詳細

Breast cancer is an abnormal growth of cells in the breast, usually in the inner lining of the milk ducts or lobules. It is currently the most common type of cancer in women in developed and developing countries. The number of women affected by breast cancer is gradually increasing and remains as a significant health concern. Researchers are continuously working to develop novel techniques to detect early stages of breast cancer. This book covers breast cancer detection, diagnosis, and treatment using different imaging modalities such as mammography, magnetic resonance imaging, computed tomography, positron emission tomography, ultrasonography, infrared imaging, and other modalities. The information and methodologies presented will be useful to researchers, doctors, teachers, and students in biomedical sciences, medical imaging, and engineering.

Sample Pages (PDF)

Preface
List of Contributors
Acronyms and Abbreviations
1 Detection of Architectural Distortion in Prior Mammograms Using Statistical Measures of Angular Spread
　Rangaraj M. Rangayyan, Shantanu Banik, and J. E. Leo Desautels
　1.1 Introduction
　1.2 Experimental Setup and Database
　1.3 Methods
　　1.3.1 Detection of potential sites of architectural distortion
　　1.3.2 Analysis of angular spread
　　1.3.3 Characterization of angular spread
　　1.3.4 Measures of angular spread
　　1.3.5 Feature selection and pattern classification
　1.4 Results
　　1.4.1 Analysis with various sets of features
　　1.4.2 Statistical significance of differences in ROC analysis
　　1.4.3 Reduction of FPs
　　1.4.4 Statistical significance of the differences in FROC analysis
　　1.4.5 Effects of the initial number of ROIs selected
　1.5 Discussion
　　1.5.1 Comparative analysis with related previous works
　　1.5.2 Comparative analysis with other works
　　1.5.3 Limitations
　1.6 Conclusion
　Acknowlegments
　References
2 Texture-based Automated Detection of Breast Cancer Using Digitized Mammograms: A Comparative Study
　U. Rajendra Acharya, E. Y. K. Ng, Jen-Hong Tan, S. Vinitha Sree, and Jasjit S. Suri
　2.1 Introduction
　2.2 Data Acquisition and Preprocessing
　2.3 Feature extraction
　　2.3.1 Gray level co-occurrence matrix
　　2.3.2 Run length matrix
　2.4 Classifiers
　　2.4.1 Support vector machine
　　2.4.2 Gaussian mixture model
　　2.4.3 Fuzzy Sugeno classifier
　　2.4.4 k-nearest neighbor
　　2.4.5 Probabilistic neural network
　　2.4.6 Decision tree
　2.5 Results
　　2.5.1 Performance measures
　　2.5.2 Receiver operating characteristics
　　2.5.3 Classification results
　　2.5.4 Graphical user interface
　2.6 Discussion
　2.7 Conclusions
　Acknowledgments
　References
3 Case-based Clinical Decision Support for Breast Magnetic Resonance Imaging
　Ye Xu and Hiroyuki Abe
　3.1 Introduction
　3.2 Methodologies
　　3.2.1 Data preparation
　　3.2.2 Block diagram of our case-based approach
　　3.2.3 Features to calculate on breast MRI images
　　3.2.4 Collections for ground truth of similarity from data
　　3.2.5 Evaluation
　3.3 Results and Discussion
　3.4 Conclusion
　References
4 Registration, Lesion Detection, and Discrimination for Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging
　Valentina Giannini, Anna Vignati, Massimo De Luca, Silvano Agliozzo, Alberto Bert, Lia Morra, Diego Persano, Filippo Molinari, and Daniele Regge
