’, ISTATRM) IF (ISTATRM .NE. 0) CALL FRIMERRC Error opening database .’, "+ ISTATRM) CALL OLDIMAGE(1, CIGOES, STATUS...image name (no .ext):’ ACCEPT 501, CIGOES 501 FORMAT(A) CALL OPENDB(’PARAM’, ISTATRM) IF (ISTATRM .NE. 0) CALL FRIMERRC Error opening database

Automatic lung nodule graph cuts segmentation with deep learning false positive reduction

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang Bill; Qian, Wei

2017-03-01

To automatic detect lung nodules from CT images, we designed a two stage computer aided detection (CAD) system. The first stage is graph cuts segmentation to identify and segment the nodule candidates, and the second stage is convolutional neural network for false positive reduction. The dataset contains 595 CT cases randomly selected from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) and the 305 pulmonary nodules achieved diagnosis consensus by all four experienced radiologists were our detection targets. Consider each slice as an individual sample, 2844 nodules were included in our database. The graph cuts segmentation was conducted in a two-dimension manner, 2733 lung nodule ROIs are successfully identified and segmented. With a false positive reduction by a seven-layer convolutional neural network, 2535 nodules remain detected while the false positive dropped to 31.6%. The average F-measure of segmented lung nodule tissue is 0.8501.

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

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

PubMed

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

2002-05-01

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

Imaged Document Optical Correlation and Conversion System (IDOCCS)

NASA Astrophysics Data System (ADS)

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

1999-03-01

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

Hepatitis Diagnosis Using Facial Color Image

NASA Astrophysics Data System (ADS)

Liu, Mingjia; Guo, Zhenhua

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

A Mobile Food Record For Integrated Dietary Assessment*

PubMed Central

Ahmad, Ziad; Kerr, Deborah A.; Bosch, Marc; Boushey, Carol J.; Delp, Edward J.; Khanna, Nitin; Zhu, Fengqing

2017-01-01

This paper presents an integrated dietary assessment system based on food image analysis that uses mobile devices or smartphones. We describe two components of our integrated system: a mobile application and an image-based food nutrient database that is connected to the mobile application. An easy-to-use mobile application user interface is described that was designed based on user preferences as well as the requirements of the image analysis methods. The user interface is validated by user feedback collected from several studies. Food nutrient and image databases are also described which facilitates image-based dietary assessment and enable dietitians and other healthcare professionals to monitor patients dietary intake in real-time. The system has been tested and validated in several user studies involving more than 500 users who took more than 60,000 food images under controlled and community-dwelling conditions. PMID:28691119

Earth Science World ImageBank (ESWIB): A Comprehensive Collection of Geoscience Images Being Developed by the American Geological Institute

NASA Astrophysics Data System (ADS)

Howe, A. W.; Keane, C. M.

2003-12-01

Although there are geoscience images available in numerous locations around the World Wide Web, there is no universal comprehensive digital archive where teachers, students, scientists, and the general public can gather images related to the Earth Sciences. To fill this need, the American Geological Institute (AGI) is developing the largest image database available: the Earth Science World ImageBank (ESWIB). The goal of ESWIB is to provide a variety of users with free access to high-quality geoscience images and technical art gathered from photographers, government organizations, and scientists. Each image is cataloged by location, author, image rights, and a detailed description of what the image shows. Additionally, images are cataloged using keywords from AGI's precise Georef indexing methodology. Students, teachers, and the general public can search or browse and download these images for use in slide show presentations, lectures, papers, or for other educational and outreach uses. This resource can be used for any age level, in any kind of educational venue. Users can also contribute images of their own to the database through the ESWIB website. AGI is scanning these images at a very high resolution (16 x 20 inches) and depending on the author's rights, is making high-resolution copies (digital or print) available for non-commercial and commercial purposes. This ImageBank is different from other photo sites available in that the scope has more breadth and depth than other image resources, and the images are cataloged with a very high grade of detail and precision, which makes finding needed images fast and easy. The image services offered by ESWIB are also unique, such as the low-cost commercial options and high quality image printouts. AGI plans on adding more features to ESWIB in the future, including connecting this resource to the up-coming online Glossary of Geology, a geospatial search option, using the images to make generic PowerPoint presentations that teachers can use and/or modify for their classes, lesson plans related to using the ImageBank, and a `photo of the day' section that will highlight a particular photo which stands out. AGI began work on this project in August of 2002 with the initial scanning and editing of images. ESWIB currently has over 1,300 images cataloged and searchable through the database homepage. In addition to the cataloged images, there are approximately 6,700 images waiting to be edited and cataloged, and over 10,000 images identified for submission at this point. AGI is constantly soliciting images in an attempt to expand the database. Through press releases, e-mail announcements, and our member societies, AGI publicized the launch of ESWIB in June of 2003. Since the launch, ESWIB has received over 300,000 image views as well as publicity in Space Daily, Russian and German news sites, various links through science websites, and most recently in Science Magazine's NetWatch section (August 29th, 2003).

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.

Rhinoplasty perioperative database using a personal digital assistant.

PubMed

Kotler, Howard S

2004-01-01

To construct a reliable, accurate, and easy-to-use handheld computer database that facilitates the point-of-care acquisition of perioperative text and image data specific to rhinoplasty. A user-modified database (Pendragon Forms [v.3.2]; Pendragon Software Corporation, Libertyville, Ill) and graphic image program (Tealpaint [v.4.87]; Tealpaint Software, San Rafael, Calif) were used to capture text and image data, respectively, on a Palm OS (v.4.11) handheld operating with 8 megabytes of memory. The handheld and desktop databases were maintained secure using PDASecure (v.2.0) and GoldSecure (v.3.0) (Trust Digital LLC, Fairfax, Va). The handheld data were then uploaded to a desktop database of either FileMaker Pro 5.0 (v.1) (FileMaker Inc, Santa Clara, Calif) or Microsoft Access 2000 (Microsoft Corp, Redmond, Wash). Patient data were collected from 15 patients undergoing rhinoplasty in a private practice outpatient ambulatory setting. Data integrity was assessed after 6 months' disk and hard drive storage. The handheld database was able to facilitate data collection and accurately record, transfer, and reliably maintain perioperative rhinoplasty data. Query capability allowed rapid search using a multitude of keyword search terms specific to the operative maneuvers performed in rhinoplasty. Handheld computer technology provides a method of reliably recording and storing perioperative rhinoplasty information. The handheld computer facilitates the reliable and accurate storage and query of perioperative data, assisting the retrospective review of one's own results and enhancement of surgical skills.

Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

Neyman Pearson detection of K-distributed random variables

NASA Astrophysics Data System (ADS)

Tucker, J. Derek; Azimi-Sadjadi, Mahmood R.

2010-04-01

In this paper a new detection method for sonar imagery is developed in K-distributed background clutter. The equation for the log-likelihood is derived and compared to the corresponding counterparts derived for the Gaussian and Rayleigh assumptions. Test results of the proposed method on a data set of synthetic underwater sonar images is also presented. This database contains images with targets of different shapes inserted into backgrounds generated using a correlated K-distributed model. Results illustrating the effectiveness of the K-distributed detector are presented in terms of probability of detection, false alarm, and correct classification rates for various bottom clutter scenarios.

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.

A Novel Robot Visual Homing Method Based on SIFT Features

PubMed Central

Zhu, Qidan; Liu, Chuanjia; Cai, Chengtao

2015-01-01

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method. PMID:26473880

Extended Multiscale Image Segmentation for Castellated Wall Management

NASA Astrophysics Data System (ADS)

Sakamoto, M.; Tsuguchi, M.; Chhatkuli, S.; Satoh, T.

