Machine Learning and Medical Imaging-1판

Key Features

Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation  
    therapy, landmark detection, imaging genomics, and brain connectomics
Features self-contained chapters with a thorough literature review
Assesses the development of future machine learning techniques and the further application of existing techniques


Description

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. 
It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical 
probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse 
representation/coding, and big data hashing. 
In the second part leading research groups around the world present a wide spectrum of machine learning methods with 
application to different medical imaging modalities, clinical domains, and organs.

The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), 
histology, and microscopy images. 
The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic 
associations. 
Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and 
engineers, advanced undergraduate and graduate students, and clinicians. 



Editor Biographies
Preface
Acknowledgments

Part 1: Cutting-Edge Machine Learning Techniques in Medical Imaging

Chapter 1: Functional connectivity parcellation of  the human brainAbstract

 1.1 Introduction
 1.2 Approaches to Connectivity-Based Brain Parcellation
 1.3 Mixture Model
 1.4 Markov Random Field Model
 1.5 Summary

Chapter 2: Kernel machine regression in neuroimaging geneticsAbstract

 Acknowledgments
 2.1 Introduction
 2.2 Mathematical Foundations
 2.3 Applications
 2.4 Conclusion and Future Directions
 Appendix A Reproducing Kernel Hilbert Spaces
 Appendix B Restricted Maximum Likelihood Estimation

Chapter 3: Deep learning of brain images and its application to multiple sclerosisAbstract

 Acknowledgments
 3.1 Introduction
 3.2 Overview of Deep Learning in Neuroimaging
 3.3 Focus on Deep Learning in Multiple Sclerosis
 3.4 Future Research Needs

Chapter 4: Machine learning and its application in microscopic image analysisAbstract

 4.1 Introduction
 4.2 Detection
 4.3 Segmentation
 4.4 Summary

Chapter 5: Sparse models for imaging geneticsAbstract

 5.1 Introduction
 5.2 Basic Sparse Models
 5.3 Structured Sparse Models
 5.4 Optimization Methods
 5.5 Screening
 5.6 Conclusions

Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentationAbstract

 6.1 Introduction
 6.2 Sparse Coding and Dictionary Learning
 6.3 Patch-Based Dictionary Sparse Coding
 6.4 Application of Dictionary Learning in Medical Imaging
 6.5 Future Directions
 6.6 Conclusion
 Glossary

Chapter 7: Advanced sparsity techniques in magnetic resonance imagingAbstract

 7.1 Introduction
 7.2 Standard Sparsity in CS-MRI
 7.3 Group Sparsity in Multicontrast MRI
 7.4 Tree Sparsity in Accelerated MRI
 7.5 Forest Sparsity in Multichannel CS-MRI
 7.6 Conclusion

Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosisAbstract

 8.1 Introduction
 8.2 Related Work
 8.3 Supervised Hashing for Large-Scale Retrieval
 8.4 Results
 8.5 Discussion and Future Work

Part 2: Successful Applications in Medical Imaging

Chapter 9: Multitemplate-based multiview learning for Alzheimer’s disease diagnosisAbstract

 9.1 Background
 9.2 Multiview Feature Representation With MR Imaging
 9.3 Multiview Learning Methods for AD Diagnosis
 9.4 Experiments
 9.5 Summary

Chapter 10: Machine learning as a means toward precision diagnostics and prognosticsAbstract

 10.1 Introduction
 10.2 Dimensionality Reduction
 10.3 Model Interpretation: From Classification to Statistical Significance Maps
 10.4 Heterogeneity
 10.5 Applications
 10.6 Conclusion

Chapter 11: Learning and predicting respiratory motion from 4D CT lung imagesAbstract

 Acknowledgment
 11.1 Introduction
 11.2 3D/4D CT Lung Image Processing
 11.3 Extracting and Estimating Motion Patterns From 4D CT
 11.4 An Example for Image-Guided Intervention
 11.5 Concluding Remarks

Chapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies
Abstract

 Acknowledgments
 12.1 Introduction
 12.2 Features Extraction: Statistical Distance from Normal Motion
 12.3 Manifold Learning: Characterizing Pathological Deviations from Normality
 12.4 Back to the Clinical Application: Understanding CRT-Induced Changes
 12.5 Discussion/Future Work

Chapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning 
technologiesAbstract

 13.1 Introduction
 13.2 Literature Review
 13.3 Anatomy Landmark Detection
 13.4 Detection of Anatomical Boxes
 13.5 Coarse Organ Segmentation
 13.6 Precise Organ Segmentation
 13.7 Conclusion

Chapter 14: Machine learning in brain imaging genomicsAbstract

 14.1 Introduction
 14.2 Mining Imaging Genomic Associations Via Regression or Correlation Analysis
 14.3 Mining Higher Level Imaging Genomic Associations Via Set-Based Analysis
 14.4 Discussion

Chapter 15: Holistic atlases of functional networks and interactions (HAFNI)Abstract
Acknowledgments

 15.1 Introduction
 15.2 HAFNI for Functional Brain Network Identification
 15.3 HAFNI Applications
 15.4 HAFNI-Based New Methods
 15.5 Future Directions of HAFNI Applications

Chapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning 
studyAbstract

 16.1 Introduction
 16.2 Treatment Outcome Prediction of Patients With TLE
 16.3 Naming Impairment Performance of Patients With TLE


 Index