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乳腺X線圖像分析:乳腺癌風險評估與計算機輔助診斷(英文版)/陳智麗,姚凡,張輝 版權信息
- ISBN：9787030665096
- 條形碼：9787030665096 ; 978-7-03-066509-6
- 裝幀：一般膠版紙
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
乳腺X線圖像分析:乳腺癌風險評估與計算機輔助診斷(英文版)/陳智麗,姚凡,張輝 內容簡介
本書主要探討計算機視覺和圖像處理技術在乳腺X線圖像分析領域中的應用，主要集中于乳腺癌風險評估和計算機輔助診斷方面。旨在為乳腺X線圖像領域的科研人員，建立一套完整的自動化乳腺癌風險評估框架，深入分析理解乳腺X線圖像反映出的組織密度、紋理和結構分布信息，并將其有效地應用于基于組織密度分布的乳腺癌風險評估體系，實現快速、客觀、準確的自動化乳腺癌風險評估。作者結合多年來從事該領域研究的經驗和取得的成果，細致介紹和講解多種乳腺X線圖像分析方法，包括：乳腺區域分割，乳腺組織分割，高密度乳腺組織檢測，乳腺組織密度定量分析，乳腺組織密度和實質模式的數學模型建立，乳腺組織的局部紋理描述，團狀乳腺組織檢測，以及乳腺密度等級自動分類等。本書涉及的所有研究驗證工作均依據乳腺X線圖像靠前標準數據庫開展，并結合本土病例探討所述方法的實際臨床應用價值，研究成果對同領域相關研究具有很好的借鑒價值。
乳腺X線圖像分析:乳腺癌風險評估與計算機輔助診斷(英文版)/陳智麗,姚凡,張輝 目錄
Contents
Chapter 1 Introduction 1
1.1 Breast Cancer Status 1
1.2 Mammography 2
1.3 Mammographic Risk Assessment 4
1.3.1 Wolfe’s Four Risk Categories 4
1.3.2 Boyd’s Six Class Categories 5
1.3.3 Four BIRADS Density Categories 5
1.3.4 Tabár’s Five Patterns 5
1.4 CAD in Mammography 7
1.5 Clinical Utility of the Present Research 8
1.6 Focus and Contributions of the Book 8
1.7 Book Outline 10
Chapter 2 A Literature Review of Mammographic Image Analysis 12
2.1 Mammographic Image Segmentation 12
2.1.1 Breast Region Segmentation 12
2.1.2 Breast Density Segmentation 19
2.2 Estimation of Mammographic Density 23
2.3 Characterisation of Mammographic Parenchymal Patterns 28
2.4 Breast Density Classification 33
2.5 Summary 37
Chapter 3 Image Segmentation in Mammography 38
3.1 Breast Region Segmentation in Mammograms 38
3.1.1 Methodology 38
3.1.2 Results and Discussion 42
3.2 A Modified FCM Algorithm for Breast Density Segmentation 49
3.2.1 FCM Algorithms 49
3.2.2 A Modified FCM Algorithm 51
3.2.3 Experimental Results 53
3.3 Topographic Representation Based Breast Density Segmentation 57
3.3.1 Topographic Representation 57
3.3.2 Segmentation of Dense Tissue Regions 59
3.3.3 Breast Density Quantification 61
3.3.4 Results 62
3.4 Summary 64
Chapter 4 Texture Analysis in Mammography 66
4.1 Local Feature Based Texture Representations 66
4.1.1 Local Binary Patterns 67
4.1.2 Local Grey-Level Appearances 67
4.1.3 Basic Image Features 68
4.1.4 Textons 69
4.2 Mammographic Tissue Appearance Modelling 70
4.3 Summary 74
Chapter 5 Multiscale Blob Detection in Mammography 75
5.1 Blob Detection 75
5.1.1 Laplacian of Gaussian 75
5.1.2 Difference of Gaussian 76
5.1.3 Determinant of the Hessian Matrix 76
5.1.4 Hessian-Laplacian 77
5.1.5 Fast-Hessian 77
5.1.6 Salient Region 77
5.2 A Blob Based Representation of Mammographic Parenchymal Patterns 78
5.2.1 Detection of Multiscale Blobs 79
5.2.2 Blob Merging 85
5.2.3 Blob Encoding 88
5.3 Results and Discussion 88
5.4 Summary 93
Chapter 6 Breast Cancer Risk Assessment 95
6.1 Experimental Data 95
6.1.1 MIAS Database 95
6.1.2 DDSM Database 96
6.2 Evaluation Methodology 97
6.2.1 Classification Algorithm 97
6.2.2 Cross-Validation Scheme 98
6.2.3 Result Representation 100
6.3 Evaluating the Proposed Methods 100
6.3.1 Evaluation of Breast Density Segmentation 100
6.3.2 Evaluation of Breast Tissue Appearance Modelling 108
6.3.3 A Combined Modelling of Breast Tissue 112
6.3.4 Evaluation of Blob-Based Representation 115
6.4 Summary 118
Chapter 7 Discussions on Breast Cancer Risk Assessment in Mammography 120
7.1 Comparison of the Proposed Methods 120
7.2 Comparing with Related Publications 126
7.3 Summary 130
Chapter 8 Computer-Aided Diagnosis of Breast Cancer Based on Deep Learning 131
8.1 Literature Review on Deep Learning Based Mammographic Image Analysis 131
