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High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-cor… Read more
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High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.
Developers of image registration algorithms and software, including graduate students, researchers (post-doctoral researchers, research scientists, professionals working in the areas of computer vision, image processing, and medical imaging
Biographies
Chapter 1. Introduction
Information in This Chapter:
1.1 Introduction
1.2 Applications of Deformable Image Registration
1.3 Algorithmic Approaches to Deformable Registration
1.4 Organization of Chapters
References
Chapter 2. Unimodal B-Spline Registration
Information in This Chapter:
2.1 Introduction
2.2 Overview of B-Spline Registration
2.3 B-Spline Registration on the GPU
2.4 Performance Evaluation
2.5 Summary
References
Chapter 3. Multimodal B-Spline Registration
Information in This Chapter
3.1 Introduction
3.2 Using B-Splines to Represent the Deformation Field
3.3 MI as A Cost Function
3.4 Efficient Computation of MI
3.5 Performance Evaluation
3.6 Related Work
3.7 Summary
References
Chapter 4. Analytic Vector Field Regularization for B-spline Parameterized Methods
Information in This Chapter:
4.1 Introduction
4.2 Theory and Mathematical Formalism
4.3 Algorithmic Implementation
4.4 Performance Evaluation
4.5 Summary
References
Chapter 5. Deformable Registration Using Optical-Flow Methods
Information in This Chapter:
5.1 Introduction
5.2 Demons Algorithm for Deformable Registration
5.3 SIMD Version of Demons Algorithm
5.4 Performance Evaluation
5.5 Summary
References
Chapter 6. Plastimatch—An Open-Source Software for Radiotherapy Imaging
Information in This Chapter:
6.1 Introduction
6.2 Overview of Plastimatch
6.3 Licensing
References
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