Processing And Analysis Of Biomedical Information
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Biomedical Information Technology
The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data. The field continues to evolve with the development of new applications to fit the needs of the biomedicine. From molecular imaging to healthcare knowledge management, the storage, access and analysis of data contributes significantly to biomedical research and practice. All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modelling, processing, registration, visualization, communication, and large-scale biological computing. In addition Biomedical Information Technology contains practical integrated clinical applications for disease detection, diagnosis, surgery, therapy, and biomedical knowledge discovery, including the latest advances in the field, such as ubiquitous M-Health systems and molecular imaging applications. - The world's most recognized authorities give their "best practices" ready for implementation - Provides professionals with the most up to date and mission critical tools to evaluate the latest advances in the field and current integrated clinical applications - Gives new staff the technological fundamentals and updates experienced professionals with the latest practical integrated clinical applications
Handbook of Medical Image Processing and Analysis
The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication.The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries.For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing.Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE. - Includes contributions from internationally renowned authors from leading institutions - NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis. - Provides a complete collection of algorithms in computer processing of medical images - Contains over 60 pages of stunning, four-color images
Advanced Deep Learning Methods for Biomedical Information Analysis (ADLMBIA)
Due to numerous biomedical information sensing devices, such as Computed Tomography (CT), Magnetic Resonance (MR) Imaging, Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. a large amount of biomedical information was gathered these years. However, identifying how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling from the collected data is important for clinical applications and to understand the underlying biological processes. Deep learning approaches have been rapidly developed in recent years, both in terms of methodologies and practical applications. Deep learning techniques provide computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. Deep Learning allows to implicitly capture intricate structures of large-scale data and ideally suited to some of the hardware architectures that are currently available.