　4.1 Introduction
　4.2 Registration
　　4.2.1 Method
　　4.2.2 Results
　4.3 Lesion Detection
　　4.3.1 Method
　　4.3.2 Results
　4.4 Lesion Discrimination
　　4.4.1 Method
　　4.4.2 Results
　4.5 Discussion and Conclusions
　References
5 Advanced Modality Imaging of the Systemic Spread of Breast Cancer
　Cher Heng Tan
　5.1 Introduction
　5.2 Nodal Disease
　　5.2.1 Auxillary nodes
　　5.2.2 Other draining nodes
　5.3 Distant Metastases
　　5.3.1 Pulmonary metastases
　　5.3.2 Bone metastases
　　5.3.3 Liver metastases
　　5.3.4 Brain metastases
　5.4 Treatment Response Evaluation: Response Evaluation Criteria in Solid Tumors (RECIST)
　5.5 Surveillance: To Do or Not To Do?
　5.6 Locoregional Recurrence
　5.7 Summary
　References
6 Nuclear Imaging with PET CT and PET Mammography
　Andrew Eik Hock Tan and Wanying Xie
　6.1 Introduction
　6.2 Breast Cancer Molecular Pathology and PET
　6.3 Diagnosis of Primary Breast Cancers
　6.4 Staging of Breast Cancers
　　6.4.1 Axillary nodal evaluation
　　6.4.2 Mediastinal and internal mammary nodal evaluation
　　6.4.3 Distant metastasis and overall staging impact of FDG PET
　6.5 Response Assessment
　6.6 Conclusion
　References
7 3D Whole-Breast Ultrasonography
　Ruey-Feng Chang and Yi-Wei Shen
　7.1 Introduction
　7.2 3D Whole-Breast Ultrasonography Machines
　7.3 Related Studies of 3D Whole-Breast Ultrasonography
　7.4 Conclusion
　References
8 Diagnosis of Breast Cancer Using Ultrasound
　Chui-Mei Tiu, Yi-Hong Chou, Chung-Ming Chen, and Jie-Zhi Cheng
　8.1 Introduction
　8.2 Instrument Requirements
　　8.2.1 Equipment and transducer
　　8.2.2 Image quality and equipment quality control
　8.3 Examination Technique
　　8.3.1 Patient positioning
　　8.3.2 Scanning technique
　　8.3.3 Doppler imaging and contrast-enhanced US
　　8.3.4 Elastography
　　8.3.5 Image labeling
　8.4 Grayscale Ultrasonic Criteria of Breast Disease
　　8.4.1 General criteria of interpretation
　　8.4.2 Diagnosing cysts
　　8.4.3 Differentiating solid lesions
　　8.4.4 Diagnosing carcinoma
　　8.4.5 Secondary signs of malignancy
　　8.4.6 Evaluation of breast calcifications
　8.5 Considerations in Interpreting US Examination Results
　8.6 Ultrasonography of Malignant Tumors
　　8.6.1 Invasive ductal carcinoma
　　8.6.2 Mucinous carcinoma
　　8.6.3 Medullary carcinoma
　　8.6.4 Invasive lobular carcinoma
　　8.6.5 Ductal carcinoma in situ
　　8.6.6 Lobular carcinoma in situ
　　8.6.7 Inflammatory carcinoma
　　8.6.8 Lymphoma and metastases of the breast
　8.7 Fibrocystic Changes and Breast Cysts
　　8.7.1 Fibrocystic changes and benign
　　8.7.2 Fibroadenomas
　　8.7.3 Fibroadenoma variants
　　8.7.4 Tubular adenomas and lactating adenomas
　　8.7.5 Papilloma
　　8.7.6 Intramammary lymph nodes
　　8.7.7 Hamartomas
　　8.7.8 Lipomas
　　8.7.9 Pseudo-angiomatous stromal hyperplasia
　　8.7.10 Hemangiomascarcinoma
　　8.7.11 Invasive Phyllodes tumors
　　8.7.12 Focal fibrosis
　　8.7.13 Diabetic mastopathy
　　8.7.14 Infections and abscesses of the breast
　8.8 Clinical Usefulness of US-Guided Aspiration and Biopsy
　　8.8.1 Ultrasound-guided breast aspiration
　　8.8.2 Ultrasound-guided breast biopsy
　　8.8.3 Vacuum-assisted biopsy
　8.9 Conclusion
　References
9 Abnormal Lesion Detection from Breast Thermal Images Using Chaos with Lyapunov Exponents
　Mahnaz Etehadtavakol, E. Y. K. Ng, Caro Lucas, and Mohammed Ataei
　9.1 Introduction
　9.2 Time Series
　9.3 Time-Delay Embedding
　9.4 Lyapunov Exponents
　9.5 Computation of the Lyapunov Exponents
　　9.5.1 Polynomial model
　9.6 Generating the Time Series
　9.7 Experimental Results and Discussion
　　9.7.1 Fractal images
　　9.7.2 Real-world IR images
　9.8 Conclusion
　References
