Progressive data transmission for anatomical landmark detection in a cloud

Methods of Information in Medicine

Volumn 51, Issue 3, 2012, Pages 268-278

  Sofka, M ^a   Ralovich, K ^b   Zhang, J ^a   Zhou, S K ^a   Comaniciu, D ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Cloud computing; Computer assisted image processing; Image compression; Machine learning; Pattern recognition system

Indexed keywords

ACCESS TO INFORMATION; ALGORITHM; ANATOMY; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; INSTRUMENTATION; MEDICAL INFORMATICS; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY;

ACCESS TO INFORMATION; ALGORITHMS; ANATOMY; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MEDICAL INFORMATICS; PATHOLOGY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84861311956     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME11-02-0017     Document Type: Article

References (29)

1. Agarwal A, Henehan N, Somashekarappa V, Pandya AS, Kalva H, Furht B. A Cloud Computing Based Patient Centric Medical Information System. In: Furht B, Escalante A, editors. Hand -book of Cloud Computing. Springer US; 2010. pp 553-573.
2. Haux R. Health information systems - past, present, future. Int J Med Inform 2006; 75 (3-4): 268-281.
3. Blobel BGME, Engel K, Pharow P. Semantic Interoperability - HL7 Version 3 Compared to Advanced Architecture Standards. Methods Inf Med 2006; 45 (4): 343-353.
4. Faggioni L, Neri E, Castellana C, Caramella D, Bar-tolozzi C. The future of PACS in healthcare enterprises. European Journal of Radiology 2011; 78 (2): 253-258.
5. Ohmann C, Kuchinke W. Future Developments of Medical Informatics from the Viewpoint of Networked Clinical Research. Methods Inf Med 2009; 48 (1): 45-54.
6. Estrella F, del Frate C, Odeh THRMM, Rogulin D, Amendolia SR, Schottlander D, et al. Resolving Clinicians Queries Across a Grids Infrastructure. Methods Inf Med 2005; 44 (2): 149-153.
7. Handels H, Ehrhardt J. Medical Image Computing for Computer-supported Diagnostics and Therapy. Methods Inf Med 2009; 48 (1): 11-17.
8. Weitzel M, Smith A, de Deugd S, Yates R. A We b 2.0 Model for Patient-Centered Health Informatics Applications. Computer 2010; 43 (7): 43-50.
9. Rosenthal A, Mork P, Li MH, Stanford J, Koester D, Reynolds P. Cloud computing: A new business paradigm for biomedical information sharing. J Biomed Inform 2009; 43: 342-353.
10. Glatard T, Pennec X, Montagnat J. Performance evaluation of grid-enabled registration algorithms using bronze-standards. In: Medical Image Computing and Computer-Assisted Intervention (MIC-CAI). Lecture Notes in Computer Science; 2006. pp 152-160.
11. Kanade T, Yin Z, Bise R, Huh S, Eom SE, Sandbothe M, et al. Cell Image Analysis: Algorithms, System and Applications. In: IEEE Workshop on Applications of Computer Vision (WACV); 2011.
12. Doukas C, Pliakas T, Maglogiannis I. Mobile Healthcare Information Management utilizing Cloud Computing and Android OS. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. Buenos Aires, Argentina; 2010. pp 1037-1040.
13. Tu Z. Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. CVPR 2005; 2; 1589-1596.
14. Viola P, Jones MJ. Rapid object detection using a boosted cascade of simple features. CVPR 2001; 1: 51-518.
15. Schiele PSMFSRB. Discriminative structure learning of hierarchical representations for object detection. CVPR 2009; 2238-2245.
16. Samaras WZGZD. Real-time Accurate Object Detection using Multiple Resolutions. ICCV 2007.
17. Zhu L, Yuille AL. A Hierarchical Compositional System for Rapid Object Detection. NIPS; 2005. pp 1633-1640.
18. Sudderth EB, Torralba A, Freeman WT, Willsky AS. Describing Visual Scenes Using Transformed Objects and Parts. IJCV 2008; 77 (1-3): 291-330.
19. Salembier VVFMP. Binary Partition Trees for Object Detection. TIP 2008; 17 (11): 2201-2216.
20. Butko NJ, Movellan JR. Optimal scanning for faster object detection. CVPR 2009. pp 2751-2758.
21. Sofka M, Zhang J, Zhou SK, Comaniciu D. Mul -tiple Object Detection by Sequential Monte Carlo and Hierarchical Detection Network. In: Pro -ceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA; 2010.
22. Schelkens P, Skodras A, Ebrahimi T. JPEG 2000 Suite. John Wiley and Sons; 2009.
23. Ringl H, Schernthaner R, Sala E, El-Rabadi K, Weber M, Schima W, et al. Lossy 3D JPEG2000 Compression of Abdominal CT Images in Patients with Acute Abdominal Complaints: Effect of Compression Ratio on Diagnostic Confidence and Accuracy. Radiology 2008; 248: 476-484.
24. Dufaux F, Ebr ahimi T. Scrambling for Video Sur veil-lance with Privacy. In: CVPR Workshop; 2006.
25. Fleck S, Busch F, Biber P, Strasser W. 3D Surveillance A Distributed Network of Smart Cameras for RealTime. In: CVPR Workshop; 2006.
26. Schwing A, Zheng Y, Harder M, Comaniciu D. Method and System for Anatomic Landmark Detection Using Constrained Marginal Space Learning and Geometric Inference; 2009. US patent filed. Application number: 12/604,495, Publication number: US 2010/0119137A1.
27. Foran DJ, Meer PP, Papathomas T, Marsic I. Compression guidelines for diagnostic telepathology. Information Technology in Biomedicine, IEEE Transactions on. 1997; 1 (1): 55-60.
28. Thielst CB. At the crossroads: NRTRC white paper examines trends driving the convergence of tele-health, EHRs and HIE. World Hosp Health Serv 2010; 46 (4): 17-23.
29. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, et al. A view of cloud computing. Commun ACM 2010; 53: 50-58.