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008			230328s2009 sz | s |||| 0|eng d
020			_a9783031022456
024	7		_a10.1007/978-3-031-02245-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7.D53 _b2009 EB
100	1		_aActon, Scott Thomas, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686355 _d1966-
245	1	0	_aBiomedical Image Analysis : _bSegmentation _cby Scott Acton, Nilanjan Ray
250			_a1st edition 2009
264		1	_aCham _bSpringer International Publishing _c2009
300			_a1 recurso en línea (VIII, 107 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Image Video and Multimedia Processing _x1559-8144
505	0		_aIntroduction -- Parametric Active Contours -- Active Contours in a Bayesian Framework -- Geometric Active Contours -- Segmentation with Graph Algorithms -- Scale-Space Image Filtering for Segmentation.
520			_aThe sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy platforms, image segmentation is a vital step in analyzing the constituent biological or medical targets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation that is accessible to well-equipped undergraduates, graduate students, and research professionals in the biology, biomedical, medical, and engineering fields. Active model methods that have emerged in the last few years are a focus of the book, including parametric active contour and active surface models, active shape models, and geometric active contours that adapt to the image topology. Additionally, Biomedical Image Analysis: Segmentation details attractive new methods that use graph theory in segmentation of biomedical imagery. Finally, the use of exciting new scale space tools in biomedical image analysis is reported. Table of Contents: Introduction / Parametric Active Contours / Active Contours in a Bayesian Framework / Geometric Active Contours / Segmentation with Graph Algorithms / Scale-Space Image Filtering for Segmentation.
988			_aSynthesis Collection of Technology_2009
650		7	_2embne _9139972 _aDiagnóstico por imagen
700	1		_aRay, Nilanjan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686356
776	0	8	_iPrinted edition: _z9783031011177
776	0	8	_iPrinted edition: _z9783031033735
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02245-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI