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乳腺X線圖像分析:乳腺癌風(fēng)險(xiǎn)評(píng)估與計(jì)算機(jī)輔助診斷(英文版)/陳智麗,姚凡,張輝
包郵 乳腺X線圖像分析:乳腺癌風(fēng)險(xiǎn)評(píng)估與計(jì)算機(jī)輔助診斷(英文版)/陳智麗,姚凡,張輝
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乳腺X線圖像分析:乳腺癌風(fēng)險(xiǎn)評(píng)估與計(jì)算機(jī)輔助診斷(英文版)/陳智麗,姚凡,張輝 版權(quán)信息
- ISBN：9787030665096
- 條形碼：9787030665096 ; 978-7-03-066509-6
- 裝幀：一般膠版紙
- 冊(cè)數(shù)：暫無
- 重量：暫無
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乳腺X線圖像分析:乳腺癌風(fēng)險(xiǎn)評(píng)估與計(jì)算機(jī)輔助診斷(英文版)/陳智麗,姚凡,張輝 內(nèi)容簡介
本書主要探討計(jì)算機(jī)視覺和圖像處理技術(shù)在乳腺X線圖像分析領(lǐng)域中的應(yīng)用，主要集中于乳腺癌風(fēng)險(xiǎn)評(píng)估和計(jì)算機(jī)輔助診斷方面。旨在為乳腺X線圖像領(lǐng)域的科研人員，建立一套完整的自動(dòng)化乳腺癌風(fēng)險(xiǎn)評(píng)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(jié)構(gòu)分布信息，并將其有效地應(yīng)用于基于組織密度分布的乳腺癌風(fēng)險(xiǎn)評(píng)估體系，實(shí)現(xiàn)快速、客觀、準(zhǔn)確的自動(dòng)化乳腺癌風(fēng)險(xiǎn)評(píng)估。作者結(jié)合多年來從事該領(lǐng)域研究的經(jīng)驗(yàn)和取得的成果，細(xì)致介紹和講解多種乳腺X線圖像分析方法，包括：乳腺區(qū)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(shí)質(zhì)模式的數(shù)學(xué)模型建立，乳腺組織的局部紋理描述，團(tuán)狀乳腺組織檢測，以及乳腺密度等級(jí)自動(dòng)分類等。本書涉及的所有研究驗(yàn)證工作均依據(jù)乳腺X線圖像靠前標(biāo)準(zhǔn)數(shù)據(jù)庫開展，并結(jié)合本土病例探討所述方法的實(shí)際臨床應(yīng)用價(jià)值，研究成果對(duì)同領(lǐng)域相關(guān)研究具有很好的借鑒價(jià)值。
乳腺X線圖像分析:乳腺癌風(fēng)險(xiǎn)評(píng)估與計(jì)算機(jī)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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