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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
[2016]
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| Colección: | Elsevier and MICCAI Society book series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.