2018-05-01

Castellated walls are positioned as tangible cultural heritage, which require regular maintenance to preserve their original state. For the demolition and repair work of the castellated wall, it is necessary to identify the individual stones constituting the wall. However, conventional approaches using laser scanning or integrated circuits (IC) tags were very time-consuming and cumbersome. Therefore, we herein propose an efficient approach for castellated wall management based on an extended multiscale image segmentation technique. In this approach, individual stone polygons are extracted from the castellated wall image and are associated with a stone management database. First, to improve the performance of the extraction of individual stone polygons having a convex shape, we developed a new shape criterion named convex hull fitness in the image segmentation process and confirmed its effectiveness. Next, we discussed the stone management database and its beneficial utilization in the repair work of castellated walls. Subsequently, we proposed irregular-shape indexes that are helpful for evaluating the stone shape and the stability of the stone arrangement state in castellated walls. Finally, we demonstrated an application of the proposed method for a typical castellated wall in Japan. Consequently, we confirmed that the stone polygons can be extracted with an acceptable level. Further, the condition of the shapes and the layout of the stones could be visually judged with the proposed irregular-shape indexes.

Imaged document information location and extraction using an optical correlator

NASA Astrophysics Data System (ADS)

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

1999-12-01

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

Training system for digital mammographic diagnoses of breast cancer

NASA Astrophysics Data System (ADS)

Thomaz, R. L.; Nirschl Crozara, M. G.; Patrocinio, A. C.

2013-03-01

As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn't been applied to any professional training yet.

Combined semantic and similarity search in medical image databases

NASA Astrophysics Data System (ADS)

Seifert, Sascha; Thoma, Marisa; Stegmaier, Florian; Hammon, Matthias; Kramer, Martin; Huber, Martin; Kriegel, Hans-Peter; Cavallaro, Alexander; Comaniciu, Dorin

2011-03-01

The current diagnostic process at hospitals is mainly based on reviewing and comparing images coming from multiple time points and modalities in order to monitor disease progression over a period of time. However, for ambiguous cases the radiologist deeply relies on reference literature or second opinion. Although there is a vast amount of acquired images stored in PACS systems which could be reused for decision support, these data sets suffer from weak search capabilities. Thus, we present a search methodology which enables the physician to fulfill intelligent search scenarios on medical image databases combining ontology-based semantic and appearance-based similarity search. It enabled the elimination of 12% of the top ten hits which would arise without taking the semantic context into account.

Astronomical database and VO-tools of Nikolaev Astronomical Observatory

NASA Astrophysics Data System (ADS)

Mazhaev, A. E.; Protsyuk, Yu. I.

2010-05-01

Results of work in 2006-2009 on creation of astronomical databases aiming at development of Nikolaev Virtual Observatory (NVO) are presented in this abstract. Results of observations and theirreduction, which were obtained during the whole history of Nikolaev Astronomical Observatory (NAO), are included in the databases. The databases may be considered as a basis for construction of a data centre. Images of different regions of the celestial sphere have been stored in NAO since 1929. About 8000 photo plates were obtained during observations in the 20th century. Observations with CCD have been started since 1996. Annually, telescopes of NAO, using CCD cameras, create data volume of several tens of gigabytes (GB) in the form of CCD images and up to 100 GB of video records. At the end of 2008, the volume of accumulated data in the form of CCD images was about 300 GB. Problems of data volume growth are common in astronomy, nuclear physics and bioinformatics. Therefore, the astronomical community needs to use archives, databases and distributed grid computing to cope with this problem in astronomy. The International Virtual Observatory Alliance (IVOA) was formed in June 2002 with a mission to "enable the international utilization of astronomical archives..." The NVO was created at the NAO website in 2008, and consists of three main parts. The first part contains 27 astrometric stellar catalogues with short descriptions. The files of catalogues were compiled in the standard VOTable format using eXtensible Markup Language (XML), and they are available for downloading. This is an example of the so-called science-ready product. The VOTable format was developed by the International Virtual Observatory Alliance (IVOA) for exchange of tabular data. A user may download these catalogues and open them using any standalone application that supports standards of the IVOA. There are several directions of development for such applications, for example, search of catalogues and images, search and visualisation of spectra, spectral energy distribution (SED) building, search of cross-correlation between objects in different catalogues, statistical data processing of large data volumes etc. The second part includes database of observations, accumulated in NAO, with access via a browser. The database has a common interface for searching of textual and graphical information concerning photographic and CCD observations. The database contains: textual information about 7437 plates as well as 2700 preview images in JPEG format with resolution of 300 DPI (dots per inch); textual information about 16660 CCD frames as well as 1100 preview images in JPEG format. Absent preview images will be added to the database as soon as they will be ready after plates scanning and CCD frames processing. The user has to define the equatorial coordinates of search centre, a search radius and a period of observations. Then he or she may also specify additional filters, such as: any combination of objects given separately for plates and CCD frames, output parameters for plates, telescope names for CCD observations. Results of search are generated in the form of two tables for photographic and CCD observations. To obtain access to the source images in FITS format with support of World Coordinate System (WCS), the user has to fill and submit electronic form given after the tables. The third part includes database of observations with access via a standalone application such as Aladin, which has been developed by Strasbourg Astronomical Data Centre. To obtain access to the database, the user has to perform a series of simple actions, which are described on a corresponding site page. Then he or she may get access to the database via a server selector of Aladin, which has a menu with wide range of image and catalogue servers located world wide, including two menu items for photographic and CCD observations of a NVO image server. The user has to define the equatorial coordinates of search centre and a search radius. The search results are outputted into a main window of Aladin in textual and graphical forms using XML and Simple Object Access Protocol (SOAP). In this way, the NVO image server is integrated with other astronomical servers, using a special configuration file. The user may conveniently request information from many servers using the same server selector of Aladin, although the servers are located in different countries. Aladin has a wide range of special tools for data analysis and handling, including connection with other standalone applications. As a conclusion, we should note that a research team of a data centre, which provides the infrastructure for data output to the internet, is responsible for creation of corresponding archives. Therefore, each observatory or data centre has to provide an access to its archives in accordance with the IVOA standards and a resolution adopted by the IAU XXV General Assembly #B.1, titled: Public Access to Astronomical Archives. A research team of NAO copes successfully with this task and continues to develop the NVO. Using our databases and VO-tools, we also take part in development of the Ukrainian Virtual Observatory (UkrVO). All three main parts of the NVO are used as prototypes for the UkrVO. Informational resources provided by other astronomical institutions from Ukraine will be included in corresponding databases and VO interfaces.

Artist Material BRDF Database for Computer Graphics Rendering

NASA Astrophysics Data System (ADS)

Ashbaugh, Justin C.

The primary goal of this thesis was to create a physical library of artist material samples. This collection provides necessary data for the development of a gonio-imaging system for use in museums to more accurately document their collections. A sample set was produced consisting of 25 panels and containing nearly 600 unique samples. Selected materials are representative of those commonly used by artists both past and present. These take into account the variability in visual appearance resulting from the materials and application techniques used. Five attributes of variability were identified including medium, color, substrate, application technique and overcoat. Combinations of these attributes were selected based on those commonly observed in museum collections and suggested by surveying experts in the field. For each sample material, image data is collected and used to measure an average bi-directional reflectance distribution function (BRDF). The results are available as a public-domain image and optical database of artist materials at art-si.org. Additionally, the database includes specifications for each sample along with other information useful for computer graphics rendering such as the rectified sample images and normal maps.

Segmentation and feature extraction of cervical spine x-ray images

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Thoma, George R.

1999-05-01

As part of an R&D project in mixed text/image database design, the National Library of Medicine has archived a collection of 17,000 digitized x-ray images of the cervical and lumbar spine which were collected as part of the second National Health and Nutrition Examination Survey (NHANES II). To make this image data available and usable to a wide audience, we are investigating techniques for indexing the image content by automated or semi-automated means. Indexing of the images by features of interest to researchers in spine disease and structure requires effective segmentation of the vertebral anatomy. This paper describes work in progress toward this segmentation of the cervical spine images into anatomical components of interest, including anatomical landmarks for vertebral location, and segmentation and identification of individual vertebrae. Our work includes developing a reliable method for automatically fixing an anatomy-based coordinate system in the images, and work to adaptively threshold the images, using methods previously applied by researchers in cardioangiography. We describe the motivation for our work and present our current results in both areas.

