Description
Springer Segmentation of Hand Bone for Bone Age Assessment by Yan Chai Hum
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._x000D_ Table of contents :- _x000D_
1 Introduction_x000D_
1.1 Introduction_x000D_
1.2 Background of the problem_x000D_
1.3 Problem statements_x000D_
1.4 Objectives of the book/brief_x000D_
1.5 Scopes_x000D_
1.6 Provided information and insights_x000D_
1.7 Book Organization_x000D_
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2 The conventional segmentation methods_x000D_
2.1 Introduction_x000D_
2.2 Thresholding_x000D_
2.2.1 Global Thresholding_x000D_
2.2.2 Adaptive Thresholding_x000D_
2.2.3 Dynamic Thresholding_x000D_
2.2.4 Automated Thresholding_x000D_
2.2.5 Summary_x000D_
2.3 Edge-based_x000D_
2.3.1 Edge Detectors_x000D_
2.3.2 Edge linking_x000D_
2.3.3 Summary_x000D_
2.4 Region-based_x000D_
2.4.1 Seeded Region Growing_x000D_
2.4.2 Region Splitting and Merging_x000D_
2.4.3 Summary_x000D_
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3 The advanced segmentation method_x000D_
3.1 Hybrid-based_x000D_
3.1.1 Watershed Segmentation_x000D_
3.1.2 Summary _x000D_
3.2 Deformable Model_x000D_
3.2.1 Active Contour Model_x000D_
3.2.2 Active Shape Model_x000D_
3.2.3 Active Appearance Model_x000D_
3.2.4 Summary_x000D_
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4 The possible solution _x000D_
4.1 Introduction_x000D_
4.2 The Proposed Segmentation Framework_x000D_
4.3 Pre-processing_x000D_
4.3.1 The Proposed MBOBHE_x000D_
4.3.1.1 Modeling of Criteria as Single Modal Objective Beta Function_x000D_
4.3.1.2 Optimal Solution of the Aggregated Multiple Objectives Function_x000D_
4.3.1.3 Histogram Decomposition_x000D_
4.3.1.4 Execution of GHE on Each Sub-Histogram_x000D_
4.3.2 The Application of Anisotropic Diffusion_x000D_
4.3.2.1 Parameter-free Diffusion Strength Function 4.3.2.2 Automated Scale Selection_x000D_
4.4 The Proposed Adaptive Crossed Reconstruction (ACR) Algorithm Design_x000D_
4.4.1 Clustering Algorithm Applied in the Proposed Segmentation Framework_x000D_
4.4.2 Automated Block Division Scheme in Adaptive Segmentation_x000D_
4.4.2.1 The Framework of the Proposed Scheme_x000D_
4.4.2.2 The Mechanism of the Automated Fuzzy Quadruple Division Scheme_x000D_
4.5 Quality Assurance Process_x000D_
4.5.1 Gray Level Intensity of Interest Identification for Elimination_x000D_
4.5.2 Hand Bone Edge Detection Technique Using Entropy_x000D_
4.5.3 The Area Restoration and Elimination Analysis_x000D_
4.6 Summary_x000D_
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5 Result analysis and discussion_x000D_
5.1 Introduction_x000D_
5.2 Performance Evaluation of the Proposed MBOBHE_x000D_
5.3 Anisotropic Diffusion in the Proposed Segmentation Framework_x000D_
5.4 Segmentation Evaluation_x000D_
5.4.1 User-specified parameters_x000D_
5.4.1.1 Active Appearance Model 5.4.1.2 The Proposed Framework _x000D_
5.4.1.3 Interpretation_x000D_
5.4.2 Segmentation Accuracy_x000D_
5.4.2.1 Evaluation on Automated Fuzzy Quadruple Division Scheme _x000D_
5.4.2.2 Evaluation on Quality Assurance Process_x000D_
5.4.2.3 Accuracy Evaluation of the Proposed Segmentation Framework_x000D_
5.5 Summary_x000D_
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6 Conclusion and Recommendation_x000D_
6.1 Conclusion_x000D_
6.2 Future works_x000D_