| Description |
1 online resource : illustrations (some color) |
| Contents |
Foreword; Preface; Contents; Chapter 1: Introduction; References; Part I: Computerized Diagnosis for Cancer; Chapter 2: Computer-Aided Detection of Lung Cancer; 2.1 Introduction; 2.1.1 CAD Field; 2.1.2 Overview of CADe for Lung Cancer Detection; 2.1.3 Overview of CADx for Lung Cancer Diagnosis; 2.2 Generic Architectures of CADe Schemes; 2.2.1 Generic Architecture; 2.2.2 Enhancement of Lesions in CADe; 2.2.3 False-Positive Reduction; 2.3 Supervised ̀̀Lesion Enhancement ́́MTANN Filter; 2.3.1 Architecture of an MTANN Filter; 2.3.2 Training of an MTANN Filter; 2.3.3 Experiments |
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2.3.3.1 Database of Lung Nodules in CT2.3.3.2 Enhancement of Nodules in the Lungs in CT; 2.3.3.3 A CAD Scheme Incorporating the MTANN Lesion Enhancement; 2.3.4 Results; 2.3.4.1 Enhancement of Nodules in the Lungs on CT Images; 2.4 False-Positive Reduction with MTANNs; 2.4.1 A. Database of Low-Dose CT Images; 2.4.2 Scheme for Lung Nodule Detection in Low-Dose CT; 2.4.3 MTANN for FP Reduction; 2.4.3.1 Architecture; 2.4.3.2 Training of MTANN; 2.4.3.3 Scoring of the MTANN Output for Testing; 2.4.4 Results; 2.4.4.1 MTANN Performance; 2.4.4.2 Performance of a CAD Scheme with MTANN Lesion Enhancer |
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2.4.5 Results for CAD for Thin-Slice CT2.5 Conclusion; References; Chapter 3: Computer-Aided Detection and Differentiation of Breast Cancer on Mammograms; 3.1 Introduction; 3.2 CADe Schemes; 3.2.1 CADe Schemes for Clustered Microcalcifications; 3.2.2 CADe Schemes for Masses; 3.2.3 CADe Schemes for Architectural Distortions; 3.2.4 CADe Schemes for Bilateral Asymmetries; 3.3 Example of CADe Scheme; 3.3.1 Summary; 3.3.2 Materials; 3.3.3 Methods; 3.3.3.1 Overall Scheme for Detecting Clustered Microcalcifications; 3.3.3.2 Filter Bank for Detecting NC and NLC |
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3.3.3.3 Extraction of the Features for Detecting Clustered Microcalcifications3.3.3.4 Detection of ROIs with Clustered Microcalcifications; 3.3.3.5 Evaluation of the Detection Performance; 3.3.4 Detection Performance; 3.4 Usefulness of CADe; 3.5 CADx Schemes; 3.6 Examples of CADx Schemes; 3.6.1 Summary; 3.6.2 Materials; 3.6.3 Methods; 3.6.3.1 Segmentation of Microcalcifications and the Determination of the Cluster Margin; 3.6.3.2 Extraction of Five Objective Features; 3.6.3.3 Identification of the Histological Classifications; 3.6.4 Classification Performance; 3.7 Usefulness of CADx |
| Summary |
This book provides a comprehensive overview of the state-of-the-art computational intelligence research and technologies in computer-assisted radiation therapy based on image engineering. It also traces major technical advancements and research findings in the field of image-based computer-assisted radiation therapy. In high-precision radiation therapies, novel approaches in image engineering including computer graphics, image processing, pattern recognition, and computational anatomy play important roles in improving the accuracy of radiation therapy and assisting decision making by radiation oncology professionals, such as radiation oncologists, radiation technologists, and medical physicists, in each phase of radiation therapy. All the topics presented in this book broaden understanding of the modern medical technologies and systems for image-based computer-assisted radiation therapy. Therefore this volume will greatly benefit not only radiation oncologists and radiologists but also radiation technologists, professors in medical physics or engineering, and engineers involved in the development of products to utilize this advanced therapy |
| Notes |
Includes index |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed February 10, 2017) |
| Subject |
Image-guided radiation therapy.
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Radiotherapy, Image-Guided
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MEDICAL -- Radiology, Radiotherapy & Nuclear Medicine.
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Image-guided radiation therapy
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| Form |
Electronic book
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| Author |
Arimura, Hidetaka, editor
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| ISBN |
9789811029455 |
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9811029458 |
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