Intensity-hue-saturation-based image fusion using iterative linear regression

NASA Astrophysics Data System (ADS)

Cetin, Mufit; Tepecik, Abdulkadir

2016-10-01

The image fusion process basically produces a high-resolution image by combining the superior features of a low-resolution spatial image and a high-resolution panchromatic image. Despite its common usage due to its fast computing capability and high sharpening ability, the intensity-hue-saturation (IHS) fusion method may cause some color distortions, especially when a large number of gray value differences exist among the images to be combined. This paper proposes a spatially adaptive IHS (SA-IHS) technique to avoid these distortions by automatically adjusting the exact spatial information to be injected into the multispectral image during the fusion process. The SA-IHS method essentially suppresses the effects of those pixels that cause the spectral distortions by assigning weaker weights to them and avoiding a large number of redundancies on the fused image. The experimental database consists of IKONOS images, and the experimental results both visually and statistically prove the enhancement of the proposed algorithm when compared with the several other IHS-like methods such as IHS, generalized IHS, fast IHS, and generalized adaptive IHS.

Absolute stellar photometry on moderate-resolution FPA images

USGS Publications Warehouse

Stone, T.C.

2009-01-01

An extensive database of star (and Moon) images has been collected by the ground-based RObotic Lunar Observatory (ROLO) as part of the US Geological Survey program for lunar calibration. The stellar data are used to derive nightly atmospheric corrections for the observations from extinction measurements, and absolute calibration of the ROLO sensors is based on observations of Vega and published reference flux and spectrum data. The ROLO telescopes were designed for imaging the Moon at moderate resolution, thus imposing some limitations for the stellar photometry. Attaining accurate stellar photometry with the ROLO image data has required development of specialized processing techniques. A key consideration is consistency in discriminating the star core signal from the off-axis point spread function. The analysis and processing methods applied to the ROLO stellar image database are described. ?? 2009 BIPM and IOP Publishing Ltd.

Imaging in Chronic Traumatic Encephalopathy and Traumatic Brain Injury

PubMed Central

Shetty, Teena; Raince, Avtar; Manning, Erin; Tsiouris, Apostolos John

2016-01-01

Context: The diagnosis of chronic traumatic encephalopathy (CTE) can only be made pathologically, and there is no concordance of defined clinical criteria for premorbid diagnosis. The absence of established criteria and the insufficient imaging findings to detect this disease in a living athlete are of growing concern. Evidence Acquisition: The article is a review of the current literature on CTE. Databases searched include Medline, PubMed, JAMA evidence, and evidence-based medicine guidelines Cochrane Library, Hospital for Special Surgery, and Cornell Library databases. Study Design: Clinical review. Level of Evidence: Level 4. Results: Chronic traumatic encephalopathy cannot be diagnosed on imaging. Examples of imaging findings in common types of head trauma are discussed. Conclusion: Further study is necessary to correlate the clinical and imaging findings of repetitive head injuries with the pathologic diagnosis of CTE. PMID:26733590

Face sketch recognition based on edge enhancement via deep learning

NASA Astrophysics Data System (ADS)

Xie, Zhenzhu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.

Mugshot Identification Database (MID)

National Institute of Standards and Technology Data Gateway

NIST Mugshot Identification Database (MID) (Web, free access)   NIST Special Database 18 is being distributed for use in development and testing of automated mugshot identification systems. The database consists of three CD-ROMs, containing a total of 3248 images of variable size using lossless compression. A newer version of the compression/decompression software on the CDROM can be found at the website http://www.nist.gov/itl/iad/ig/nigos.cfm as part of the NBIS package.

Learning about Severe Combined Immunodeficiency (SCID)

MedlinePlus

... Genomics Regulation of Genetic Tests Statute and Legislation Database Newsroom Calendar of Events Current News Releases Image ... Release: February 22, 2005 X-linked SCID mutation database (IL2RGbase) On Other Sites: Development of population-based ...

Semisupervised kernel marginal Fisher analysis for face recognition.

PubMed

Wang, Ziqiang; Sun, Xia; Sun, Lijun; Huang, Yuchun

2013-01-01

Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.

Regional Principal Color Based Saliency Detection

PubMed Central

Lou, Jing; Ren, Mingwu; Wang, Huan

2014-01-01

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms. PMID:25379960

8-Bit Gray Scale Images of Fingerprint Image Groups

National Institute of Standards and Technology Data Gateway

NIST 8-Bit Gray Scale Images of Fingerprint Image Groups (Web, free access)   The NIST database of fingerprint images contains 2000 8-bit gray scale fingerprint image pairs. A newer version of the compression/decompression software on the CDROM can be found at the website http://www.nist.gov/itl/iad/ig/nigos.cfm as part of the NBIS package.

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

PubMed Central

Wang, Jingjing; Sun, Tao; Gao, Ni; Menon, Desmond Dev; Luo, Yanxia; Gao, Qi; Li, Xia; Wang, Wei; Zhu, Huiping; Lv, Pingxin; Liang, Zhigang; Tao, Lixin; Liu, Xiangtong; Guo, Xiuhua

2014-01-01

Objective To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer. PMID:25250576

Deep Convolutional Neural Networks for breast cancer screening.

PubMed

Chougrad, Hiba; Zouaki, Hamid; Alheyane, Omar

2018-04-01

Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health care system. In this paper we developed a Computer-aided Diagnosis (CAD) system based on deep Convolutional Neural Networks (CNN) that aims to help the radiologist classify mammography mass lesions. Deep learning usually requires large datasets to train networks of a certain depth from scratch. Transfer learning is an effective method to deal with relatively small datasets as in the case of medical images, although it can be tricky as we can easily start overfitting. In this work, we explore the importance of transfer learning and we experimentally determine the best fine-tuning strategy to adopt when training a CNN model. We were able to successfully fine-tune some of the recent, most powerful CNNs and achieved better results compared to other state-of-the-art methods which classified the same public datasets. For instance we achieved 97.35% accuracy and 0.98 AUC on the DDSM database, 95.50% accuracy and 0.97 AUC on the INbreast database and 96.67% accuracy and 0.96 AUC on the BCDR database. Furthermore, after pre-processing and normalizing all the extracted Regions of Interest (ROIs) from the full mammograms, we merged all the datasets to build one large set of images and used it to fine-tune our CNNs. The CNN model which achieved the best results, a 98.94% accuracy, was used as a baseline to build the Breast Cancer Screening Framework. To evaluate the proposed CAD system and its efficiency to classify new images, we tested it on an independent database (MIAS) and got 98.23% accuracy and 0.99 AUC. The results obtained demonstrate that the proposed framework is performant and can indeed be used to predict if the mass lesions are benign or malignant. Copyright © 2018 Elsevier B.V. All rights reserved.

Astronomical Archive at Tartu Observatory

NASA Astrophysics Data System (ADS)

Annuk, K.

2007-10-01

Archiving astronomical data is important task not only at large observatories but also at small observatories. Here we describe the astronomical archive at Tartu Observatory. The archive consists of old photographic plate images, photographic spectrograms, CCD direct--images and CCD spectroscopic data. The photographic plate digitizing project was started in 2005. An on-line database (based on MySQL) was created. The database includes CCD data as well photographic data. A PHP-MySQL interface was written for access to all data.

SMS Two Column Template: Smithsonian Marine Station (SMS) at Fort Pierce

Science.gov Websites

appreciation of this invaluable natural resource. My image My image My image My image My image Discover: - over difference in IRL water quality Check Out: - the IRL Photo Gallery - the IRL Species Image Collection Downloads: - Species Database - IRL Species Bibliography My image My image My image My image My image EOL

Database for the collection and analysis of clinical data and images of neoplasms of the sinonasal tract.

