Graph Cuts Framework for Kidney Segmentation with Prior Shape Constraints 1st Edition by Asem M Ali, Aly A Farag, Ayman S El Baz ISBN 9783540757573

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ISBN 10: 
ISBN 13: 9783540757573
Author: Asem M Ali, Aly A Farag, Ayman S El Baz

We propose a novel kidney segmentation approach based on the graph cuts technique. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability region using a Poisson distribution refined by positive and negative Gaussian components. To segment a kidney slice, we align it with the training slices so we can use the distance probabilistic model. Then its gray level is approximated with a LCG with sign-alternate components. The spatial interaction between the neighboring pixels is identified using a new analytical approach. Finally, we formulate a new energy function using both image appearance models and shape constraints. This function is globally minimized using s/t graph cuts to get the optimal segmentation. Experimental results show that the proposed technique gives promising results compared to others without shape constraints.

Graph Cuts Framework for Kidney Segmentation with Prior Shape Constraints 1st Table of contents:

• Introduction
  1.1 Motivation and Background
  1.2 Importance of Kidney Segmentation in Medical Imaging
  1.3 Challenges in Kidney Segmentation
  1.4 Graph Cuts and Prior Shape Constraints in Medical Image Analysis
  1.5 Key Contributions of the Paper
  1.6 Structure of the Paper

• Preliminaries
  2.1 Kidney Anatomy and Imaging Techniques
  2.2 Overview of Graph Cuts for Image Segmentation
  2.3 Shape Constraints in Medical Image Segmentation
  2.4 Related Work in Kidney Segmentation
  2.5 Previous Approaches Using Graph Cuts and Shape Priors

• Graph Cuts Framework
  3.1 Basics of Graph Cuts in Image Segmentation
  3.2 Energy Function for Graph Cuts
  3.3 Incorporating Shape Constraints in the Graph Cuts Framework
  3.4 Optimizing the Graph Cut Algorithm for Kidney Segmentation
  3.5 Advantages and Limitations of Graph Cuts for Segmentation

• Prior Shape Constraints
  4.1 Defining Shape Priors for Kidney Segmentation
  4.2 Types of Shape Constraints: Rigid vs. Non-Rigid
  4.3 Incorporating Statistical Shape Models (e.g., Active Shape Models, Shape Priors)
  4.4 Methods for Integrating Shape Information into Graph Cuts
  4.5 Impact of Shape Priors on Segmentation Accuracy

• Methodology
  5.1 Data Acquisition and Kidney Image Preprocessing
  5.2 Graph Construction for Kidney Segmentation
  5.3 Encoding Prior Shape Information in the Graph
  5.4 Segmentation Pipeline: From Preprocessing to Post-Processing
  5.5 Evaluation Metrics for Segmentation Performance

• Results and Evaluation
  6.1 Experimental Setup and Datasets Used
  6.2 Quantitative Evaluation: Accuracy, Dice Coefficient, and Overlap Measures
  6.3 Comparison with Other Kidney Segmentation Methods
  6.4 Case Studies and Results on Real-World Medical Data
  6.5 Sensitivity to Variations in Shape Priors and Image Quality

• Applications in Clinical Practice
  7.1 Importance of Accurate Kidney Segmentation in Medical Imaging
  7.2 Role in Pre-Surgical Planning and Treatment Planning
  7.3 Kidney Disease Detection and Monitoring (e.g., Tumors, Cysts)
  7.4 Integration with Other Imaging Modalities (e.g., CT, MRI)

• Discussion
  8.1 Insights from Using Graph Cuts with Shape Priors for Kidney Segmentation
  8.2 Limitations and Challenges in the Current Framework
  8.3 Future Directions and Potential Improvements
  8.4 Opportunities for Enhancing Performance with Deep Learning Techniques

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