Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC.
1. 2.1 Introduction - p. 22 2. 2.2 Imaging: definitions, quality and similarity measures - p. 22 3. 2.3 Vector and matrix theory results - p. 23 4. 2.4 Linear processing and transformed domains - p. 35 5. 2.5 Calculus - p. 44 6. 2.6 Notions on shapes - p. page50 7. 2.7 Exercises - p. page53 8. References - p. 56 9. Chapter 3. Regression and classification - p. 57 10. Chapter 4. Estimation and inference - p. 85 11. Part II: Image representation and processing - p. 111 12. Chapter 5. Image representation and 2D signal processing - p. 115 13. Chapter 6. Image filtering: enhancement and restoration - p. 145 14. Chapter 7. Multiscale and multiresolution analysis - p. 177 15. Part III: Medical image segmentation - p. page197 16. Chapter 8. Statistical shape models - p. 201 17. Chapter 9. Segmentation by deformable models - p. 227 18. Chapter 10. Graph cut-based segmentation - p. 247 19. Part IV: Medical image registration - p. 273 20. Chapter 11. Points and surface registration - p. 277 21. Chapter 12. Graph matching and registration - p. 303 22. Chapter 13. Parametric volumetric registration - p. 331 23. Chapter 14. Non-parametric volumetric registration - p. 355 24. Chapter 15. Image mosaicking - p. 387 25. Part V: Machine learning in medical image analysis - p. 411 26. Chapter 16. Deep learning fundamentals - p. 415 27. Chapter 17. Deep learning for vision and representation learning - p. 451 28. Chapter 18. Deep learning medical image segmentation - p. 475 29. Chapter 19. Machine learning in image registration - p. 501 30. Part VI: Advanced topics in medical image analysis - p. 515 31. Chapter 20. Motion and deformation recovery and analysis - p. 519 32. Chapter 21. Imaging Genetics - p. 549 33. Part VII: Large-scale databases - p. 576 34. Chapter 22. Detection and quantitative enumeration of objects from large images - p. 579 35. Chapter 23. Image retrieval in big image data - p. 609 36. Part VIII: Evaluation in medical image analysis - p. 622 37. Chapter 24. Assessment of image computing methods - p. 625 38. Index - p. 643
Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC.
1. 2.1 Introduction - p. 22 2. 2.2 Imaging: definitions, quality and similarity measures - p. 22 3. 2.3 Vector and matrix theory results - p. 23 4. 2.4 Linear processing and transformed domains - p. 35 5. 2.5 Calculus - p. 44 6. 2.6 Notions on shapes - p. page50 7. 2.7 Exercises - p. page53 8. References - p. 56 9. Chapter 3. Regression and classification - p. 57 10. Chapter 4. Estimation and inference - p. 85 11. Part II: Image representation and processing - p. 111 12. Chapter 5. Image representation and 2D signal processing - p. 115 13. Chapter 6. Image filtering: enhancement and restoration - p. 145 14. Chapter 7. Multiscale and multiresolution analysis - p. 177 15. Part III: Medical image segmentation - p. page197 16. Chapter 8. Statistical shape models - p. 201 17. Chapter 9. Segmentation by deformable models - p. 227 18. Chapter 10. Graph cut-based segmentation - p. 247 19. Part IV: Medical image registration - p. 273 20. Chapter 11. Points and surface registration - p. 277 21. Chapter 12. Graph matching and registration - p. 303 22. Chapter 13. Parametric volumetric registration - p. 331 23. Chapter 14. Non-parametric volumetric registration - p. 355 24. Chapter 15. Image mosaicking - p. 387 25. Part V: Machine learning in medical image analysis - p. 411 26. Chapter 16. Deep learning fundamentals - p. 415 27. Chapter 17. Deep learning for vision and representation learning - p. 451 28. Chapter 18. Deep learning medical image segmentation - p. 475 29. Chapter 19. Machine learning in image registration - p. 501 30. Part VI: Advanced topics in medical image analysis - p. 515 31. Chapter 20. Motion and deformation recovery and analysis - p. 519 32. Chapter 21. Imaging Genetics - p. 549 33. Part VII: Large-scale databases - p. 576 34. Chapter 22. Detection and quantitative enumeration of objects from large images - p. 579 35. Chapter 23. Image retrieval in big image data - p. 609 36. Part VIII: Evaluation in medical image analysis - p. 622 37. Chapter 24. Assessment of image computing methods - p. 625 38. Index - p. 643