PubMed

Trimarchi, Matteo; Lund, Valerie J; Nicolai, Piero; Pini, Massimiliano; Senna, Massimo; Howard, David J

2004-04-01

The Neoplasms of the Sinonasal Tract software package (NSNT v 1.0) implements a complete visual database for patients with sinonasal neoplasia, facilitating standardization of data and statistical analysis. The software, which is compatible with the Macintosh and Windows platforms, provides multiuser application with a dedicated server (on Windows NT or 2000 or Macintosh OS 9 or X and a network of clients) together with web access, if required. The system hardware consists of an Apple Power Macintosh G4500 MHz computer with PCI bus, 256 Mb of RAM plus 60 Gb hard disk, or any IBM-compatible computer with a Pentium 2 processor. Image acquisition may be performed with different frame-grabber cards for analog or digital video input of different standards (PAL, SECAM, or NTSC) and levels of quality (VHS, S-VHS, Betacam, Mini DV, DV). The visual database is based on 4th Dimension by 4D Inc, and video compression is made in real-time MPEG format. Six sections have been developed: demographics, symptoms, extent of disease, radiology, treatment, and follow-up. Acquisition of data includes computed tomography and magnetic resonance imaging, histology, and endoscopy images, allowing sequential comparison. Statistical analysis integral to the program provides Kaplan-Meier survival curves. The development of a dedicated, user-friendly database for sinonasal neoplasia facilitates a multicenter network and has obvious clinical and research benefits.

Neuroinformatics Database (NiDB) – A Modular, Portable Database for the Storage, Analysis, and Sharing of Neuroimaging Data

PubMed Central

Anderson, Beth M.; Stevens, Michael C.; Glahn, David C.; Assaf, Michal; Pearlson, Godfrey D.

2013-01-01

We present a modular, high performance, open-source database system that incorporates popular neuroimaging database features with novel peer-to-peer sharing, and a simple installation. An increasing number of imaging centers have created a massive amount of neuroimaging data since fMRI became popular more than 20 years ago, with much of that data unshared. The Neuroinformatics Database (NiDB) provides a stable platform to store and manipulate neuroimaging data and addresses several of the impediments to data sharing presented by the INCF Task Force on Neuroimaging Datasharing, including 1) motivation to share data, 2) technical issues, and 3) standards development. NiDB solves these problems by 1) minimizing PHI use, providing a cost effective simple locally stored platform, 2) storing and associating all data (including genome) with a subject and creating a peer-to-peer sharing model, and 3) defining a sample, normalized definition of a data storage structure that is used in NiDB. NiDB not only simplifies the local storage and analysis of neuroimaging data, but also enables simple sharing of raw data and analysis methods, which may encourage further sharing. PMID:23912507

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.

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.

PubMed

Chi, Jianning; Walia, Ekta; Babyn, Paul; Wang, Jimmy; Groot, Gary; Eramian, Mark

2017-08-01

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into "malignant" and "benign" cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

Efficiency analysis of color image filtering

NASA Astrophysics Data System (ADS)

Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Abramov, Sergey K.; Egiazarian, Karen O.; Astola, Jaakko T.

2011-12-01

This article addresses under which conditions filtering can visibly improve the image quality. The key points are the following. First, we analyze filtering efficiency for 25 test images, from the color image database TID2008. This database allows assessing filter efficiency for images corrupted by different noise types for several levels of noise variance. Second, the limit of filtering efficiency is determined for independent and identically distributed (i.i.d.) additive noise and compared to the output mean square error of state-of-the-art filters. Third, component-wise and vector denoising is studied, where the latter approach is demonstrated to be more efficient. Fourth, using of modern visual quality metrics, we determine that for which levels of i.i.d. and spatially correlated noise the noise in original images or residual noise and distortions because of filtering in output images are practically invisible. We also demonstrate that it is possible to roughly estimate whether or not the visual quality can clearly be improved by filtering.

Digitizing Olin Eggen's Card Database

NASA Astrophysics Data System (ADS)

Crast, J.; Silvis, G.

2017-06-01

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

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

No-reference quality assessment based on visual perception

NASA Astrophysics Data System (ADS)

Li, Junshan; Yang, Yawei; Hu, Shuangyan; Zhang, Jiao

2014-11-01

The visual quality assessment of images/videos is an ongoing hot research topic, which has become more and more important for numerous image and video processing applications with the rapid development of digital imaging and communication technologies. The goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images/videos in agreement with human quality judgments. Up to now, two kinds of models have been used for IQA, namely full-reference (FR) and no-reference (NR) models. For FR models, IQA algorithms interpret image quality as fidelity or similarity with a perfect image in some perceptual space. However, the reference image is not available in many practical applications, and a NR IQA approach is desired. Considering natural vision as optimized by the millions of years of evolutionary pressure, many methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychological features of the human visual system (HVS). To reach this goal, researchers try to simulate HVS with image sparsity coding and supervised machine learning, which are two main features of HVS. A typical HVS captures the scenes by sparsity coding, and uses experienced knowledge to apperceive objects. In this paper, we propose a novel IQA approach based on visual perception. Firstly, a standard model of HVS is studied and analyzed, and the sparse representation of image is accomplished with the model; and then, the mapping correlation between sparse codes and subjective quality scores is trained with the regression technique of least squaresupport vector machine (LS-SVM), which gains the regressor that can predict the image quality; the visual metric of image is predicted with the trained regressor at last. We validate the performance of proposed approach on Laboratory for Image and Video Engineering (LIVE) database, the specific contents of the type of distortions present in the database are: 227 images of JPEG2000, 233 images of JPEG, 174 images of White Noise, 174 images of Gaussian Blur, 174 images of Fast Fading. The database includes subjective differential mean opinion score (DMOS) for each image. The experimental results show that the proposed approach not only can assess many kinds of distorted images quality, but also exhibits a superior accuracy and monotonicity.

RExPrimer: an integrated primer designing tool increases PCR effectiveness by avoiding 3' SNP-in-primer and mis-priming from structural variation

PubMed Central

2009-01-01

Background Polymerase chain reaction (PCR) is very useful in many areas of molecular biology research. It is commonly observed that PCR success is critically dependent on design of an effective primer pair. Current tools for primer design do not adequately address the problem of PCR failure due to mis-priming on target-related sequences and structural variations in the genome. Methods We have developed an integrated graphical web-based application for primer design, called RExPrimer, which was written in Python language. The software uses Primer3 as the primer designing core algorithm. Locally stored sequence information and genomic variant information were hosted on MySQLv5.0 and were incorporated into RExPrimer. Results RExPrimer provides many functionalities for improved PCR primer design. Several databases, namely annotated human SNP databases, insertion/deletion (indel) polymorphisms database, pseudogene database, and structural genomic variation databases were integrated into RExPrimer, enabling an effective without-leaving-the-website validation of the resulting primers. By incorporating these databases, the primers reported by RExPrimer avoid mis-priming to related sequences (e.g. pseudogene, segmental duplication) as well as possible PCR failure because of structural polymorphisms (SNP, indel, and copy number variation (CNV)). To prevent mismatching caused by unexpected SNPs in the designed primers, in particular the 3' end (SNP-in-Primer), several SNP databases covering the broad range of population-specific SNP information are utilized to report SNPs present in the primer sequences. Population-specific SNP information also helps customize primer design for a specific population. Furthermore, RExPrimer offers a graphical user-friendly interface through the use of scalable vector graphic image that intuitively presents resulting primers along with the corresponding gene structure. In this study, we demonstrated the program effectiveness in successfully generating primers for strong homologous sequences. Conclusion The improvements for primer design incorporated into RExPrimer were demonstrated to be effective in designing primers for challenging PCR experiments. Integration of SNP and structural variation databases allows for robust primer design for a variety of PCR applications, irrespective of the sequence complexity in the region of interest. This software is freely available at http://www4a.biotec.or.th/rexprimer. PMID:19958502

A new phase-correlation-based iris matching for degraded images.

