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洋書 | 技術書

Multimodality Breast Imaging: Diagnosis and Treatment
商品コード: 9780819492944

Multimodality Breast Imaging: Diagnosis and Treatment

販売価格(税込) 13,400 円
ポイント: 134 Pt
関連カテゴリ:

洋書 > 技術書

出版社別 > SPIE

個  数

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E. Y. K. Ng; U. Rajendra Acharya; Rangaraj M. Rangayyan; Jasjit S. Suri
572 pages; Hardcover
2013/3/4
PM227

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

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