Computational Radiology For Orthopaedic Interventions
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Computational Radiology for Orthopaedic Interventions
This book provides a cohesive overview of the current technological advances in computational radiology, and their applications in orthopaedic interventions. Contributed by the leading researchers in the field, this volume covers not only basic computational radiology techniques such as statistical shape modeling, CT/MRI segmentation, augmented reality and micro-CT image processing, but also the applications of these techniques to various orthopaedic interventional tasks. Details about following important state-of-the-art development are featured: 3D preoperative planning and patient-specific instrumentation for surgical treatment of long-bone deformities, computer assisted diagnosis and planning of periacetabular osteotomy and femoroacetabular impingement, 2D-3D reconstruction-based planning of total hip arthroplasty, image fusion for computer-assisted bone tumor surgery, intra-operative three-dimensional imaging in fracture treatment, augmented reality based orthopaedic interventions and education, medical robotics for musculoskeletal surgery, inertial sensor-based cost-effective surgical navigation, and computer assisted hip resurfacing using patient-specific instrument guides. Edited and authored by leading researchers in the field, this work is an essential reference for biomedical engineers, computer scientists and orthopaedic surgeons to develop or use computational radiology approaches for orthopaedic surgery and interventions.
Handbook of Medical Image Computing and Computer Assisted Intervention
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention
Advances in Computational Techniques for Biomedical Image Analysis
Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation. Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems. - Covers various challenges and common research issues related to biomedical image analysis - Describes advanced computational approaches for biomedical image analysis - Shows how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. - Explores a range of computational algorithms and techniques, such as neural networks, fuzzy sets, and evolutionary optimization - Explores cloud based medical imaging together with medical imaging security and forensics