LEADER 03826nam 22006735 450 001 9910299046703321 005 20200704232946.0 010 $a1-4939-0600-3 024 7 $a10.1007/978-1-4939-0600-0 035 $a(CKB)2560000000148539 035 $a(EBL)1730896 035 $a(OCoLC)902412176 035 $a(SSID)ssj0001199749 035 $a(PQKBManifestationID)11763871 035 $a(PQKBTitleCode)TC0001199749 035 $a(PQKBWorkID)11204787 035 $a(PQKB)10276430 035 $a(MiAaPQ)EBC1730896 035 $a(DE-He213)978-1-4939-0600-0 035 $a(PPN)178317551 035 $a(EXLCZ)992560000000148539 100 $a20140416d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMarginal Space Learning for Medical Image Analysis$b[electronic resource] $eEfficient Detection and Segmentation of Anatomical Structures /$fby Yefeng Zheng, Dorin Comaniciu 205 $a1st ed. 2014. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2014. 215 $a1 online resource (284 p.) 300 $aDescription based upon print version of record. 311 $a1-322-03779-5 311 $a1-4939-0599-6 320 $aIncludes bibliographical references and index at the end of each chapters. 327 $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. 330 $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. 606 $aOptical data processing 606 $aRadiology 606 $aArtificial intelligence 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aImaging / Radiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H29005 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aOptical data processing. 615 0$aRadiology. 615 0$aArtificial intelligence. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aImaging / Radiology. 615 24$aArtificial Intelligence. 676 $a004 676 $a006.3 676 $a006.6 676 $a616.0754 700 $aZheng$b Yefeng$4aut$4http://id.loc.gov/vocabulary/relators/aut$0941827 702 $aComaniciu$b Dorin$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299046703321 996 $aMarginal Space Learning for Medical Image Analysis$92124959 997 $aUNINA