000			03719nam a22004815i 4500
001			978-1-84800-107-7
003			DE-He213
005			20201213200640.0
007			cr nn 008mamaa
008			100301s2008 xxk| s |||| 0|eng d
020			_a9781848001077 _9978-1-84800-107-7
024	7		_a10.1007/978-1-84800-107-7 _2doi
050		4	_aT385
072		7	_aUML _2bicssc
072		7	_aCOM012000 _2bisacsh
082	0	4	_a006.6 _223
100	1		_aHarders, Matthias. _eauthor.
245	1	0	_aSurgical Scene Generation for Virtual Reality-Based Training in Medicine _h[electronic resource] / _cby Matthias Harders.
264		1	_aLondon : _bSpringer London, _c2008.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Surgical Education -- Geometry -- Relevance to Surgical Education -- Process Elements -- Data Acquisition -- Uterine Image Acquisition -- Healthy Anatomy Generation -- Shape Prediction -- Pathology Integration -- Skeleton-based Design -- Cellular Automata Growth Model -- Particle System Growth Model -- Volumetric Representation -- Appearance -- Texturing in Computer Graphics -- Relevance to Surgical Education -- Data Acquisition and Enhancement -- In-vivo Image Acquisition -- Image Enhancement -- Base Texture Generation -- Texture Generation for Laparoscopic Simulation -- Texture Generation for Hysteroscopic Simulation -- Embedding of Specks into Liver Textures -- Biomechanics -- Deformation Models -- Tissue Parameter Acquisition -- Genetic Optimization Approach -- 2D Topology Optimization -- Extension to 3D Topology Identification -- Analytical Derivation -- Derivation for Constant Strain Triangle -- Derivation for Tetrahedral Element -- Conclusion -- Hysteroscopy Simulation -- Extension: Vessel Generation.
520			_aOne of the most important elements needed for effective training in Virtual Reality (VR) is the generation of variable scenarios. Without this, trainees quickly become familiar with a scene and the natural variations encountered in real-life situations cannot be reproduced. Generating such models in VR-based applications is difficult, but with the increase in computational power (allowing for larger and more finely-detailed virtual environments) there is an increasing demand for improved methods for model acquisition, enhancement, optimization and adaptation. The field of medicine lends itself very well to VR-based training particularly in the area of surgery. In this book Matthias Harders examines the main components needed when defining effective scenarios: • scene geometry • organ appearance • biomechanical parameters providing an extensive overview of related work and introducing specific solutions in detail. With plenty of examples to show the outcome and performance of the methods presented in the book, this will be an essential resource for all those involved in generating training scenarios in medical education, as well as in VR-based training in general.
650		0	_aComputer science.
650		0	_aSurgery.
650		0	_aComputer graphics.
650		0	_aComputer vision.
650		0	_aMedical Education.
650	1	4	_aComputer Science.
650	2	4	_aComputer Graphics.
650	2	4	_aGeneral Surgery.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aMedical Education.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781848001060
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84800-107-7
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c13212 _d13212