10 Intelligent Rule-based Classification of Image Features for Breast Thermogram Analysis
　Gerald Schaefer
　10.1 Introduction
　10.2 Image Features
　10.3 Fuzzy Rule-based Classification
　　10.3.1 Classification algorithm
　　10.3.2 Experimental results
　10.4 Ant Colony Optimization Classification
　　10.4.1 Classification algorithm
　　10.4.2 Experimental results
　10.5 Conclusions
　Acknowledgments
　References
11 Infrared Imaging for Breast Cancer Detection with Proper Selection of Properties: From Acquisition Protocol to Numerical Simulation
　Luciete A. Bezerra, Marília M. Oliveira, Marcus C. Araújo, Mariana J. A. Viana, Ladjane C. Santos, Francisco G. S. Santos, Tiago L. Rolim, Paulo R. M. Lyra, Rita C. F. Lima, Tiago B. Borschartt, Roger Resmini, and Aura Conci
　11.1 Introduction
　11.2 Computer-Aided Diagnosis
　　11.2.1 Standardization in acquiring IR breast images
　　11.2.2 Data storage
　　11.2.3 Breast segmentation
　　11.2.4 Extracting features
　　11.2.5 Classification results
　11.3 Several Approaches for Improving the Numerical Simulation of Temperature Profiles
　　11.3.1 Surrogate geometry of the breast
　　11.3.2 A parametric analysis to investigate IR sensitivity
　　11.3.3 Estimation of some breast and tumor properties
　11.4 Conclusions
　References
12 Diffuse Optical Imaging of the Breast: Recent Progress
　Jun Hui Ho, Jing Dong, and Kijoon Lee
　12.1 Introduction
　12.2 Theory
　　12.2.1 Photon propagation model
　　12.2.2 Diffuse optical spectroscopy
　　12.2.3 Diffuse correlation spectroscopy
　　12.2.4 Diffuse optical tomography
　12.3 Classifiers
　　12.3.1 Diffuse optical spectroscopy
　　12.3.2 Diffuse correlation spectroscopy
　　12.3.3 Diffuse optical tomography
　12.4 Clinical Applications
　　12.4.1 Breast cancer detection/characterization
　　12.4.2 Therapy monitoring
　12.5 Future of DOI of the Breast
　　12.5.1 Structural illumination
　　12.5.2 Spectral derivative
　　12.5.3 New parameters
　12.6 Conclusion
　References
13 Computer Vision Theoretic Approach for Breast Cancer Diagnosis: Commonly Perceived Diagnostic Significance of Cytological Features and Feature Usability Analysis of an Existing Breast Cancer Database
　Hrushikesh Garud, Debdoot Sheet, Jyotirmoy Chatterjee, Manjunatha Mahadevappa, Ajoy Kumar Ray, and Arindam Ghosh
　13.1 Introduction
　13.2 Commonly Perceived Significance of Cytological Features in Breast FNAC
　　13.2.1 Overview of the survey
　　13.2.2 Opinion of the experts
　13.3 Analysis of the Wisconsin Diagnostic Breast Cancer (WDBC) Database
　　13.3.1 Ranking of features using feature usability index
　　13.3.2 Feature selection
　13.4 Conclusions
　References
14 Radiofrequency Ablation of Breast Neoplasms
　José Luis del Cura
　14.1 Introduction
　14.2 Radiofrequency
　　14.2.1 Concept
　　14.2.2 Technical issues
　14.3 Radiofrequency Ablation in the Breast
　14.4 Technique of Ablation
　14.5 Outcomes
　14.6 Complications
　14.7 Conclusions and Future Trends
　References
15 Minimally Invasive Thermal Ablation for Breast Cancer
　Feng Wu
　15.1 Introduction
　15.2 Methods of Thermal Ablation Technique
　　15.2.1 Radiofrequency ablation (RFA)
　　15.2.2 Laser ablation (LA)
　　15.2.3 Microwave ablation (MWA)
　　15.2.4 Cryoablation
　　15.2.5 High-intensity focused ultrasound (HIFU) ablation
　15.3 Scientific Principles of Thermal Ablation
　15.4 Mechanisms of Thermal Ablation
　　15.4.1 Direct thermal and nonthermal effects on tumors
　　15.4.2 Thermal effects on tumor vasculature
　　15.4.3 Indirect effects on tumor
　15.5 Clinical Studies on Thermal Ablation of Breast Cancer
　　15.5.1 Radiofrequency ablation
　　15.5.2 Laser ablation
　　15.5.3 Microwave ablation
