Handbook of medical image computing and computer assisted intervention 1st Edition- Ebook PDF Instant Delivery – ISBN(s): 9780128161760,0128161760,9780128165867, 0128165863
Product details:
- ISBN-10 : 0128165863
- ISBN-13 : 9780128165867
- Author: S. Kevin Zhou , Daniel Rueckert , Gabor Fichtinger
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.
Table of contents:
Chapter 1: Image synthesis and superresolution in medical imaging
Chapter 2: Machine learning for image reconstruction
Chapter 3: Liver lesion detection in CT using deep learning techniques
Chapter 4: CAD in lung
Chapter 5: Text mining and deep learning for disease classification
Chapter 6: Multiatlas segmentation
Chapter 7: Segmentation using adversarial image-to-image networks
Chapter 8: Multimodal medical volumes translation and segmentation with generative adversarial network
Chapter 9: Landmark detection and multiorgan segmentation: Representations and supervised approaches
Chapter 10: Deep multilevel contextual networks for biomedical image segmentation
Chapter 11: LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
Chapter 12: Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
Chapter 13: Image registration with sliding motion
Chapter 14: Image registration using machine and deep learning
Chapter 15: Imaging biomarkers in Alzheimer’s disease
Chapter 16: Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
Chapter 17: Imaging biomarkers for cardiovascular diseases
Chapter 18: Radiomics
Chapter 19: Random forests in medical image computing
Chapter 20: Convolutional neural networks
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