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If you use data from The Cancer Image Archive (TCIA) in your research we ask that you include appropriate references in your publications and presentations. These citations are critical for providing continued justification of funding from the agencies that support TCIA, and are what allow us to provide this data to you free of charge. Guidelines for how to cite TCIA can be found on our Citation Guidelines wiki page.

In addition we would like to list these publications here on our web site. If you have utilized TCIA in your research please contact us at CancerImagingArchive@mail.nih.gov so that we can include your publications in the list below. You can obtain the publications listed below in  xml format.
 


Publications relating to specific data collections:


TCIA General

PUBLICATIONS

  1. Erickson BJ, Fajnwaks P, Langer SG, and Perry J. Multisite Image Data Collection and Management Using the RSNA Image Sharing Network., Translational oncology, 2014. 7(1):36-39. (paper)
  2. Gutman DA, Cobb J, Somanna D, et al. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data., Journal of the American Medical Informatics Association, 2013. 20(6): p. 1091-1098. doi: 10.1136/amiajnl-2012-001469 (paper)
  3. Bui AAT, Hsu W, Arnold C, El-Saden S, Aberle DR, Taira RK. Imaging-based observational databases for clinical problem solving: the role of informatics., J Am Med Inform Assoc, 2013 June;20(6):1053-1058. doi: 10.1136/amiajnl-2012-001340 (paper)
  4. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
  5. Jaffe, C Carl. Imaging and Genomics: Is There a Synergy?Radiology. 2012. 264(2):329-31.(paper).
  6. Mongkolwat P, Channin DS, Kleper V, Rubin DL. Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder.Radiographics .2012. 32(4):1223-32. (paper).
  7. Freymann JB, Kirby JS, Perry JH, Clunie DA, and Jaffe CC. Image data sharing for biomedical research—meeting HIPAA requirements for de-identification.Journal of Digital Imaging 25, no. 1 (2012): 14-24. (paper)
  8. Villani L and Prati RC. Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário. Congresso Brasileiro de Informática em Saúde (CBIS’2012). Citado na. 2012.(paper)
  9. Vendt B, Smith K, Clark K, Commean P, Koppel P, Maffitt D, Moore S, Moulton J, Phillips S, Tarbox L, Wolfsberger M, Prior F; The Cancer Imaging Archive (TCIA). Presented at the 2013 Mallinckrodt Institute of Radiology Poster Session, March 26, 2013, Washington University School of Medicine, Saint Louis MO; (poster).
  10. Moore S, Kirby J, Clark K, Freymann J, Vendt B, Jaffe C, Smith K, Maffitt D, Tarbox L, Prior F;Safe (Compliant) De-identification of Medical Images With Maximum Retention of Scientific Research Value. Presented at the Annual Meeting of the Radiological Society of North America (RSNA), 2012; (poster)
  11. Prior F, Kirby J, Freymann J, Jaffe C, Smith K, Vendt B, Clark K, Tarbox L; Building Open Science Communities For Imaging Research. Presented at the Open Source EHR Summit & Workshop (OSEHRA) October 17-18, 2012. (PPTX)
  12. Tarbox L, Koppel P, Moore S, Pringle M, Kirby J, Prior F; The Cancer Imaging Archive (TCIA): Creating a Large Public Image Collection. Presented at the Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM), 2012; (PPT)
  13. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F; The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Presented at the Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM), 2012; (poster)
  14. Freymann J, Kirby J, Jaffe C; A Scalable Methodology for Correlating Clinical Imaging Features with TCGA Data. Presented at the 2011 TCGA Scientific Symposium. (poster)
  15. Prior F, Freymann J, Vendt B, Clark K, Kirby J, Tarbox L, Koppel P, Moore S, Pringle M, Phillips S, Maffitt D, Jaffe C; A Repository of Advanced Imaging Information Correlated with TCGA Samples. Presented at the 2011 TCGA Scientific Symposium. (poster)

