List of Contributors. Preface. Acknowledgement. 1. Digital Image Management in Biomedicine; S.T.C. Wong. 2. Understanding and Using Dicom, the Data Interchange Standard for Biomedical Imaging; W.D. Bidgood, Jr., et al. 3. Multimodal Brain Atlases; A.W. Toga, P. Thompson. 4. The Use of Anatomical Knowledge in Medical Imaging: An Overview of the University of Washington Structural Informatics Group; J.F. Brinkley. 5. A Graphical Database for 3D Reconstruction Supporting (4) Different Geometrical Representations; F.J. Verbeek, D.P. Huijsmans. 6. Ontologies and Models for the Handling of Medical Images: Application to Image Databases; F. Aubry, et al. 7. Advances in Image Database Languages; J.D.N. Dionisio, A.F. Cárdenas. 8. Indexing Large Collections of Tumor-Like Shapes; F. Korn, et al. 9. An Active Medical Information System Using Active Index and Artificial Neural Network; S.-K. Chang, et al. 10. Telematics in Healthcare; S.C. Orphanoudakis, et al. 11. Mission-DBS: A Distributed Multimedia Database System for High-Performance Telemedicine; H.-M. Chen, D.Y.Y. Yun. 12. Wavelet-Based Progressive Transmission and Security Filtering for Medical Image Distribution; J.Z. Wang, et al. 13. Web Access to National Health Survey Text/Image Databases; L.R. Long, G.R. Thomas. 14. Model-Based Mining of Remotely Sensed Data for Environmental and Public Health Applications; S.-C. Li, et al. 15. A Decision Support System Based on Congenital Malformation Image Databases; S. Tsumoto. Index.
Medical Image Databases covers the new technologies of biomedical imaging databases and their applications in clinical services, education, and research. Authors were selected because they are doing cutting-edge basic or technology work in relevant areas. This was done to infuse each chapter with ideas from people actively investigating and developing medical image databases rather than simply review the existing literature. The authors have analyzed the literature and have expanded on their own research. They have also addressed several common threads within their generic topics. These include system architecture, standards, information retrieval, data modeling, image visualizations, query languages, telematics, data mining, and decision supports.
The new ideas and results reported in this volume suggest new and better ways to develop imaging databases and possibly lead us to the next information infrastructure in biomedicine.
Medical Image Databases is suitable as a textbook for a graduate-level course on biomedical imaging or medical image databases, and as a reference for researchers and practitioners in industry.
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