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Marginal space learning for medical image analysis : efficient detection and segmentation of anatomical structures /

Yefeng Zheng, Dorin Comaniciu.

Book Cover
Main Author: Zheng, Yefeng.
Other Names: Comaniciu, Dorin,
Published: New York : Springer, [2014]
Topics: Diagnostic imaging. | Image analysis. | HEALTH & FITNESS / Diseases / General | MEDICAL / Clinical Medicine | MEDICAL / Diseases | MEDICAL / Evidence-Based Medicine | MEDICAL / Internal Medicine | Computer Science. | Computer Imaging, Vision, Pattern Recognition and Graphics. | Imaging / Radiology. | Artificial Intelligence (incl. Robotics).
Genres: Electronic books.
Online Access: SpringerLink Link to Marginal space learning for medical image analysis (access limited to Benedictine University patrons)
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020 |z9781493905997
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020 |a1493906003 (electronic bk.)
024 7 |a10.1007/978-1-4939-0600-0|2doi
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072 7|aMED|x014000|2bisacsh
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100 1 |aZheng, Yefeng.
245 10|aMarginal space learning for medical image analysis :|befficient detection and segmentation of anatomical structures /|cYefeng Zheng, Dorin Comaniciu.
264 1|aNew York :|bSpringer,|c[2014]
264 4|c©2014
300 |a1 online resource :|billustrations (some color)
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
504 |aIncludes bibliographical references and index.
505 0 |aMarginal 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.
588 |aDescription based on print version record.
650 0|aDiagnostic imaging.
650 0|aImage analysis.
650 7|aHEALTH & FITNESS / Diseases / General|2bisacsh
650 7|aMEDICAL / Clinical Medicine|2bisacsh
650 7|aMEDICAL / Diseases|2bisacsh
650 7|aMEDICAL / Evidence-Based Medicine|2bisacsh
650 7|aMEDICAL / Internal Medicine|2bisacsh
650 14|aComputer Science.
650 24|aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 24|aImaging / Radiology.
650 24|aArtificial Intelligence (incl. Robotics).
655 0|aElectronic books.
700 1 |aComaniciu, Dorin,|eauthor.
776 08|iPrint version:|tMarginal Space Learning for Medical Image Analysis|z9781493905997|w(OCoLC)869266990
852 80|beitem|hSpringer|t1
856 40|3SpringerLink|uhttp://libweb.ben.edu/login?source=opac&url=http://dx.doi.org/10.1007/978-1-4939-0600-0|yLink to Marginal space learning for medical image analysis |z(access limited to Benedictine University patrons)
938 |aEBSCOhost|bEBSC|n760919
938 |aYBP Library Services|bYANK|n11788450
994 |a92|bILL

Staff View for: Marginal space learning for medical imag