Vertebra Shape Classification using MLP for Content-Based Image Retrieval

A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system

Keyword spotting for cursive document retrieval

We present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented

Multimedia authoring, development environments, and digital video editing

Multimedia systems integrate text, audio, video, graphics, and other media and allow them to be utilized in a combined and interactive manner. Using this exciting and rapidly developing technology, multimedia applications can provide extensive benefits in a variety of arenas, including research, education, medicine, and commerce. While there are many commercial multimedia development packages, the easy and fast creation of a useful, full-featured multimedia document is not yet a straightforward task. This paper addresses issues in the development of multimedia documents, ranging from user-interface tools that manipulate multimedia documents to multimedia communication technologies such as compression, digital video editing and information retrieval. It outlines the basic steps in the multimedia authoring process and some of the requirements that need to be met by multimedia development environments. It also presents the role of video, an essential component of multimedia systems and the role of programming in digital video editing. A model is described for remote access of distributed video. The paper concludes with a discussion of future research directions and new uses of multimedia documents

A Compact Sift-Based Strategy for Visual Information Retrieval in Large Image Databases

This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transformations. The core assumption is that textured regions are locally planar and stationary. A descriptor with this type of invariance is sufficient to discern and describe a textured area regardless of the viewpoint and lighting in a perspective image, and it permits the identification of similar types of texture in a figure, such as an iris texture on an eye. It also enables to establish the correspondence between texture regions from distinct images acquired from different viewpoints (as, for example, two views of the front of a house), scales and/or subjected to linear transformations such as translation. Experiments have confirmed that the S-SIFT algorithm is a potent tool for a variety of problems in image identification

The Second-Generation Guide Star Catalog: Description and Properties

The GSC-II is an all-sky database of objects derived from the uncompressed DSS that the STScI has created from the Palomar and UK Schmidt survey plates and made available to the community. Like its predecessor (GSC-I), the GSC-II was primarily created to provide guide star information and observation planning support for HST. This version, however, is already employed at some of the ground-based new-technology telescopes such as GEMINI, VLT, and TNG, and will also be used to provide support for the JWST and Gaia space missions as well as LAMOST, one of the major ongoing scientific projects in China. Two catalogs have already been extracted from the GSC-II database and released to the astronomical community. A magnitude-limited (R=18.0) version, GSC2.2, was distributed soon after its production in 2001, while the GSC2.3 release has been available for general access since 2007. The GSC2.3 catalog described in this paper contains astrometry, photometry, and classification for 945,592,683 objects down to the magnitude limit of the plates. Positions are tied to the ICRS; for stellar sources, the all-sky average absolute error per coordinate ranges from 0.2" to 0.28" depending on magnitude. When dealing with extended objects, astrometric errors are 20% worse in the case of galaxies and approximately a factor of 2 worse for blended images. Stellar photometry is determined to 0.13-0.22 mag as a function of magnitude and photographic passbands (B,R,I). Outside of the galactic plane, stellar classification is reliable to at least 90% confidence for magnitudes brighter than R=19.5, and the catalog is complete to R=20.Comment: 52 pages, 33 figures, to be published in AJ August 200

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view. ii

Les mixtures de Dirichlet et leurs apports pour la classification et la recherche d'images par le contenu

Le développement de la médecine moderne dans le domaine des techniques de diagnostic comme la radiologie, l'histopathologie et la tomographie avait comme résultat l'explosion du nombre et de l'importance des images médicales sauvegardées par la majorité des hôpitaux. Afin d'aider les médecins à confirmer leurs diagnostics, plusieurs systèmes de recherche d'images médicales ont vu le jour. La conception de ces systèmes présente plusieurs étapes. Nous pensons que le résumé des bases de données d'images est une étape importante dans chaque système de recherche. En effet, la catégorisation d'une base de données d'images facilite énormément la recherche et permet de localiser les images voulues en un minimum de temps. Dans ce mémoire, nous étudions en un premier temps, les différents problèmes communs à tous les systèmes de recherche d'images à savoir l'indexation, l'extraction des caractéristiques, la définition des mesures de similarités et le retour de pertinence. Nous étudions aussi d'autres catégories de problèmes spécifiques à la recherche d'images. Cette étude est complétée par une analyse des systèmes existants les plus connus. Dans la deuxième partie du mémoire, nous nous intéressons aux mixtures de Dirichlet et comment on peut les exploiter pour la classification, en particulier le résumé des bases de données d'images. Contrairement aux approches classiques qui considèrent la loi normale comme densité, nous utilisons une généralisation de la Dirichlet pour l'adapter plus aux problèmes réels. Notre approche est traduite par un modèle mathématique basé sur le maximum de vraisemblance et la méthode de Fisher. Une interprétation très intéressante de notre méthode, basée sur la statistique géométrique, est donnée. Finalement, nous présentons des évaluations contextuelles et non-contextuelles, qui prouvent la validité de notre méthode

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics