000			03039nam a22005055i 4500
001			978-1-4939-0600-0
003			DE-He213
005			20200421111838.0
007			cr nn 008mamaa
008			140416s2014 xxu| s |||| 0|eng d
020			_a9781493906000 _9978-1-4939-0600-0
024	7		_a10.1007/978-1-4939-0600-0 _2doi
050		4	_aT385
050		4	_aTA1637-1638
050		4	_aTK7882.P3
072		7	_aUYQV _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.6 _223
100	1		_aZheng, Yefeng. _eauthor.
245	1	0	_aMarginal Space Learning for Medical Image Analysis _h[electronic resource] : _bEfficient Detection and Segmentation of Anatomical Structures / _cby Yefeng Zheng, Dorin Comaniciu.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2014.
300			_aXX, 268 p. 122 illus., 58 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Marginal Space Learning -- Comparison of Marginal Space Learning and Full Space Learning in 2D -- Constrained Marginal Space Learning -- Part-Based Object Detection and Segmentation -- Optimal Mean Shape for Nonrigid Object Detection and Segmentation -- Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation -- Applications of Marginal Space Learning in Medical Imaging -- Conclusions and Future Work.
520			_aAutomatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications. This book presents an efficient object detection and segmentation framework, called Marginal Space Learning, which runs at a sub-second speed on a current desktop computer, faster than the state-of-the-art. Trained with a sufficient number of data sets, Marginal Space Learning is also robust under imaging artifacts, noise and anatomical variations. The book showcases 35 clinical applications of Marginal Space Learning and its extensions to detecting and segmenting various anatomical structures, such as the heart, liver, lymph nodes and prostate in major medical imaging modalities (CT, MRI, X-Ray and Ultrasound), demonstrating its efficiency and robustness.
650		0	_aComputer science.
650		0	_aRadiology.
650		0	_aArtificial intelligence.
650		0	_aComputer graphics.
650	1	4	_aComputer Science.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aImaging / Radiology.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aComaniciu, Dorin. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781493905997
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4939-0600-0
912			_aZDB-2-SCS
942			_cEBK
999			_c55387 _d55387