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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
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
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
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
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
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
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