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Table of contents
Chapter 1 – Introduction to Medical Image Recognition, Segmentation, and Parsing
Part 1: Automatic Recognition and Detection Algorithms
Chapter 2 – A Survey of Anatomy Detection
Chapter 3 – Robust Multi-Landmark Detection Based on Information Theoretic Scheduling
Chapter 4 – Landmark Detection Using Submodular Functions
Chapter 5 – Random Forests for Localization of Spinal Anatomy
Chapter 6 – Integrated Detection Network for Multiple Object Recognition
Chapter 7 – Organ Detection Using Deep Learning
Part 2: Automatic Segmentation and Parsing Algorithms
Chapter 8 – A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets
Chapter 9 – LOGISMOS: A Family of Graph-Based Optimal Image Segmentation Methods
Chapter 10 – A Context Integration Framework for Rapid Multiple Organ Parsing
Chapter 11 – Multiple-Atlas Segmentation in Medical Imaging
Chapter 12 – An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging
Chapter 13 – Robust and Scalable Shape Prior Modeling via Sparse Representation and Dictionary Learning
Part 3: Recognition, Segmentation and Parsing of Specific Objects
Chapter 14 – Semantic Parsing of Brain MR Images
Chapter 15 – Parsing of the Lungs and Airways
Chapter 16 – Aortic and Mitral Valve Modeling From Multi-Modal Image Data
Chapter 17 – Model-Based 3D Cardiac Image Segmentation With Marginal Space Learning
Chapter 18 – Spine Disk and RIB Centerline Parsing
Chapter 19 – Data-Driven Detection and Segmentation of Lymph Nodes
Chapter 20 – Polyp Segmentation on CT Colonography
Chapter 21 – Detect Cells and Cellular Behaviors in Phase Contrast Microscopy Images
Book Description
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 objects
- Methods and theories for medical image recognition, segmentation and parsing of multiple objects
- Efficient and effective machine learning solutions based on big datasets
- Selected applications of medical image parsing using proven algorithms
Key Features
- Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
- Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
- Includes algorithms for recognizing and parsing of known anatomies for practical applications
Details
- ISBN: 978-0-12-802581-9
- Language: English
- Published: 2016
- Copyright: Copyright © 2016 Elsevier Inc. All rights reserved.
- Imprint: Academic Press
- No. of pages: 542
- DOI: https://doi.org/10.1016/C2014-0-02794-3
- Editors: S. Kevin Zhou
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