Robust Computation of Mutual Information Using Spatially Adaptive Meshes 1st Edition by Hari Sundar, Dinggang Shen, George Biros, Chenyang Xu, Christos Davatzikos ISBN 9783540757573

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ISBN 13: 9783540757573
Author: Hari Sundar, Dinggang Shen, George Biros, Chenyang Xu, Christos Davatzikos

We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.

 Robust Computation of Mutual Information Using Spatially Adaptive Meshes 1st Table of contents:

• Introduction
  1.1 Motivation and Background
  1.2 Importance of Mutual Information in Image Registration
  1.3 Challenges in Mutual Information Computation
  1.4 Role of Spatially Adaptive Meshes in Improving MI Accuracy
  1.5 Objectives and Contributions of the Paper
  1.6 Structure of the Paper

• Related Work
  2.1 Overview of Mutual Information in Image Registration
  2.2 Methods for Computing Mutual Information
  2.3 Use of Adaptive Grids and Meshes in Computational Methods
  2.4 Previous Approaches to Robust MI Computation
  2.5 Limitations of Existing MI Computation Techniques

• Problem Formulation
  3.1 Definition and Mathematical Foundation of Mutual Information
  3.2 Issues in Standard MI Computation Techniques
  3.3 Introduction to Spatially Adaptive Meshes
  3.4 Problem Statement and Goals of the Proposed Approach
  3.5 Benefits of Adaptive Meshes in MI Computation

• Methodology
  4.1 Overview of the Proposed Approach
  4.2 Construction of Spatially Adaptive Meshes for MI Computation
  4.3 Adaptation Process: Refinement and Coarsening of Meshes
  4.4 Interpolation and Estimation Techniques on Adaptive Meshes
  4.5 Robustness Considerations for Noise and Image Deformation
  4.6 Computational Complexity and Efficiency
  4.7 Algorithmic Implementation and Optimization

• Experimental Setup and Data Sets
  5.1 Description of Data Sets Used for Evaluation
  5.2 Comparison of Image Registration Tasks: Synthetic vs. Real Data
  5.3 Methodology for Comparing MI Computation Accuracy
  5.4 Benchmarks and Baseline Techniques for Mutual Information
  5.5 Performance Metrics for Robustness and Accuracy Evaluation

• Results
  6.1 Accuracy and Robustness of MI Computation Using Adaptive Meshes
  6.2 Comparison with Standard MI Computation Methods
  6.3 Sensitivity to Noise and Image Artifacts
  6.4 Computational Performance and Efficiency of Adaptive Meshes
  6.5 Visual and Quantitative Results on Image Registration Tasks
  6.6 Evaluation on Real-World and Synthetic Datasets

• Discussion
  7.1 Analysis of Results: Strengths and Limitations of the Approach
  7.2 Insights into the Impact of Adaptive Meshes on MI Computation
  7.3 Challenges in Real-Time Application of Adaptive Meshes
  7.4 Comparison with Other Adaptive Mesh and Grid Methods
  7.5 Implications for Robust Image Registration and Similar Applications

• Applications
  8.1 Medical Image Registration and Analysis
  8.2 Use in Multi-modal Imaging
  8.3 Image Segmentation and Object Detection
  8.4 Remote Sensing and Geospatial Data Registration
  8.5 Future Potential in Machine Learning and Computer Vision

• Conclusion
  9.1 Summary of Key Findings
  9.2 Contributions to Robust Mutual Information Computation
  9.3 Potential for Future Improvements and Extensions
  9.4 Closing Remarks

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