PubMed

Krichen, Emine; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

2009-08-01

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases, namely, the CASIA-BIOSECURE and Iris Challenge Evaluation 2005 databases, show the improvement of our method compared to two available reference systems, Masek and Open Source for Iris (OSRIS), in verification mode.

Identification of suitable fundus images using automated quality assessment methods.

PubMed

Şevik, Uğur; Köse, Cemal; Berber, Tolga; Erdöl, Hidayet

2014-04-01

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.

Aero-Optics Measurement System for the AEDC Aero-Optics Test Facility

DTIC Science & Technology

1991-02-01

Pulse Energy Statistics , 150 Pulses ........................................ 41 AEDC-TR-90-20 APPENDIXES A. Optical Performance of Heated Windows...hypersonic wind tunnel, where the requisite extensive statistical database can be developed in a cost- and time-effective manner. Ground testing...At the present time at AEDC, measured AO parameter statistics are derived from sets of image-spot recordings with a set containing as many as 150

Spread spectrum image watermarking based on perceptual quality metric.

PubMed

Zhang, Fan; Liu, Wenyu; Lin, Weisi; Ngan, King Ngi

2011-11-01

Efficient image watermarking calls for full exploitation of the perceptual distortion constraint. Second-order statistics of visual stimuli are regarded as critical features for perception. This paper proposes a second-order statistics (SOS)-based image quality metric, which considers the texture masking effect and the contrast sensitivity in Karhunen-Loève transform domain. Compared with the state-of-the-art metrics, the quality prediction by SOS better correlates with several subjectively rated image databases, in which the images are impaired by the typical coding and watermarking artifacts. With the explicit metric definition, spread spectrum watermarking is posed as an optimization problem: we search for a watermark to minimize the distortion of the watermarked image and to maximize the correlation between the watermark pattern and the spread spectrum carrier. The simple metric guarantees the optimal watermark a closed-form solution and a fast implementation. The experiments show that the proposed watermarking scheme can take full advantage of the distortion constraint and improve the robustness in return.

Part-based deep representation for product tagging and search

NASA Astrophysics Data System (ADS)

Chen, Keqing

2017-06-01

Despite previous studies, tagging and indexing the product images remain challenging due to the large inner-class variation of the products. In the traditional methods, the quantized hand-crafted features such as SIFTs are extracted as the representation of the product images, which are not discriminative enough to handle the inner-class variation. For discriminative image representation, this paper firstly presents a novel deep convolutional neural networks (DCNNs) architect true pre-trained on a large-scale general image dataset. Compared to the traditional features, our DCNNs representation is of more discriminative power with fewer dimensions. Moreover, we incorporate the part-based model into the framework to overcome the negative effect of bad alignment and cluttered background and hence the descriptive ability of the deep representation is further enhanced. Finally, we collect and contribute a well-labeled shoe image database, i.e., the TBShoes, on which we apply the part-based deep representation for product image tagging and search, respectively. The experimental results highlight the advantages of the proposed part-based deep representation.

Scoring Package

National Institute of Standards and Technology Data Gateway

NIST Scoring Package (PC database for purchase)   The NIST Scoring Package (Special Database 1) is a reference implementation of the draft Standard Method for Evaluating the Performance of Systems Intended to Recognize Hand-printed Characters from Image Data Scanned from Forms.

Database for vertigo.

PubMed

Kentala, E; Pyykkö, I; Auramo, Y; Juhola, M

1995-03-01

An interactive database has been developed to assist the diagnostic procedure for vertigo and to store the data. The database offers a possibility to split and reunite the collected information when needed. It contains detailed information about a patient's history, symptoms, and findings in otoneurologic, audiologic, and imaging tests. The symptoms are classified into sets of questions on vertigo (including postural instability), hearing loss and tinnitus, and provoking factors. Confounding disorders are screened. The otoneurologic tests involve saccades, smooth pursuit, posturography, and a caloric test. In addition, findings from specific antibody tests, clinical neurotologic tests, magnetic resonance imaging, brain stem audiometry, and electrocochleography are included. The input information can be applied to workups for vertigo in an expert system called ONE. The database assists its user in that the input of information is easy. If not only can be used for diagnostic purposes but is also beneficial for research, and in combination with the expert system, it provides a tutorial guide for medical students.

On the Privacy Protection of Biometric Traits: Palmprint, Face, and Signature

NASA Astrophysics Data System (ADS)

Panigrahy, Saroj Kumar; Jena, Debasish; Korra, Sathya Babu; Jena, Sanjay Kumar

Biometrics are expected to add a new level of security to applications, as a person attempting access must prove who he or she really is by presenting a biometric to the system. The recent developments in the biometrics area have lead to smaller, faster and cheaper systems, which in turn has increased the number of possible application areas for biometric identity verification. The biometric data, being derived from human bodies (and especially when used to identify or verify those bodies) is considered personally identifiable information (PII). The collection, use and disclosure of biometric data — image or template, invokes rights on the part of an individual and obligations on the part of an organization. As biometric uses and databases grow, so do concerns that the personal data collected will not be used in reasonable and accountable ways. Privacy concerns arise when biometric data are used for secondary purposes, invoking function creep, data matching, aggregation, surveillance and profiling. Biometric data transmitted across networks and stored in various databases by others can also be stolen, copied, or otherwise misused in ways that can materially affect the individual involved. As Biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analysed before they are massively deployed in security systems. Along with security, also the privacy of the users is an important factor as the constructions of lines in palmprints contain personal characteristics, from face images a person can be recognised, and fake signatures can be practised by carefully watching the signature images available in the database. We propose a cryptographic approach to encrypt the images of palmprints, faces, and signatures by an advanced Hill cipher technique for hiding the information in the images. It also provides security to these images from being attacked by above mentioned attacks. So, during the feature extraction, the encrypted images are first decrypted, then the features are extracted, and used for identification or verification.

Digital map databases in support of avionic display systems

NASA Astrophysics Data System (ADS)

Trenchard, Michael E.; Lohrenz, Maura C.; Rosche, Henry, III; Wischow, Perry B.

1991-08-01

The emergence of computerized mission planning systems (MPS) and airborne digital moving map systems (DMS) has necessitated the development of a global database of raster aeronautical chart data specifically designed for input to these systems. The Naval Oceanographic and Atmospheric Research Laboratory''s (NOARL) Map Data Formatting Facility (MDFF) is presently dedicated to supporting these avionic display systems with the development of the Compressed Aeronautical Chart (CAC) database on Compact Disk Read Only Memory (CDROM) optical discs. The MDFF is also developing a series of aircraft-specific Write-Once Read Many (WORM) optical discs. NOARL has initiated a comprehensive research program aimed at improving the pilots'' moving map displays current research efforts include the development of an alternate image compression technique and generation of a standard set of color palettes. The CAC database will provide digital aeronautical chart data in six different scales. CAC is derived from the Defense Mapping Agency''s (DMA) Equal Arc-second (ARC) Digitized Raster Graphics (ADRG) a series of scanned aeronautical charts. NOARL processes ADRG to tailor the chart image resolution to that of the DMS display while reducing storage requirements through image compression techniques. CAC is being distributed by DMA as a library of CDROMs.

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.

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

PubMed

Zhang, Lining; Wang, Lipo; Lin, Weisi

2012-08-01

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

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction.

PubMed

Ravì, Daniele; Szczotka, Agnieszka Barbara; Shakir, Dzhoshkun Ismail; Pereira, Stephen P; Vercauteren, Tom

2018-06-01

Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.

Development of the mouse cochlea database (MCD).

