03826nam 22006735 450 991029904670332120200704232946.01-4939-0600-310.1007/978-1-4939-0600-0(CKB)2560000000148539(EBL)1730896(OCoLC)902412176(SSID)ssj0001199749(PQKBManifestationID)11763871(PQKBTitleCode)TC0001199749(PQKBWorkID)11204787(PQKB)10276430(MiAaPQ)EBC1730896(DE-He213)978-1-4939-0600-0(PPN)178317551(EXLCZ)99256000000014853920140416d2014 u| 0engur|n|---|||||txtccrMarginal Space Learning for Medical Image Analysis[electronic resource] Efficient Detection and Segmentation of Anatomical Structures /by Yefeng Zheng, Dorin Comaniciu1st ed. 2014.New York, NY :Springer New York :Imprint: Springer,2014.1 online resource (284 p.)Description based upon print version of record.1-322-03779-5 1-4939-0599-6 Includes bibliographical references and index at the end of each chapters.Introduction -- 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.Automatic 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.Optical data processingRadiologyArtificial intelligenceComputer Imaging, Vision, Pattern Recognition and Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22005Imaging / Radiologyhttps://scigraph.springernature.com/ontologies/product-market-codes/H29005Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Optical data processing.Radiology.Artificial intelligence.Computer Imaging, Vision, Pattern Recognition and Graphics.Imaging / Radiology.Artificial Intelligence.004006.3006.6616.0754Zheng Yefengauthttp://id.loc.gov/vocabulary/relators/aut941827Comaniciu Dorinauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299046703321Marginal Space Learning for Medical Image Analysis2124959UNINA