Biomedical Image Analysis
Statistical and Variational Methods
- Author: Aly A. Farag, University of Louisville, Kentucky
Ideal for classroom use and self-study, this book explains the implementation of the most effective modern methods in image analysis, covering segmentation, registration and visualisation, and focusing on the key theories, algorithms and applications that have emerged from recent progress in computer vision, imaging and computational biomedical science. Structured around five core building blocks - signals, systems, image formation and modality; stochastic models; computational geometry; level set methods; and tools and CAD models - it provides a solid overview of the field. Mathematical and statistical topics are presented in a straightforward manner, enabling the reader to gain a deep understanding of the subject without becoming entangled in mathematical complexities. Theory is connected to practical examples in x-ray, ultrasound, nuclear medicine, MRI and CT imaging, removing the abstract nature of the models and assisting reader understanding.Read more
- Requires no advanced mathematics beyond a basic understanding of probability theory and calculus
- Includes detailed descriptions of how to work with Markov models, key to some of the most complicated but effective approaches
- Brings together recent advances from the fields of mathematics, physics, engineering, computer science and the life sciences
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- Date Published: October 2014
- format: Adobe eBook Reader
- isbn: 9781139989138
- contains: 200 b/w illus.
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Overview of biomedical image analysis
Part I. Signals and Systems, Image Formation, and Image Modality:
2. Overview of two-dimensional signals and systems
3. Biomedical imaging modalities
Part II. Stochastic Models:
4. Random variables
5. Random processes
6. Basics of random fields
7. Probability density estimation by linear models
Part III. Computational Geometry:
8. Basics of topology and computational geometry
9. Geometric features extraction
Part IV. Variational Calculus and Level Set Methods:
10. Variational approaches and level sets
Part V. Image Analysis Tools:
11. Segmentation – statistical approach
12. Segmentation – variational approach
13. Basics of registration
14. Variational methods for shape registrations
15. Statistical models of shape and appearance.
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