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Large image databases have been used in various applications and in recent years. The major prerequisite of these databases is the means by which their contents can be indexed and retrieved. This dissertation presents an attempt to improve image storage using image data representation model that integrates both metadata and content-based image description in ORDBMS (Object Relational Database Management systems). This dissertation research is verified using real examples from the medical domain. Both image and salient objects are considered in this model. A prototype called MISS (Medical Image Storage System) has been realized to validate the key aspect of the approach used. This research work also presents initial work on the design and implementation of a prototype Medical Image Database System for the core medical image modalities. Based on a novel image data model and an original data repository model, this representation also supports salient object-relational data and adheres to the Digital Imaging and Communication in Medicine (DICOM) Standard. This standard, along with storage, metadata retrieval and processing of medical images are widely studied and researched in the domain of medical imaging. The efficiency of any image retrieval is strongly related to the representation of the model. The better the features of the image are represented in the metadata, the more efficient the image retrieval technique is able to satisfy complex queries. An object-relational database such as Oracle's multimedia formerly known as Oracle interMedia can provide a seamless integration of image data and metadata. A sample prototype application is presented in this research work. This representation can be applied to different application areas using the Object-Relational DBMS model. The Image databases are partitioned into similar images either by using a cluster generation technique, or by using external information about the content of the image. It is also important to state that the research study only focuses on efficient medical imaging storage and retrieval using metadata and not on content-based retrieval (CBR) using image processing techniques.
In hospitals today, medical images are normally processed and saved digitally in Picture Archiving and Communication Systems (PACS) along with some text descriptions within Digital Communication (DICOM) standards. Additional information saved with the image could include a doctor's name, patient identification, etc. This information is used to retrieve medical images, but text query statements frequently ask for information that is not a part of these text descriptors or labels. This situation will obviously have a negative effect on the result of a query submitted to retrieve the image. Low-level image features should help avoid this problem. Low-level features are those that are measurable and can be automatically extracted from an image. These features include color, shape, and texture. This research project investigated a method to link low-level features that can be automatically extracted from the image to high-level features that are represented in the textual Image Retrieval for Medical Application (IRMA) code included in test collection of images provided for this project. The second project goal was to use semantic types included in the IRMA codes (e.g. plain radiography from image modality, reproductive system form biological system facet) to expand text queries so a content-based image retrieval system can respond more effectively to specific queries. We used a machine learning approach to identify the link between low-level features and text descriptions to automatically assign the semantic types from IRMA. We used a standard dataset of images released by the ImageCLEF2005 conference to participating groups. We indexed the whole dataset of 9,000 images using the GNU Image Finding Tool (GIFT), and extracted images features using the same application. We used image features, as well as the manually assigned IRMA classification code to train a multi-class support vector machine (SVM Multi-class). Our results showed that some medical images are easily classified using low-level features. These results also showed that the performance of the classifier was affected by the uneven distribution of images in each class of the ImageCLEF2005 campaign dataset. Where the images were unique in any one of the four main facets of the IRMA code, the classifier identified them correctly.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
This is the second edition of a very popular book on DICOM that introduces this complex standard from a very practical point of view. It is aimed at a broad audience of radiologists, clinical administrators, information technologists, medical students, and lecturers. The book provides a gradual, down to earth introduction to DICOM, accompanied by an analysis of the most common problems associated with its implementation. Compared with the first edition, many improvements and additions have been made, based on feedback from readers. Whether you are running a teleradiology project or writing DICOM software, this book will provide you with clear and helpful guidance. It will prepare you for any DICOM projects or problem solving, and assist you in taking full advantage of multifaceted DICOM functionality.
An essential resource for medical imaging professionals, this book provides everything you need to create exceptional radiology reports. In an accessible and informal style, one of the foremost experts on radiology reporting gives you practical tips for precise image interpretation and clear communication. This book should be required reading for radiologists in training, and is destined to become an indispensable part of every radiologist's library. Topics include: * The virtues of "normal" * How to say "I don't know" * Building a rhetorical foundation * Spatial relationships * Making recommendations * Suggesting clinical correlation * The hedge * Severity straddling * Size matters * Eponyms in radiology * A summary of reporting best practices * How speech recognition works * Optimizing your speech recognition * Templates and macros * The history of radiology reporting * Structured reporting case study * Structured reporting: what you can do today * Standard terminology for the radiology report * How to think about imaging information * Logic, probability, and the radiology report * Decision making in radiology * The radiology report in 2025
Provides an overview and instruction on the evaluation of interactive information retrieval systems with users.