03040nam 22006375 450 991030043170332120200629225349.03-319-09928-010.1007/978-3-319-09928-6(CKB)3710000000311729(EBL)1968188(OCoLC)908089124(SSID)ssj0001408220(PQKBManifestationID)11967319(PQKBTitleCode)TC0001408220(PQKBWorkID)11348274(PQKB)11216237(DE-He213)978-3-319-09928-6(MiAaPQ)EBC1968188(PPN)183153626(EXLCZ)99371000000031172920141204d2015 u| 0engur|n|---|||||txtccrThe Cosmic Microwave Background How It Changed Our Understanding of the Universe /by Rhodri Evans1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (212 p.)Astronomers' Universe,1614-659XDescription based upon print version of record.3-319-09927-2 Includes bibliographical references.From Kapteyn to Hubble -- George Gamow, Ralph Alpher and Robert Herman -- Penzias and Wilson and Dicke et al -- COBE -- DASI and BOOMERANG and other ground-based experiments -- WMAP -- Planck -- The Future.Rhodri Evans tells the story of what we know about the universe, from Jacobus Kapteyn’s Island universe at the turn of the 20th Century, and the discovery by Hubble that the nebulae were external to our own galaxy, through Gamow’s early work on the cosmic microwave background (CMB) and its subsequent discovery by Penzias and Wilson, to modern day satellite-lead CMB research. Research results from the ground-based experiments DASI, BOOMERANG, and satellite missions COBE, WMAP and Planck are explained and interpreted to show how our current picture of the universe was arrived at, and the author looks at the future of CMB research and what we still need to learn. This account is enlivened by Dr Rhodri Evans' personal connections to the characters and places in the story.Astronomers' Universe,1614-659XAstronomyCosmologyPopular Science in Astronomyhttps://scigraph.springernature.com/ontologies/product-market-codes/Q11009Cosmologyhttps://scigraph.springernature.com/ontologies/product-market-codes/P22049Astronomy.Cosmology.Popular Science in Astronomy.Cosmology.520523.1Evans Rhodriauthttp://id.loc.gov/vocabulary/relators/aut759000MiAaPQMiAaPQMiAaPQBOOK9910300431703321The Cosmic Microwave Background2510903UNINA03799nam 22006735 450 991029904670332120251116165857.01-4939-0600-310.1007/978-1-4939-0600-0(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 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