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

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