Collection:  CT Colonography

PUBLICATIONS

  1. Namías R, et al., Automatic rectum limit detection by anatomical markers correlation. Computerized Medical Imaging and Graphics, 2014. 38(4):245-250.(link)
  2. Boone DJ, Halligan S, Roth HR, et al., CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration. Radiology, 2013. 268(3):752-760.(link)
  3. Roth HR, et al., External clinical validation of prone and supine CT colonography registration in Abdominal Imaging. Computational and Clinical Applications 2012, Springer. 7601:10-19.(link)
  4. Taylor SA, Robinson C, Boone D, Honeyfield L, Halligan S. Polyp Characteristics Correctly Annotated by Computer-aided Detection Software but Ignored by Reporting Radiologists during CT Colonography. Radiology. 2009 Dec;253(3):715-23. Epub 2009 Sep 29. (link)
  5. Johnson CD, Chen MH, Toledano AY, Heiken JP, et al. Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med. 2008 Sep 18;359(12):1207-17. (link)

 

Collection:  LIDC-IDRI

PUBLICATIONS

  1. Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume. Academic Radiology, 2013. 20(2):173-180. doi: 10.1016/j.acra.2012.08.014. (link)
  2. Aggarwal P, Vig R, and Sardana H Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases. Journal of Computers, 2013. 8(9):2245-2255. (link)
  3. Sivakumar S and Chandrasekar C, Lung nodule detection using fuzzy clustering and support vector machines. International Journal of Engineering and Technology, 2013. 5(1):179-185.(link)
  4. Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
  5. Sivakumar S and Chandrasekar C, Lungs image segmentation through weighted FCM.Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference. 25-27 April 2012 pages 109-113. IEEE. doi:10.1109/RACSS.2012.6212707 (link)
  6. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Medical Physics, 38: 915–931, 2011. (link)
  7. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. Biomedical Engineering, IEEE Transactions. 2011. 58(12):3418-3428. doi: 10.1109/TBME.2011.2167621. (link)
  8. Armato SG III, Roberts RY, Kocherginsky M, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz DF, McLennan G, McNitt-Gray MF, Meyer CR, Reeves AP, Caligiuri P, Quint LE, Sundaram B, Croft BY, Clarke LP: Assessment of radiologist performance in the detection of lung nodules: Dependence on the definition of “truth”. Academic Radiology 16(1): 28–38, 2009. (link)
  9. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJR, Kazerooni EA, Yankelevitz DF, McNitt-Gray MF, McLennan G, Armato SG III, Henschke CI, Aberle DR, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 14(12): 1475–1485, 2007. (link)
  10. McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais R, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.Academic Radiology 14(12): 1464–1474, 2007. (link)
  11. Armato SG III, Roberts RY, McNitt-Gray MF, Meyer CR, Reeves AP, McLennan G, Engelmann RM, Bland PH, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth.” Academic Radiology 14(12): 1455–1463, 2007. (link)
  12. Armato SG III, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology 14(12): 1409–1421, 2007. (link)
  13. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, Mullan BF, Yankelevitz DF, van Beek EJR, Armato SG III, McNitt-Gray MF, Reeves AP, Gur D, Henschke CI, Hoffman EA, Bland PH, Laderach G, Pais R, Qing D, Piker C, Guo J, Starkey A, Max D, Croft BY, Clarke LP: Evaluation of lung MDCT nodule annotation across radiologists and methods.Academic Radiology 13(10): 1254–1265, 2006. (link)
  14. Dodd LE, Wagner RF, Armato SG III, McNitt-Gray MF, Beiden S, Chan H-P, Gur D, McLennan G, Metz CE, Petrick N, Sahiner B, Sayre J, The Lung Image Database Consortium Research Group: Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: Contemporary research topics relevant to the Lung Image Database Consortium. Academic Radiology 11: 462–475, 2004. (link)
  15. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, The Lung Image Database Consortium Research Group: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232: 739–748, 2004. (link)

 

Collection:  NLST

 

Collection:  Phantom FDA

PUBLICATIONS

  1. Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of Overlapping Reconstruction for Improving the Quantitative Assessment of CT Lung Nodule Volume.Acad Radiol. 2013 Feb;20(2):173-80. doi: 10.1016/j.acra.2012.08.014. Epub 2012 Oct 22. (link)
  2. Peskin AP, Dima AA, Saiprasad G. An Automated Method for Locating Phantom modules in Anthropomorphic Thoracic Phantom CT Studies. The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2012.(link)
  3. Gavrielides MA, Kinnard LM, Myers KJ ,Peregoy J, Pritchard WF, Zeng R, Esparza J, Karanian J, Petrick N, A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom, Optics Express , vol. 18, n.14, pp. 15244-15255, 2010. (link)