PubMed

Santi, Peter A; Rapson, Ian; Voie, Arne

2008-09-01

The mouse cochlea database (MCD) provides an interactive, image database of the mouse cochlea for learning its anatomy and data mining of its resources. The MCD website is hosted on a centrally maintained, high-speed server at the following URL: (http://mousecochlea.umn.edu). The MCD contains two types of image resources, serial 2D image stacks and 3D reconstructions of cochlear structures. Complete image stacks of the cochlea from two different mouse strains were obtained using orthogonal plane fluorescence optical microscopy (OPFOS). 2D images of the cochlea are presented on the MCD website as: viewable images within a stack, 2D atlas of the cochlea, orthogonal sections, and direct volume renderings combined with isosurface reconstructions. In order to assess cochlear structures quantitatively, "true" cross-sections of the scala media along the length of the basilar membrane were generated by virtual resectioning of a cochlea orthogonal to a cochlear structure, such as the centroid of the basilar membrane or the scala media. 3D images are presented on the MCD website as: direct volume renderings, movies, interactive QuickTime VRs, flythrough, and isosurface 3D reconstructions of different cochlear structures. 3D computer models can also be used for solid model fabrication by rapid prototyping and models from different cochleas can be combined to produce an average 3D model. The MCD is the first comprehensive image resource on the mouse cochlea and is a new paradigm for understanding the anatomy of the cochlea, and establishing morphometric parameters of cochlear structures in normal and mutant mice.

Reference point detection for camera-based fingerprint image based on wavelet transformation.

PubMed

Khalil, Mohammed S

2015-04-30

Fingerprint recognition systems essentially require core-point detection prior to fingerprint matching. The core-point is used as a reference point to align the fingerprint with a template database. When processing a larger fingerprint database, it is necessary to consider the core-point during feature extraction. Numerous core-point detection methods are available and have been reported in the literature. However, these methods are generally applied to scanner-based images. Hence, this paper attempts to explore the feasibility of applying a core-point detection method to a fingerprint image obtained using a camera phone. The proposed method utilizes a discrete wavelet transform to extract the ridge information from a color image. The performance of proposed method is evaluated in terms of accuracy and consistency. These two indicators are calculated automatically by comparing the method's output with the defined core points. The proposed method is tested on two data sets, controlled and uncontrolled environment, collected from 13 different subjects. In the controlled environment, the proposed method achieved a detection rate 82.98%. In uncontrolled environment, the proposed method yield a detection rate of 78.21%. The proposed method yields promising results in a collected-image database. Moreover, the proposed method outperformed compare to existing method.

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes

PubMed Central

Bode, Stefan; Murawski, Carsten; Laham, Simon M.

2018-01-01

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/. PMID:29364985

The clinical information system GastroBase: integration of image processing and laboratory communication.

PubMed

Kocna, P

1995-01-01

GastroBase, a clinical information system, incorporates patient identification, medical records, images, laboratory data, patient history, physical examination, and other patient-related information. Program modules are written in C; all data is processed using Novell-Btrieve data manager. Patient identification database represents the main core of this information systems. A graphic library developed in the past year and graphic modules with a special video-card enables the storing, archiving, and linking of different images to the electronic patient-medical-record. GastroBase has been running for more than four years in daily routine and the database contains more than 25,000 medical records and 1,500 images. This new version of GastroBase is now incorporated into the clinical information system of University Clinic in Prague.

Kodak Picture Exchange--Online Access to Photographs and Images.

ERIC Educational Resources Information Center

Valauskas, Edward J.

1995-01-01

Describes the Kodak Picture Exchange that includes a database of over 200,000 photographic images collected from 28 stock photography companies in the United States that allows the user to search for images by topic. Highlights include installing the software, alternative search strategies, costs, manipulating images, and rules governing the use…

Image segmentation evaluation for very-large datasets

NASA Astrophysics Data System (ADS)

Reeves, Anthony P.; Liu, Shuang; Xie, Yiting

2016-03-01

With the advent of modern machine learning methods and fully automated image analysis there is a need for very large image datasets having documented segmentations for both computer algorithm training and evaluation. Current approaches of visual inspection and manual markings do not scale well to big data. We present a new approach that depends on fully automated algorithm outcomes for segmentation documentation, requires no manual marking, and provides quantitative evaluation for computer algorithms. The documentation of new image segmentations and new algorithm outcomes are achieved by visual inspection. The burden of visual inspection on large datasets is minimized by (a) customized visualizations for rapid review and (b) reducing the number of cases to be reviewed through analysis of quantitative segmentation evaluation. This method has been applied to a dataset of 7,440 whole-lung CT images for 6 different segmentation algorithms designed to fully automatically facilitate the measurement of a number of very important quantitative image biomarkers. The results indicate that we could achieve 93% to 99% successful segmentation for these algorithms on this relatively large image database. The presented evaluation method may be scaled to much larger image databases.

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

PubMed

Dash, Jyotiprava; Bhoi, Nilamani

2018-04-26

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.

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.

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

PubMed

Sugumar, S J; Jayaparvathy, R

2014-01-01

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

A knowledge-based system for patient image pre-fetching in heterogeneous database environments--modeling, design, and evaluation.

PubMed

Wei, C P; Hu, P J; Sheng, O R

2001-03-01

When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may become more efficient, consultatively effective, and better satisfied when supported by the pre-fetching system than when relying on the study site's pre-fetching practice.

Supplemental Fingerprint Card Data (SFCD) for NIST Special Database 9

National Institute of Standards and Technology Data Gateway

Supplemental Fingerprint Card Data (SFCD) for NIST Special Database 9 (Web, free access)   NIST Special Database 10 (Supplemental Fingerprint Card Data for Special Database 9 - 8-Bit Gray Scale Images) provides a larger sample of fingerprint patterns that have a low natural frequency of occurrence and transitional fingerprint classes in NIST Special Database 9. The software is the same code used with NIST Special Database 4 and 9. A newer version of the compression/decompression software on the CDROM can be found at the website http://www.nist.gov/itl/iad/ig/nigos.cfm as part of the NBIS package.

The geometrical precision of virtual bone models derived from clinical computed tomography data for forensic anthropology.

PubMed

Colman, Kerri L; Dobbe, Johannes G G; Stull, Kyra E; Ruijter, Jan M; Oostra, Roelof-Jan; van Rijn, Rick R; van der Merwe, Alie E; de Boer, Hans H; Streekstra, Geert J

2017-07-01

Almost all European countries lack contemporary skeletal collections for the development and validation of forensic anthropological methods. Furthermore, legal, ethical and practical considerations hinder the development of skeletal collections. A virtual skeletal database derived from clinical computed tomography (CT) scans provides a potential solution. However, clinical CT scans are typically generated with varying settings. This study investigates the effects of image segmentation and varying imaging conditions on the precision of virtual modelled pelves. An adult human cadaver was scanned using varying imaging conditions, such as scanner type and standard patient scanning protocol, slice thickness and exposure level. The pelvis was segmented from the various CT images resulting in virtually modelled pelves. The precision of the virtual modelling was determined per polygon mesh point. The fraction of mesh points resulting in point-to-point distance variations of 2 mm or less (95% confidence interval (CI)) was reported. Colour mapping was used to visualise modelling variability. At almost all (>97%) locations across the pelvis, the point-to-point distance variation is less than 2 mm (CI = 95%). In >91% of the locations, the point-to-point distance variation was less than 1 mm (CI = 95%). This indicates that the geometric variability of the virtual pelvis as a result of segmentation and imaging conditions rarely exceeds the generally accepted linear error of 2 mm. Colour mapping shows that areas with large variability are predominantly joint surfaces. Therefore, results indicate that segmented bone elements from patient-derived CT scans are a sufficiently precise source for creating a virtual skeletal database.

The neurobiology of addiction: the perspective from magnetic resonance imaging present and future.

