Download or read book Medical Image Storage Sysytem [sic] for Metadata-based Retrieval written by Selwyn Onyebuchi Igwe and published by . This book was released on 2011 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.