Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objectsMethods and theories for medical image recognition, segmentation and parsing of multiple objectsEfficient and effective machine learning solutions based on big datasetsSelected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objectsPresents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasetsIncludes algorithms for recognizing and parsing of known anatomies for practical applications INDICE: Preface Chapter 1 Introduction to Medical Image Recognition and Parsing Chapter 2 Discriminative Anatomy Detection: Classification vs. Regression Chapter 3: Information Theoretic Landmark Detection Chapter 4: Submodular Landmark Detection Chapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object Recognition Chapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level Sets Chapter 9: Context Integration for Rapid Multiple Organ Parsing Chapter 10: Multi-Atlas Methods and Label Fusion Chapter 11: Multi-Compartment Segmentation Framework Chapter 12: Deformable Segmentation via Sparse Representation and Dictionary Learning Chapter 13: Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection Chapter 14: Whole Brain Anatomical Structure Parsing Chapter 15: Aortic and Mitral Valve Segmentation Chapter 16: Parsing of Heart, Chambers and Coronary Vessels Chapter 17: Spine Segmentation Chapter 18: Parsing of Rib and Knee Bones Chapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy

• ISBN: 978-0-12-802581-9
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 500
• Fecha Publicación: 01/12/2015
• Nº Volúmenes: 1
• Idioma: Inglés