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020			_a4431559760 _q(electronic bk.)
020			_a9784431559764 _q(electronic bk.)
020			_z4431559744 _q(print)
020			_z9784431559740 _q(print)
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050		4	_aQM28 _b.C667 2017 EB
245	0	0	_aComputational anatomy based on whole body imaging : _bbasic principles of computer-assisted diagnosis and therapy _cHidefumi Kobatake, Yoshitaka Masutani, editors.
264		1	_aTokyo, Japan _bSpringer _c[2017]
264		4	_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
500			_aSpringerLink
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Acknowledgments; Contents; 1 Introduction; 1.1 What Is Computational Anatomy?; 1.2 Needs, Seeds, and Solutions Around Medical Imaging: History and Perspectives; 1.2.1 Needs in Medical Education and Clinical Practice; 1.2.1.1 From the Viewpoint of Medical Education; 1.2.1.2 From the Viewpoint of Diagnostic Radiology; 1.2.1.3 From the Viewpoint of Therapeutic Radiology; 1.2.1.4 From the Viewpoint of Surgery; 1.2.2 Seeds and Solutions in Science, Technology, and Engineering; 1.3 Whole-Body Computational Anatomy; 1.3.1 Impact of Whole-Body Imaging.
505	8		_a1.3.2 Toward Complete Medical Image Understanding1.4 Book Organization; References; 2 Fundamental Theories and Techniques; 2.1 From Anatomy to Computational Anatomy; 2.1.1 Introduction; 2.1.2 Simple Examples; 2.1.2.1 Outline of ASM; 2.1.2.2 Required Techniques; 2.2 Mathematical Foundation; 2.2.1 Signal Processing; 2.2.1.1 Digital Images; 2.2.1.2 Linear Operation; 2.2.1.3 Convolution; 2.2.1.4 Cross Correlation; 2.2.1.5 Fourier Series Expansion; 2.2.1.6 Differentiation of Discrete Signals; 2.2.2 Fundamental Transformations; 2.2.2.1 Coordinate Transformation; 2.2.2.2 Linear Subspace.
505	8		_a2.2.2.3 Affine Transformation2.2.2.4 Singular Value Decomposition; 2.2.2.5 Principal Component Analysis; 2.2.3 Probability and Statistics: Foundations of CA; 2.2.3.1 Sum Rule and Product Rule of Probability; 2.2.3.2 Expectation and Variance; 2.2.3.3 Gaussian Distribution; 2.2.4 Foundations of Pattern Recognition; 2.2.4.1 Bayes Decision Theory; 2.2.4.2 Classifier Design; 2.3 Computational Anatomical Model; 2.3.1 Models for Segmentation; 2.3.2 Geometrical Representation; 2.3.2.1 Representation Using Functions of Voxels; 2.3.2.2 Representation Using Parametric Functions; 2.3.2.3 Curves.
505	8		_a2.3.2.4 Surfaces2.3.2.5 Registration Required Before Measurement or Analysis; 2.3.3 Image Features and Landmarks; 2.3.3.1 Anatomical Landmarks; 2.3.3.2 Keypoints; 2.3.3.3 Edges and Ridges; 2.3.4 Diffeomorphism Frameworks; 2.3.4.1 LDDMM Framework for Registration; 2.3.4.2 SVF Framework; 2.3.4.3 Statistical Analysis on Shape Manifold; 2.3.4.4 Applications and Future Works; 2.3.5 Computational Anatomy and Registration; 2.3.5.1 Probabilistic Atlas; 2.3.5.2 SSMs; 2.3.6 CA-Based Segmentation; 2.3.6.1 Probabilistic Atlas-Based Segmentation; 2.3.6.2 Active Shape Model; 2.3.6.3 Level Set with CA.
505	8		_a2.3.6.4 Graph-Cuts with CA2.3.6.5 Ensemble Learning with CA; 2.3.7 Multiple Organs, Anomaly, and Lesions; 2.3.7.1 Multiple Organs; 2.3.7.2 Anatomical Anomaly; References; 3 Understanding Medical Images Based on Computational Anatomy Models; 3.1 Introduction; 3.2 Bone; 3.3 Skeletal Muscle; 3.3.1 Anatomical Modeling of Skeletal Muscles; 3.3.1.1 Muscle Distribution Model; 3.3.1.2 SSM; 3.4 Lymph Nodes; 3.4.1 Overview of Lymph Node Segmentation on Medical Images; 3.4.2 Overview of Lymph Node Segmentation from Abdominal CT Images; 3.4.2.1 Preprocessing; 3.4.2.2 Blob-Like Structure Enhancement.
520	3		_aThis book deals with computational anatomy, an emerging discipline recognized in medical science as a derivative of conventional anatomy. It is also a completely new research area on the boundaries of several sciences and technologies, such as medical imaging, computer vision, and applied mathematics. Computational Anatomy Based on Whole Body Imaging highlights the underlying principles, basic theories, and fundamental techniques in computational anatomy, which are derived from conventional anatomy, medical imaging, computer vision, and applied mathematics, in addition to various examples of applications in clinical data. The book will cover topics on the basics and applications of the new discipline. Drawing from areas in multidisciplinary fields, it provides comprehensive, integrated coverage of innovative approaches to computational anatomy. As well, Computational Anatomy Based on Whole Body Imaging serves as a valuable resource for researchers including graduate students in the field and a connection with the innovative approaches that are discussed. Each chapter has been supplemented with concrete examples of images and illustrations to facilitate understanding even for readers unfamiliar with computational anatomy.
588	0		_aOnline resource; title from PDF title page (SpringerLink, viewed June 21, 2017).
988			_aEBOOK, EBSPRINGER_2017D
650		7	_2embne _aAnatomía humana _9138474
700	1		_aKobatake, Hidefumi, _eeditor literario
700	1		_aMasutani, Yoshitaka, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-4-431-55976-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI