{
"item_title" : "Segmentation of Hand Bone for Bone Age Assessment",
"item_author" : [" Yan Chai Hum "],
"item_description" : "1 Introduction1.1 Introduction1.2 Background of the problem1.3 Problem statements1.4 Objectives of the book/brief1.5 Scopes1.6 Provided information and insights1.7 Book Organization2 The conventional segmentation methods2.1 Introduction2.2 Thresholding2.2.1 Global Thresholding2.2.2 Adaptive Thresholding2.2.3 Dynamic Thresholding2.2.4 Automated Thresholding2.2.5 Summary2.3 Edge-based2.3.1 Edge Detectors2.3.2 Edge linking2.3.3 Summary2.4 Region-based2.4.1 Seeded Region Growing2.4.2 Region Splitting and Merging2.4.3 Summary 3 The advanced segmentation method3.1 Hybrid-based3.1.1 Watershed Segmentation3.1.2 Summary3.2 Deformable Model3.2.1 Active Contour Model3.2.2 Active Shape Model3.2.3 Active Appearance Model3.2.4 Summary 4 The possible solution4.1 Introduction4.2 The Proposed Segmentation Framework4.3 Pre-processing4.3.1 The Proposed MBOBHE4.3.1.1 Modeling of Criteria as Single Modal Objective Beta Function4.3.1.2 Optimal Solution of the Aggregated Multiple Objectives Function4.3.1.3 Histogram Decomposition4.3.1.4 Execution of GHE on Each Sub-Histogram4.3.2 The Application of Anisotropic Diffusion4.3.2.1 Parameter-free Diffusion Strength Function 4.3.2.2 Automated Scale Selection4.4 The Proposed Adaptive Crossed Reconstruction (ACR) Algorithm Design4.4.1 Clustering Algorithm Applied in the Proposed Segmentation Framework4.4.2 Automated Block Division Scheme in Adaptive Segmentation4.4.2.1 The Framework of the Proposed Scheme4.4.2.2 The Mechanism of the Automated Fuzzy Quadruple Division Scheme4.5 Quality Assurance Process4.5.1 Gray Level Intensity of Interest Identification for Elimination4.5.2 Hand Bone Edge Detection Technique Using Entropy4.5.3 The Area Restoration and Elimination Analysis4.6 Summary 5 Result analysis and discussion5.1 Introduction5.2 Performance Evaluation of the Proposed MBOBHE5.3 Anisotropic Diffusion in the Proposed Segmentation Framework5.4 Segmentation Evaluation5.4.1 User-specified parameters5.4.1.1 Active Appearance Model 5.4.1.2 The Proposed Framework5.4.1.3 Interpretation5.4.2 Segmentation Accuracy5.4.2.1 Evaluation on Automated Fuzzy Quadruple Division Scheme5.4.2.2 Evaluation on Quality Assurance Process5.4.2.3 Accuracy Evaluation of the Proposed Segmentation Framework5.5 Summary 6 Conclusion and Recommendation6.1 Conclusion6.2 Future works",
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Overview
1 Introduction
1.1 Introduction
1.2 Background of the problem
1.3 Problem statements
1.4 Objectives of the book/brief
1.5 Scopes
1.6 Provided information and insights
1.7 Book Organization
2.1 Introduction
2.2 Thresholding
2.2.1 Global Thresholding
2.2.2 Adaptive Thresholding
2.2.3 Dynamic Thresholding
2.2.4 Automated Thresholding
2.2.5 Summary
2.3 Edge-based
2.3.1 Edge Detectors
2.3.2 Edge linking
2.3.3 Summary
2.4 Region-based
2.4.1 Seeded Region Growing
2.4.2 Region Splitting and Merging
2.4.3 Summary 3 The advanced segmentation method
3.1 Hybrid-based
3.1.1 Watershed Segmentation
3.1.2 Summary
3.2 Deformable Model
3.2.1 Active Contour Model
3.2.2 Active Shape Model
3.2.3 Active Appearance Model
3.2.4 Summary 4 The possible solution
4.1 Introduction
4.2 The Proposed Segmentation Framework
4.3 Pre-processing
4.3.1 The Proposed MBOBHE
4.3.1.1 Modeling of Criteria as Single Modal Objective Beta Function
4.3.1.2 Optimal Solution of the Aggregated Multiple Objectives Function
4.3.1.3 Histogram Decomposition
4.3.1.4 Execution of GHE on Each Sub-Histogram
4.3.2 The Application of Anisotropic Diffusion
4.3.2.1 Parameter-free Diffusion Strength Function 4.3.2.2 Automated Scale Selection
4.4 The Proposed Adaptive Crossed Reconstruction (ACR) Algorithm Design
4.4.1 Clustering Algorithm Applied in the Proposed Segmentation Framework
4.4.2 Automated Block Division Scheme in Adaptive Segmentation
4.4.2.1 The Framework of the Proposed Scheme
4.4.2.2 The Mechanism of the Automated Fuzzy Quadruple Division Scheme
4.5 Quality Assurance Process
4.5.1 Gray Level Intensity of Interest Identification for Elimination
4.5.2 Hand Bone Edge Detection Technique Using Entropy
4.5.3 The Area Restoration and Elimination Analysis
4.6 Summary 5 Result analysis and discussion
5.1 Introduction
5.2 Performance Evaluation of the Proposed MBOBHE
5.3 Anisotropic Diffusion in the Proposed Segmentation Framework
5.4 Segmentation Evaluation
5.4.1 User-specified parameters
5.4.1.1 Active Appearance Model 5.4.1.2 The Proposed Framework
5.4.1.3 Interpretation
5.4.2 Segmentation Accuracy
5.4.2.1 Evaluation on Automated Fuzzy Quadruple Division Scheme
5.4.2.2 Evaluation on Quality Assurance Process
5.4.2.3 Accuracy Evaluation of the Proposed Segmentation Framework
5.5 Summary 6 Conclusion and Recommendation
6.1 Conclusion
6.2 Future works
This item is Non-Returnable
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Details
- ISBN-13: 9789814451659
- ISBN-10: 9814451657
- Publisher: Springer
- Publish Date: June 2013
- Dimensions: 9.05 x 6.13 x 0.34 inches
- Shipping Weight: 0.5 pounds
- Page Count: 132
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