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The objective of this Brief is to provide a solution to the unsolved technical problem in segmentation for the automated bone age assessment system. The task is accomplished by first applying the modified histogram equalized module, then applying the proposed automated anisotropic diffusion technique. It is followed by a novel fuzzy quadruple division scheme to optimize the central segmentation algorithm, and then an additional quality assurance scheme. The designed segmentation framework works without demanding scarce resources such as training sets and skillful operators. The results have shown that the designed framework is capable of separating the soft-tissue and background from the hand bone with high accuracy. This Brief should be especially useful for students and professional researchers in the Biomedical and image processing fields.
Content Level »Research
Keywords »Active Contour Model - Automated Anisotropic Diffusion - Bone Age Assessment - Central Segmentation Algorithm - Clustering Algorithm - Edge Detectors - Fuzzy Quadruple Division - Hand Bone Segmentation - Ossification Development - Region Splitting and Merging - Seeded Region Growing - Thresholding - Watershed Segmentation
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 The conventional segmentation methods 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 22.214.171.124 Modeling of Criteria as Single Modal Objective Beta Function 126.96.36.199 Optimal Solution of the Aggregated Multiple Objectives Function 188.8.131.52 Histogram Decomposition 184.108.40.206 Execution of GHE on Each Sub-Histogram 4.3.2 The Application of Anisotropic Diffusion 220.127.116.11 Parameter-free Diffusion Strength Function 18.104.22.168 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 22.214.171.124 The Framework of the Proposed Scheme 126.96.36.199 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 188.8.131.52 Active Appearance Model 184.108.40.206 The Proposed Framework 220.127.116.11 Interpretation 5.4.2 Segmentation Accuracy 18.104.22.168 Evaluation on Automated Fuzzy Quadruple Division Scheme 22.214.171.124 Evaluation on Quality Assurance Process 126.96.36.199 Accuracy Evaluation of the Proposed Segmentation Framework 5.5 Summary
6 Conclusion and Recommendation 6.1 Conclusion 6.2 Future works