Main description:

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.

Contents:

Introduction 1. Historical perspective on Medical Image Analysis 2. Brief Introduction to Medical Imaging Modalities 3. Mathematical Preliminaries and Notation

Image Representation 4. Image Representation and Filtering 5. Multi-scale Image Representation 6. Multi-resolution Image Representation 7. Multi-dimensional Image Representation

Feature Detection, Analysis and Tracking 8. Feature Detection and Texture Analysis 9. Tracking of Salient Points

Image Segmentation 10. Clustering-based Segmentation 11. Statistical Model-based Segmentation 12. Implicit Model-based Segmentation 13. Segmentation of Tree-like Structures

Image Registration 14. Points and Surface Registration 15. Graph Matching 16. Volumetric Rigid Registration 17. Volumetric Non-Rigid Registration 18. Image Mosaicking

Motion, Deformation and Growth Analysis 19. Motion Recovery and Analysis 20. Deformation Recovery and Analysis 21. Development, degeneration, and growth analysis

Medical Image Analysis in Large Scale Databases 22. Detection and counting in large databases 23. Segmentation and classification 24. Population atlases and variability

Design and Validation of Medical Image Analysis Methods 25. Designing Translational Solutions in Medical Image Analysis 26. Assessment of Medical Image Computing Methods