High Performance Deformable Image Registration Algorithms for Manycore Processors
- 1st Edition - July 15, 2013
- Authors: James Shackleford, Nagarajan Kandasamy, Gregory Sharp
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 4 0 7 7 4 1 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 7 8 8 0 - 2
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
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.
- Demonstrates how to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithms
- Shows how to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUs
- Provides Programming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU
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
- Edition: 1
- Published: July 15, 2013
- Language: English
JS
James Shackleford
Affiliations and expertise
Assistant Professor, Drexel University ECE Dept.NK
Nagarajan Kandasamy
Affiliations and expertise
Drexel University, Philadelphia, PA, USAGS
Gregory Sharp
Affiliations and expertise
Massachusetts General Hospital, Boston, MA, USARead High Performance Deformable Image Registration Algorithms for Manycore Processors on ScienceDirect