000			03244nam a22005055i 4500
001			vtls000541528
003			RU-ToGU
005			20210922081958.0
007			cr nn 008mamaa
008			160915s2014 xxu| s |||| 0|eng d
020			_a9781493906000 _9978-1-4939-0600-0
024	7		_a10.1007/978-1-4939-0600-0 _2doi
035			_ato000541528
040			_aSpringer _cSpringer _dRU-ToGU
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. _9446240
245	1	0	_aMarginal Space Learning for Medical Image Analysis _helectronic resource _bEfficient Detection and Segmentation of Anatomical Structures / _cby Yefeng Zheng, Dorin Comaniciu.
260			_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
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. _9155490
650		0	_aRadiology, Medical. _9303290
650		0	_aArtificial intelligence. _9274099
650		0	_aComputer vision. _9274100
650	1	4	_aComputer Science. _9155490
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics. _9274101
650	2	4	_aImaging / Radiology. _9281358
650	2	4	_aArtificial Intelligence (incl. Robotics). _9274102
700	1		_aComaniciu, Dorin. _eauthor. _9446241
710	2		_aSpringerLink (Online service) _9143950
773	0		_tSpringer eBooks
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4939-0600-0
912			_aZDB-2-SCS
999			_c398913