PubMed

Suckling, John; Nestor, Liam J

2017-02-01

Addiction is associated with severe economic and social consequences and personal tragedies, the scientific exploration of which draws upon investigations at the molecular, cellular and systems levels with a wide variety of technologies. Magnetic resonance imaging (MRI) has been key to mapping effects observed at the microscopic and mesoscopic scales. The range of measurements from this apparatus has opened new avenues linking neurobiology to behaviour. This review considers the role of MRI in addiction research, and what future technological improvements might offer. A hermeneutic strategy supplemented by an expansive, systematic search of PubMed, Scopus and Web of Science databases, covering from database inception to October 2015, with a conjunction of search terms relevant to addiction and MRI. Formal meta-analyses were prioritized. Results from methods that probe brain structure and function suggest frontostriatal circuitry disturbances within specific cognitive domains, some of which predict drug relapse and treatment response. New methods of processing imaging data are opening opportunities for understanding the role of cerebral vasculature, a global view of brain communication and the complex topology of the cortical surface and drug action. Future technological advances include increases in MRI field strength, with concomitant improvements in image quality. The magnetic resonance imaging literature provides a limited but convergent picture of the neurobiology of addiction as global changes to brain structure and functional disturbances to frontostriatal circuitry, accompanied by changes in anterior white matter. © 2016 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.

Lack of magnetic resonance imaging lesion activity as a treatment target in multiple sclerosis: An evaluation using electronically collected outcomes.

PubMed

Conway, Devon S; Thompson, Nicolas R; Cohen, Jeffrey A

2016-09-01

The appropriate treatment target in multiple sclerosis (MS) is unclear. Lack of magnetic resonance imaging (MRI) lesion activity, a component of the no evidence of disease activity concept, has been proposed as a treatment target in MS. We used our MS database to investigate whether aggressively pursuing MRI stability by changing disease modifying therapy (DMT) when MRI activity is observed leads to better clinical and imaging outcomes. The Knowledge Program (KP) is a database linked to our electronic medical record allowing capture of patient and clinician reported outcomes. Through KP query and chart review, we identified all relapsing-remitting MS patients visiting between 1 January 2008 and 31 December 2014 with active MRIs despite DMT. Propensity modeling based on demographic and disease characteristics was used to match DMT switchers to non-switchers. KP and MRI outcomes were compared 18 months after the active MRI using mixed-effects linear regression models. We identified 417 patients who met criteria for our analysis. After propensity matching, 78 switchers and 91 non-switchers were analyzed. There was no difference in clinical or radiologic outcomes between these groups at 18 months. We did not find a short-term benefit of changing DMT to pursue MRI stability. Copyright © 2016 Elsevier B.V. All rights reserved.

Measured body composition and geometrical data of four ``virtual family'' members for thermoregulatory modeling

NASA Astrophysics Data System (ADS)

Xu, Xiaojiang; Rioux, Timothy P.; MacLeod, Tynan; Patel, Tejash; Rome, Maxwell N.; Potter, Adam W.

2017-03-01

The purpose of this paper is to develop a database of tissue composition, distribution, volume, surface area, and skin thickness from anatomically correct human models, the virtual family. These models were based on high-resolution magnetic resonance imaging (MRI) of human volunteers, including two adults (male and female) and two children (boy and girl). In the segmented image dataset, each voxel is associated with a label which refers to a tissue type that occupies up that specific cubic millimeter of the body. The tissue volume was calculated from the number of the voxels with the same label. Volumes of 24 organs in body and volumes of 7 tissues in 10 specific body regions were calculated. Surface area was calculated from the collection of voxels that are touching the exterior air. Skin thicknesses were estimated from its volume and surface area. The differences between the calculated and original masses were about 3 % or less for tissues or organs that are important to thermoregulatory modeling, e.g., muscle, skin, and fat. This accurate database of body tissue distributions and geometry is essential for the development of human thermoregulatory models. Data derived from medical imaging provide new effective tools to enhance thermal physiology research and gain deeper insight into the mechanisms of how the human body maintains heat balance.

The SSABLE system - Automated archive, catalog, browse and distribution of satellite data in near-real time

NASA Technical Reports Server (NTRS)

Simpson, James J.; Harkins, Daniel N.

1993-01-01

Historically, locating and browsing satellite data has been a cumbersome and expensive process. This has impeded the efficient and effective use of satellite data in the geosciences. SSABLE is a new interactive tool for the archive, browse, order, and distribution of satellite date based upon X Window, high bandwidth networks, and digital image rendering techniques. SSABLE provides for automatically constructing relational database queries to archived image datasets based on time, data, geographical location, and other selection criteria. SSABLE also provides a visual representation of the selected archived data for viewing on the user's X terminal. SSABLE is a near real-time system; for example, data are added to SSABLE's database within 10 min after capture. SSABLE is network and machine independent; it will run identically on any machine which satisfies the following three requirements: 1) has a bitmapped display (monochrome or greater); 2) is running the X Window system; and 3) is on a network directly reachable by the SSABLE system. SSABLE has been evaluated at over 100 international sites. Network response time in the United States and Canada varies between 4 and 7 s for browse image updates; reported transmission times to Europe and Australia typically are 20-25 s.

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.

A review of automatic mass detection and segmentation in mammographic images.

PubMed

Oliver, Arnau; Freixenet, Jordi; Martí, Joan; Pérez, Elsa; Pont, Josep; Denton, Erika R E; Zwiggelaar, Reyer

2010-04-01

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis. Copyright 2009 Elsevier B.V. All rights reserved.

Historical Collections | Alaska State Library

Science.gov Websites

Microfilm eResources Electronic Books (EBSCO) World Catalog (WorldCat) Free Images and Sounds Journal Finder Publications Catalog and Library Card Info Federal Publications Free Images and Sounds Library Resources Articles & Databases Free Images & Sounds Journal Finder Library Resources Live Homework Help

Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

PubMed

Li, Feng

2015-07-01

This review paper is based on our research experience in the past 30 years. The importance of radiologists' role is discussed in the development or evaluation of new medical images and of computer-aided detection (CAD) schemes in chest radiology. The four main topics include (1) introducing what diseases can be included in a research database for different imaging techniques or CAD systems and what imaging database can be built by radiologists, (2) understanding how radiologists' subjective judgment can be combined with technical objective features to improve CAD performance, (3) sharing our experience in the design of successful observer performance studies, and (4) finally, discussing whether the new images and CAD systems can improve radiologists' diagnostic ability in chest radiology. In conclusion, advanced imaging techniques and detection/classification of CAD systems have a potential clinical impact on improvement of radiologists' diagnostic ability, for both the detection and the differential diagnosis of various lung diseases, in chest radiology.

Computer-aided system for detecting runway incursions

NASA Astrophysics Data System (ADS)

Sridhar, Banavar; Chatterji, Gano B.

1994-07-01

A synthetic vision system for enhancing the pilot's ability to navigate and control the aircraft on the ground is described. The system uses the onboard airport database and images acquired by external sensors. Additional navigation information needed by the system is provided by the Inertial Navigation System and the Global Positioning System. The various functions of the system, such as image enhancement, map generation, obstacle detection, collision avoidance, guidance, etc., are identified. The available technologies, some of which were developed at NASA, that are applicable to the aircraft ground navigation problem are noted. Example images of a truck crossing the runway while the aircraft flies close to the runway centerline are described. These images are from a sequence of images acquired during one of the several flight experiments conducted by NASA to acquire data to be used for the development and verification of the synthetic vision concepts. These experiments provide a realistic database including video and infrared images, motion states from the Inertial Navigation System and the Global Positioning System, and camera parameters.

Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties.

PubMed

Shao, Feng; Li, Kemeng; Lin, Weisi; Jiang, Gangyi; Yu, Mei; Dai, Qionghai

2015-10-01

Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Iris Recognition: The Consequences of Image Compression

NASA Astrophysics Data System (ADS)

Ives, Robert W.; Bishop, Daniel A.; Du, Yingzi; Belcher, Craig

2010-12-01

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected.