8.2 Mass Detection and Classification in Mammograms withaDeepPipeline 135
8.2.1 Dataset Information 136
8.2.2 Model Architecture 139
8.2.3 Training 140
8.2.4 Results & Discussion 140
8.3 Summary 149
Chapter 9 Conclusions 150
9.1 Summary of the Book 150
9.2 Contributions and Novel Aspects 152
9.3 Future Work 154
Bibliography 156
Biography 167
Chapter 1 Introduction 1
1.1 Breast Cancer Status 1
1.2 Mammography 2
1.3 Mammographic Risk Assessment 4
1.3.1 Wolfe’s Four Risk Categories 4
1.3.2 Boyd’s Six Class Categories 5
1.3.3 Four BIRADS Density Categories 5
1.3.4 Tabár’s Five Patterns 5
1.4 CAD in Mammography 7
1.5 Clinical Utility of the Present Research 8
1.6 Focus and Contributions of the Book 8
1.7 Book Outline 10
Chapter 2 A Literature Review of Mammographic Image Analysis 12
2.1 Mammographic Image Segmentation 12
2.1.1 Breast Region Segmentation 12
2.1.2 Breast Density Segmentation 19
2.2 Estimation of Mammographic Density 23
2.3 Characterisation of Mammographic Parenchymal Patterns 28
2.4 Breast Density Classification 33
2.5 Summary 37
Chapter 3 Image Segmentation in Mammography 38
3.1 Breast Region Segmentation in Mammograms 38
3.1.1 Methodology 38
3.1.2 Results and Discussion 42
3.2 A Modified FCM Algorithm for Breast Density Segmentation 49
3.2.1 FCM Algorithms 49
3.2.2 A Modified FCM Algorithm 51
3.2.3 Experimental Results 53
3.3 Topographic Representation Based Breast Density Segmentation 57
3.3.1 Topographic Representation 57
3.3.2 Segmentation of Dense Tissue Regions 59
3.3.3 Breast Density Quantification 61
3.3.4 Results 62
3.4 Summary 64
Chapter 4 Texture Analysis in Mammography 66
4.1 Local Feature Based Texture Representations 66
4.1.1 Local Binary Patterns 67
4.1.2 Local Grey-Level Appearances 67
4.1.3 Basic Image Features 68
4.1.4 Textons 69
4.2 Mammographic Tissue Appearance Modelling 70
4.3 Summary 74
Chapter 5 Multiscale Blob Detection in Mammography 75
5.1 Blob Detection 75
5.1.1 Laplacian of Gaussian 75
5.1.2 Difference of Gaussian 76
5.1.3 Determinant of the Hessian Matrix 76
5.1.4 Hessian-Laplacian 77
5.1.5 Fast-Hessian 77
5.1.6 Salient Region 77
5.2 A Blob Based Representation of Mammographic Parenchymal Patterns 78
5.2.1 Detection of Multiscale Blobs 79
5.2.2 Blob Merging 85
5.2.3 Blob Encoding 88
5.3 Results and Discussion 88
5.4 Summary 93
Chapter 6 Breast Cancer Risk Assessment 95
6.1 Experimental Data 95
6.1.1 MIAS Database 95
6.1.2 DDSM Database 96
6.2 Evaluation Methodology 97
6.2.1 Classification Algorithm 97
6.2.2 Cross-Validation Scheme 98
6.2.3 Result Representation 100
6.3 Evaluating the Proposed Methods 100
6.3.1 Evaluation of Breast Density Segmentation 100
6.3.2 Evaluation of Breast Tissue Appearance Modelling 108
6.3.3 A Combined Modelling of Breast Tissue 112
6.3.4 Evaluation of Blob-Based Representation 115
6.4 Summary 118
Chapter 7 Discussions on Breast Cancer Risk Assessment in Mammography 120
7.1 Comparison of the Proposed Methods 120
7.2 Comparing with Related Publications 126
7.3 Summary 130
Chapter 8 Computer-Aided Diagnosis of Breast Cancer Based on Deep Learning 131
8.1 Literature Review on Deep Learning Based Mammographic Image Analysis 131
8.2 Mass Detection and Classification in Mammograms withaDeepPipeline 135
8.2.1 Dataset Information 136
8.2.2 Model Architecture 139
8.2.3 Training 140
8.2.4 Results & Discussion 140
8.3 Summary 149
Chapter 9 Conclusions 150
9.1 Summary of the Book 150
9.2 Contributions and Novel Aspects 152
9.3 Future Work 154
Bibliography 156
Biography 167
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