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020			_a9789811029455 _q(electronic bk.)
020			_a9811029458 _q(electronic bk.)
020			_z9789811029431 _q(print)
020			_z9811029431
035			_a(OCoLC)971542225 _z(OCoLC)971594766 _z(OCoLC)971941947 _z(OCoLC)972156842 _z(OCoLC)972255532 _z(OCoLC)972393623 _z(OCoLC)972538661 _z(OCoLC)981869031 _z(OCoLC)1005771234 _z(OCoLC)1012091688
040			_aN$T _cN$T _dGW5XE _dEBLCP _dN$T _dIDEBK _dYDX _dOCLCF _dNJR _dUAB _dCOO _dIOG _dAZU _dVT2 _dUPM _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aRC271.R3 _b2017 EB
245	0	0	_aImage-based computer-assisted radiation therapy _cHidetaka Arimura, editor.
264		1	_aSingapore _bSpringer International Publishing _c2017
300			_a1 recurso en línea _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aForeword; 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 Ḿ́TANN Filter; 2.3.1 Architecture of an MTANN Filter; 2.3.2 Training of an MTANN Filter; 2.3.3 Experiments.
505	8		_a2.3.3.1 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.
505	8		_a2.4.5 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.
505	8		_a3.3.3.3 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.
520	3		_aThis 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.
650		7	_aRadioterapia _2embne _0(OCoLC)fst01745023 _0 _9139979
700	1		_aArimura, Hidetaka, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-981-10-2945-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aSpringer_Medicine_2017
998			_b02/2018 _dz _e- _zSI
999			_c95334 _d95334 _x1