Infrared imagery acquisition process supporting simulation and real image training

NASA Astrophysics Data System (ADS)

O'Connor, John

2012-05-01

The increasing use of infrared sensors requires development of advanced infrared training and simulation tools to meet current Warfighter needs. In order to prepare the force, a challenge exists for training and simulation images to be both realistic and consistent with each other to be effective and avoid negative training. The US Army Night Vision and Electronic Sensors Directorate has corrected this deficiency by developing and implementing infrared image collection methods that meet the needs of both real image trainers and real-time simulations. The author presents innovative methods for collection of high-fidelity digital infrared images and the associated equipment and environmental standards. The collected images are the foundation for US Army, and USMC Recognition of Combat Vehicles (ROC-V) real image combat ID training and also support simulations including the Night Vision Image Generator and Synthetic Environment Core. The characteristics, consistency, and quality of these images have contributed to the success of these and other programs. To date, this method has been employed to generate signature sets for over 350 vehicles. The needs of future physics-based simulations will also be met by this data. NVESD's ROC-V image database will support the development of training and simulation capabilities as Warfighter needs evolve.

An integrated multimedia medical information network system.

PubMed

Yamamoto, K; Makino, J; Sasagawa, N; Nagira, M

1998-01-01

An integrated multimedia medical information network system at Shimane Medical university has been developed to organize medical information generated from each section and provide information services useful for education, research and clinical practice. The report describes the outline of our system. It is designed to serve as a distributed database for electronic medical records and images. We are developing the MML engine that is to be linked to the world wide web (WWW) network system. To the users, this system will present an integrated multimedia representation of the patient records, providing access to both the image and text-based data required for an effective clinical decision making and medical education.

Combining 1D and 2D linear discriminant analysis for palmprint recognition

NASA Astrophysics Data System (ADS)

Zhang, Jian; Ji, Hongbing; Wang, Lei; Lin, Lin

2011-11-01

In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Modis, SeaWIFS, and Pathfinder funded activities

NASA Technical Reports Server (NTRS)

Evans, Robert H.

1995-01-01

MODIS (Moderate Resolution Imaging Spectrometer), SeaWIFS (Sea-viewing Wide Field Sensor), Pathfinder, and DSP (Digital Signal Processor) objectives are summarized. An overview of current progress is given for the automatic processing database, client/server status, matchup database, and DSP support.

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

PubMed

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

2016-01-28

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

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

PubMed Central

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

2016-01-01

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

Effect of routine diagnostic imaging for patients with musculoskeletal disorders: A meta-analysis.

PubMed

Karel, Yasmaine H J M; Verkerk, Karin; Endenburg, Silvio; Metselaar, Sven; Verhagen, Arianne P

2015-10-01

The increasing use of diagnostic imaging has led to high expenditures, unnecessary invasive procedures and/or false-positive diagnoses, without certainty that the patients actually benefit from these imaging procedures. This review explores whether diagnostic imaging leads to better patient-reported outcomes in individuals with musculoskeletal disorders. Databases were searched from inception to September 2013, together with scrutiny of selected bibliographies. Trials were eligible when: 1) a diagnostic imaging procedure was compared with any control group not getting or not receiving the results of imaging; 2) the population included individuals suffering from musculoskeletal disorders, and 3) if patient-reported outcomes were available. Primary outcome measures were pain and function. Secondary outcome measures were satisfaction and quality of life. Subgroup analysis was done for different musculoskeletal complaints and high technological medical imaging (MRI/CT). Eleven trials were eligible. The effects of diagnostic imaging were only evaluated in patients with low back pain (n=7) and knee complaints (n=4). Overall, there was a moderate level of evidence for no benefit of diagnostic imaging on all outcomes compared with controls. A significant but clinically irrelevant effect was found in favor of no (routine) imaging in low back pain patients in terms of pain severity at short [SMD 0.17 (0.04-0.31)] and long-term follow-up [SMD 0.13 (0.02-0.24)], and for overall improvement [RR 1.15 (1.03-1.28)]. Subgroup analysis did not significantly change these results. These results strengthen the available evidence that routine referral to diagnostic imaging by general practitioners for patients with knee and low back pain yields little to no benefit. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Foveation: an alternative method to simultaneously preserve privacy and information in face images

NASA Astrophysics Data System (ADS)

Alonso, Víctor E.; Enríquez-Caldera, Rogerio; Sucar, Luis Enrique

2017-03-01

This paper presents a real-time foveation technique proposed as an alternative method for image obfuscation while simultaneously preserving privacy in face deidentification. Relevance of the proposed technique is discussed through a comparative study of the most common distortions methods in face images and an assessment on performance and effectiveness of privacy protection. All the different techniques presented here are evaluated when they go through a face recognition software. Evaluating the data utility preservation was carried out under gender and facial expression classification. Results on quantifying the tradeoff between privacy protection and image information preservation at different obfuscation levels are presented. Comparative results using the facial expression subset of the FERET database show that the technique achieves a good tradeoff between privacy and awareness with 30% of recognition rate and a classification accuracy as high as 88% obtained from the common figures of merit using the privacy-awareness map.

Visual affective classification by combining visual and text features.

PubMed

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task.

Visual affective classification by combining visual and text features

PubMed Central

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task. PMID:28850566

Palmprint Based Verification System Using SURF Features

NASA Astrophysics Data System (ADS)

Srinivas, Badrinath G.; Gupta, Phalguni

This paper describes the design and development of a prototype of robust biometric system for verification. The system uses features extracted using Speeded Up Robust Features (SURF) operator of human hand. The hand image for features is acquired using a low cost scanner. The palmprint region extracted is robust to hand translation and rotation on the scanner. The system is tested on IITK database of 200 images and PolyU database of 7751 images. The system is found to be robust with respect to translation and rotation. It has FAR 0.02%, FRR 0.01% and accuracy of 99.98% and can be a suitable system for civilian applications and high-security environments.

Filmless PACS in a multiple facility environment

NASA Astrophysics Data System (ADS)

Wilson, Dennis L.; Glicksman, Robert A.; Prior, Fred W.; Siu, Kai-Yeung; Goldburgh, Mitchell M.

1996-05-01

A Picture Archiving and Communication System centered on a shared image file server can support a filmless hospital. Systems based on this architecture have proven themselves in over four years of clinical operation. Changes in healthcare delivery are causing radiology groups to support multiple facilities for remote clinic support and consolidation of services. There will be a corresponding need for communicating over a standardized wide area network (WAN). Interactive workflow, a natural extension to the single facility case, requires a means to work effectively and seamlessly across moderate to low speed communication networks. Several schemes for supporting a consortium of medical treatment facilities over a WAN are explored. Both centralized and distributed database approaches are evaluated against several WAN scenarios. Likewise, several architectures for distributing image file servers or buffers over a WAN are explored, along with the caching and distribution strategies that support them. An open system implementation is critical to the success of a wide area system. The role of the Digital Imaging and Communications in Medicine (DICOM) standard in supporting multi- facility and multi-vendor open systems is also addressed. An open system can be achieved by using a DICOM server to provide a view of the system-wide distributed database. The DICOM server interface to a local version of the global database lets a local workstation treat the multiple, distributed data servers as though they were one local server for purposes of examination queries. The query will recover information about the examination that will permit retrieval over the network from the server on which the examination resides. For efficiency reasons, the ability to build cross-facility radiologist worklists and clinician-oriented patient folders is essential. The technologies of the World-Wide-Web can be used to generate worklists and patient folders across facilities. A reliable broadcast protocol may be a convenient way to notify many different users and many image servers about new activities in the network of image servers. In addition to ensuring reliability of message delivery and global serialization of each broadcast message in the network, the broadcast protocol should not introduce significant communication overhead.