| Description |
1 online resource : illustrations |
| Series |
The Elsevier and MICCAI society book series |
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Elsevier and MICCAI Society book series.
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| Contents |
Front Cover; Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches; Copyright; Contents; Foreword; Acknowledgments; Contributors; Chapter 1: Introduction to Medical Image Recognition; 1.1 Introduction; 1.2 Challenges and Opportunities; 1.3 Rough-to-Exact Object Representation; 1.4 Simple-to-Complex Probabilistic Modeling; 1.4.1 Chain Rule; 1.4.2 Bayes' Rule and the Equivalence of Probabilistic Modelingand Energy-Based Method; 1.4.3 Practical Medical Image Recognition, Segmentation, and Parsing Algorithms |
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1.5 Medical Image Recognition Using Machine Learning Methods1.5.1 Object Detection and Context; 1.5.2 Machine Learning Methods; 1.5.2.1 Classification; 1.5.2.2 Regression; 1.6 Medical Image Segmentation Methods; 1.6.1 Simple Image Segmentation Methods; 1.6.2 Active Contour Method; 1.6.3 Variational Methods; 1.6.4 Level Set Methods; 1.6.5 Active Shape Models and Active Appearance Models; 1.6.6 Graph Cut Method; 1.7 Conclusions; Recommended Notations; Notes; References; Part 1: AutomaticRecognition and DetectionAlgorithms; Chapter 2: A Survey of Anatomy Detection; 2.1 Introduction |
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2.2 Methods for Detecting an Anatomy2.2.1 Classification-Based Detection Methods; 2.2.1.1 Boosting detection cascade; 2.2.1.2 Probabilistic boosting tree; 2.2.1.3 Randomized decision forest; 2.2.1.4 Exhaustive search to handle pose variation; 2.2.1.5 Parallel, pyramid, and tree structures; 2.2.1.6 Network structure: Probabilistic boosting network; 2.2.1.7 Marginal space learning; 2.2.1.8 Probabilistic, hierarchical, and discriminant framework; 2.2.1.9 Multiple instance boosting to handle inaccurate annotation; 2.2.2 Regression-Based Detection Methods; 2.2.2.1 Shape regression machine |
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2.2.2.2 Hough forest2.2.3 Classification-Based vs Regression-Based Object Detection; 2.3 Methods for Detecting Multiple Anatomies; 2.3.1 Classification-Based Methods; 2.3.1.1 Discriminative anatomical network; 2.3.1.2 Active scheduling; 2.3.1.3 Submodular detection; 2.3.1.4 Integrated detection network; 2.3.2 Regression-Based Method: Regression Forest; 2.3.3 Combining Classification and Regression: Context Integration; 2.4 Conclusions; References; Chapter 3: Robust Multi-Landmark Detection Based on Information Theoretic Scheduling; 3.1 Introduction; 3.2 Literature Review; 3.3 Methods |
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3.3.1 Problem Statement3.3.2 Scheduling Criterion Based on Information Gain; 3.3.3 Monte-Carlo Simulation Method for the Evaluation of Information Gain; 3.3.4 Implementation; Learning-based landmark detection; Spatial correlation across landmarks; 3.4 Applications; 3.4.1 Automatic View Identification of Radiographs; 3.4.2 Auto-Alignment for MR Knee Scan Planning; 3.4.3 Auto-Navigation for Anatomical Measurement in CT; 3.4.4 Automatic Vertebrae Labeling; 3.4.5 Virtual Attenuation Correction of Brain PET Images; 3.4.6 Bone Segmentation in MR for PET-MR Attenuation Correction; 3.5 Conclusion |
| Summary |
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. You will learn how to: 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. -- Edited summary from book |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed December 18, 2015) |
| Subject |
Imaging systems in medicine.
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Machine learning.
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Image reconstruction.
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Pattern recognition systems.
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Image Processing, Computer-Assisted
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Image Interpretation, Computer-Assisted
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Diagnostic Imaging -- methods
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Machine Learning
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Pattern Recognition, Automated
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HEALTH & FITNESS -- Diseases -- General.
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MEDICAL -- Clinical Medicine.
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MEDICAL -- Diseases.
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MEDICAL -- Evidence-Based Medicine.
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MEDICAL -- Internal Medicine.
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Pattern recognition systems
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Image reconstruction
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Imaging systems in medicine
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Machine learning
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| Form |
Electronic book
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| Author |
Zhou, S. Kevin, editor
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| ISBN |
9780128026762 |
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0128026766 |
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