　　15.5.4 Cryoablation
　　15.5.5 High-intensity focused ultrasound ablation
　15.6 Antitumor Immune Response after Thermal Ablation
　　15.6.1 Antitumor immune response after RFA
　　15.6.2 Antitumor immune response after LA
　　15.6.3 Antitumor immune response after cryoablation
　　15.6.4 Antitumor immune response after MWA
　　15.6.5 Antitumor immune response after HIFU ablation
　15.7 Summary
　References
16 Correlated Microwave Acoustic Imaging for Breast Cancer Detection
　Yuanjin Zheng, Fei Gao, and Zhiping Lin
　16.1 Introduction
　16.2 Emerging Microwave-based Imaging Modality
　　16.2.1 Dielectric property of biological tissue
　　16.2.2 Microwave imaging
　　16.2.3 Microwave-induced thermo-acoustic imaging
　16.3 Correlated Microwave Acoustic Imaging: Numerical Example
　　16.3.1 Image reconstruction algorithm
　　16.3.2 Numerical simulation results
　16.4 Preliminary Prototyping
　　16.4.1 Collecting microwaves and acoustic waves simultaneously
　　16.4.2 UWB transmitter design
　16.5 Conclusion
　References
17 Diagnostic Sensing of Specific Proteins in Breast Cancer Cells Using Hollow-Core Photonic Crystal Fiber
　Vadakke Matham Murukeshan, Vengalathunadakal Kuttinarayanan Shinoj, Saraswathi Padmanabhan, and Parasuraman Padmanabhan
　17.1 Introduction
　17.2 Photonic Crystal Fibers
　　17.2.1 Refractive-index scaling law
　　17.2.2 Selection of fibers
　17.3 Sensing Mechanism Based on Evanescent Waves
　　17.3.1 Conventional-fiber-based evanescent wave sensing
　　17.3.2 Evanescent wave sensing using HC-PCF
　17.4 Materials and Methods
　　17.4.1 Cell culture and sample preparation
　17.5 Results and Discussion
　　17.5.1 HC-PCF-based fluorescence detection
　17.6 Conclusion
　References
18 Quality Assurance in Digital Mammography
　Kwan-Hoong Ng, Tânia Aparecida Correia Furquim, and Noriah Jamal
　18.1 Introduction
　　18.1.1 Scope
　18.2 Technical Quality Control
　18.3 Testing by Medical Physicists and Equipment Performance
　　18.3.1 Mammography unit assembly evaluation
　　18.3.2 Compression force and thickness accuracy
　　18.3.3 Site technique factors for SDNR (radiographer baseline)
　　18.3.4 Automatic exposure control evaluation
　　18.3.5 Baseline for detector performance
　　18.3.6 Spatial linearity and geometric distortion of the detector
　　18.3.7 Detector ghosting
　　18.3.8 Detector uniformity and artifact evaluation
　　18.3.9 Modulation transfer function
　　18.3.10 Limiting spatial resolution
　　18.3.11 Half-value layer
　　18.3.12 Incident air kerma at the entrance surface of PMMA slabs
　　18.3.13 Mean glandular dose
　　18.3.14 Collimation system
　　18.3.15 Image display quality
　　18.3.16 Laser printer (where applicable)
　　18.3.17 Phantom image quality
　18.4 Technologist Testing
　　18.4.1 Inspection, cleaning, and viewing conditions of monitors and view boxes
　　18.4.2 Laser printer
　　18.4.3 Phantom image quality
　　18.4.4 Digital mammography equipment daily checklist
　　18.4.5 Daily and monthly flat-field phantom image test
　　18.4.6 Visual inspection for artifacts (CR systems only)
　　18.4.7 Image plate erasure (CR systems only)
　　18.4.8 Monitor QC
　　18.4.9 Weekly QC test object and full-field artifacts
　　18.4.10 Safety and function checks of examination room and equipment
　　18.4.11 Repeat image analysis
　　18.4.12 Spatial resolution test (CR and scanning systems only)
Appendix 18.1 ACR Summary of Medical Physicist's and Technologist's QC Tests: General Electric
Appendix 18.2 ACR Summary of Medical Physicist's and Technologist's QC Tests: Hologic
Appendix 18.3 IAEA Safety and Function Checklist of Examination Room and Equipment
References
Index