 

Collection:  Quantitative Imaging Network Collections

PUBLICATIONS

  1. Kalpathy-Cramer J, Freymann JB, Kirby JS, et al. Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive Translational Oncology. 2014 Feb;7(1):147-52. doi: 10.1593/tlo.13862. (link)
  2. Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpahthy-Cramer J. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Translational Oncology. 2014 Feb;7(1):153-66. (link)
  3. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals Translational Oncology. 2014 Feb;7(1):1-4. doi: http://dx.doi.org/10.1593/tlo.13832. (link)
  4. Levy MA, Freymann JB, Kirby JS, Fedorov A, Fennessy FM, Eschrich SA, Berglund AE, Fenstermacher DA, Tan Y, Guo X, Casavant TL, Brown BJ, Braun TA, Dekker A, Roelofs E, Mountz JM, Boada F, Laymon C, Oborski M, Rubin DL. Informatics methods to enable sharing of quantitative imaging research data. Magnetic Resonance Imaging. 2012 Nov;30(9):1249-56. doi: 10.1016/j.mri.2012.04.007. Epub 2012 Jul 6. (link)

 

Collection:  RIDER Collections

PUBLICATIONS

  1. Aerts HJ, Velazquez ER, Leijenaar RTH, Parmar C, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014. 5(4006). doi:10.1038/ncomms5006(link)
  2. Meyer CR, Armato SG III, Fenimore CP, McLennan G, Bidaut LM, Barboriak DP, Gavrielides MA, Jackson EF, McNitt-Gray MF, Kinahan PE, Petrick N, Zhao B. Quantitative imaging to assess tumor response to therapy: Common themes of measurement, truth data and error sources. Translational Oncology 2: 198–210, 2009. (link)
  3. McNitt-Gray MF, Bidaut LM, Armato SG III, Meyer CR, Gavrielides MA, Fenimore CP, McLennan G, Petrick N, Zhao B, Reeves AP, Beichel R, Kim H-J, Kinnard L. CT assessment of response to therapy: Tumor volume change measurement, truth data and error. Translational Oncology2009. 2:216–222. (link)
  4. Kinahan PE, Doot RK, Wanner-Roybal M, Bidaut LM, Armato SG III, Meyer CR, McLennan G.PET/CT assessment of response to therapy: Tumor change measurement, truth data and error. Translational Oncology 2:223–230, 2009. (link)
  5. Jackson EF, Barboriak DP, Bidaut LM, Meyer CR. Magnetic resonance assessment of response to therapy: tumor change measurement, truth data and error sources.Translational Oncology 2009 Dec;2(4):211-5. PubMed PMID: 19956380; PubMed Central PMCID: PMC2781079. (link)
  6. Armato SG 3rd, Meyer CR, Mcnitt-Gray MF, McLennan G, Reeves AP, Croft BY, Clarke LP;RIDER Research Group. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.Clin Pharmacol Ther. 2008 Oct;84(4):448-56. PubMed PMID: 18754000. (link)

 

Collection:  TCGA-BRCA

PUBLICATIONS

  1. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology, 2014. doi: 10.1148/radiol.14132641 (link)

 

Collection:  TCGA-GBM

PUBLICATIONS

  1. Natteshan N, Jothi JAA. Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers. Advances in Intelligent Informatics. Springer. 2015 320:19-30. doi:10.1007/978-3-319-11218-3_3 (link)
  2. Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Rajan J, Jilwan-Nicola M, Chen JY, Raghavan P, Holder CA, Rubin D, Huang E, Kirby J, Freymann J, Jaffee CC, Flanders A, Zinn PO. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.BMC Medical Genomics, 2014. 7(1):30. doi:10.1186/1755-8794-7-30 (link)
  3. Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Chesier SH, Napel S, Zaharchuk G, Plevritis SK. Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology, 2014. doi: 10.1148/radiol.14131731 (link)
  4. Mazurowski MA, Zhang J, Peters KB, and Hobbs H. Computer-extracted MR imaging features are associated with survival in glioblastoma patients. Journal of Neuro-Oncology. 2014 Aug 24 [Epub ahead of print] doi: 10.1007/s11060-014-1580-5 (link)
  5. Jain R, Poisson L, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Jaffe C, Mikkelsen T, Flanders A. Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology. 2014 Aug;272(2):484-93. doi: 10.1148/radiol.14131691. Epub 2014 Mar 19. 2014 (link)
  6. Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, July 2014. doi: 10.1016/j.neurad.2014.02.006
  7. Gutman DA, Cooper LAD, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Monqkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. Radiology. 2013 May:267(2):560-569,doi:10.1148/radiol.13120118 (link)
  8. Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology, 2013 Apr:267(1):212 –220, doi:10.1148/radiol.12120846 (link)
  9. Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro-oncology, 2013. 15(10):1389-1394 (link)
  10. Karnayana PM. Radiogenomic correlation for prognosis in patients with glioblastoma multiformae. Journal of Neuro-Oncology. 2013 June 13 (link to thesis)
  11. Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
  12. Jain R, Poisson L, Narang J, Scarpace L, Rosenblum ML, Rempel S, Mikkelson T. Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study. American Journal of Neuroradiology, 2012. 33(7):1343-1348 [Epub ahead of print] (link)
  13. Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, et al. (2012) A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 2012 7(8): e41522. doi:10.1371/journal.pone.0041522 (link)
  14. Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. 2011Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE, 2011 6(10): e25451. doi:10.1371/journal.pone.0025451 (link)

 

Collection:  Algorithm Development

PUBLICATIONS

  1. Codella N, Connell J, Pankanti S, Merler M, and Smith JR. Automated Medical Image Modality Recognition by Fusion of Visual and Text Information. Medical Image Computing and Computer-Assisted Intervention. 2014, Springer. 487-495. (link)
  2. Ertugrul OF. Adaptive Texture Energy Measure Method. International Journal of Intelligent Information Systems. 2014. 3(2):13-18. doi:10.11648/j.ijiis.20140302.11 (link)
  3. Kawa J, Juszczyk J, Pyciński B, Badura P, Pietka E. Radiological Atlas for Patient Specific Model Generation. Information Technologies in Biomedicine, 2014 4:69-82. 10.1007/978-3-319-06596-0_7. (link)
  4. Kowalik-Urbaniak I, Brunet D, Wang J, Koff D, Smolarski-Koff N, Vrscay ER, Wallace B, Wang Z.The quest for ‘diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images. SPIE Medical Imaging. 2014. Vol. 9073. International Society for Optics and Photonics. doi:10.1117/12.2043196 (link)
  5. Naresh P and Shettar R. Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey. International Journal of Recent Trends in Engineering & Technology, 2014. 11:595-601 (link)
  6. Patel NP, Parmar SK, and Jain KR. Swift Pre Rendering Volumetric Visualization of Magnetic Resonance Cardiac Images based on Isosurface Technique. Procedia Technology, 2014. 14:422-429. DOI: 10.1016/j.protcy.2014.08.054 (link)
  7. Roy S, Brown MS, and Shih GL. Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting. Journal of Digital Imaging, 2014. 27(1):49-57. doi: 10.1007/s10278-013-9624-5 (link)
  8. Sivakumar S, and Chandrasekar C. A Study on Image Denoising for Lung CT Scan Images.International Journal of Emerging Technologies in Computational and Applied Sciences, 2014. 7(1):86-91 (link)
  9. Harmon S, Wendelberger B, and Jeraj R. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis. Medical Physics, 2014. 41(6): p. 178-178. doi:http://dx.doi.org/10.1118/1.4888150 (link)
  10. Krishnakumar V. and Parthiban L. Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and NonSubsampled Contourlet Transforms. Annual Review & Research in Biology, 2014. 4(19):2938-2956. doi:10.9734/ARRB/2014/9131#sthash.qFePVdL1.dpuf (link)
  11. Codella N, Merler M. IBM TJ Watson Research Center. Semantic Model Vector for ImageCLEF2013. June 18, 2014. (link)
  12. Agostinelli F, Anderson MR, and Lee H. Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising. Advances in Neural Information Processing Systems. 2013. (link)
  13. Breseman K, Lee C, Bloch BN, and Jaffe C. Constructing 3D-Printable CAD Models of Prostates from MR Images. Bioengineering Conference (NEBEC),
    39th Annual Northeast , IEEE, 27-28. 5-7 April 2013. doi:10.1109/NEBEC.2013.8
  14. Buckler A, Liu TT, Savig E, Suzek BE, Rubin DL, and Paik D. Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. Journal of Digital Imaging, 2013. 26(4):630-641. doi:10.1007/s10278-013-9599-2 (link)
  15. Heyns M, Breseman K, Lee C, Bloch BN, Jaffe C, and Xiang H. Design of a Patient-Specific Radiotherapy Treatment Target. Bioengineering Conference (NEBEC), 2013 39th Annual Northeast. 2013.171-172. IEEE.doi:10.1109/NEBEC.2013.75
  16. Kumar A, Kim J, Cai W, Fulham M, and Feng D. Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data. Journal of Digital Imaging, 2013. 26(6):1025-1039. doi: 10.1007/s10278-013-9619-2.(link)
  17. Lundström C. vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging. International Journal of Computer Assisted Radiology and Surgery, 2013. 8(3):437-450. doi: 10.1007/s11548-012-0792-4 (link)
  18. Olmedo I, Guerra Perez Y, Johnson JF, Raut L, Hoe DHK. Image segmentation on GPGPUs: a cellular automata-based approach. Proceedings of the 2013 Summer Computer Simulation Conference. Society for Modeling & Simulation International. 2013. 51. (link)
  19. Pambrun JF, Noumeir R. Compressibility variations of JPEG2000 compressed computed tomography. Conference Proceedings, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013:3375-3378. doi: 10.1109/EMBC.2013.6610265 (link)
  20. Roozgard A, Barzigar N, Verma P, and Cheng S. 3D medical image denoising using 3D block matching and low-rank matrix completion. Signals, Systems and Computers, Asilomar Conference, 3-6 Nov. 2013, 253 – 257 IEEE. doi:10.1109/ACSSC.2013.6810271
  21. Yankeelov TE, Atuegwu N, Hormuth D, et al. Clinically Relevant Modeling of Tumor Growth and Treatment Response. Sci Transl Med. 2013 May 29;5(187):187ps9 doi: 10.1126/scitranslmed.3005686 (link)
  22. Cao L, Chang Y-C, Codella N, Merler M, Nguyen Q-B, Smith JR. IBM TJ Watson Research Center.Multimedia Analytics: Modality Classification and Case-Based Retrieval Tasks of ImageCLEF2012. CLEF (Online Working Notes/Labs/Workshop).2012.(link)
  23. Huang LC, Yseng LY, Hwang MS. A reversible data hiding method by histogram shifting in high quality medical images. Journal of Systems and Software 2013 March;86(3):716-27 doi: 10.1016/j.jss.2012.11.024 (link)
  24. Pheng HS and Shamsuddin SM. Texture classification of lung computed tomography images. 2012 International Conference on Graphic and Image Processing. 2013. Vol. 8768. International Society for Optics and Photonics. doi:10.1117/12.2011108 (link)
  25. Barzigar N, Roozgard A, Verma P, Cheng S. Removing Mixture Noise from Medical Images Using Block Matching Filtering and Low-Rank Matrix Completion. Healthcare Informatics, Imaging and Systems Biology, IEEE International Conference. 2012.134. doi:10.1109/HISB.2012.59 (link)
  26. Otake Y, Schafer S, Stayman JW, Zbijewski W, Kleinszig G, Graumann R, Khanna AJ, Siewerdsen JH. Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration. SPIE Medical Imaging, 2012. Volume: 8316. International Society for Optics and Photonics. doi:10.1117/12.911308 (link)
  27. Roozgard A, Cheng AS, Liu H. Malignant nodule detection on lung ct scan images with kernel rx-algorithm. Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on 5-7 Jan. 2012 499 – 502. IEEE. doi: 10.1109/BHI.2012.6211627.
  28. Biancardi AM, Jirapatnakul AC, Reeves AP. A comparison of ground truth estimation methods. International Journal of Computer Assisted Radiology and Surgery, 2010. 5(3):295-305. DOI: 10.1007/s11548-009-0401-3 (link)
  29. Soysal OM, Chen P, Schneider H. An Image Processing Tool for Efficient Feature Extraction in Computer-Aided Detection Systems. Granular Computing (GrC) IEEE International Conference 2010. 14-16 Aug. 438-442. doi:10.1109/GrC.2010.128
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