Artificial Intelligence in Medicine
Volume 44, Issue 3 , Pages 183-199 , November 2008

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

  • Umberto Castellani

      Affiliations

    • Dipartimento di Informatica, Università degli Studi di Verona, Ca’ Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy
    • Corresponding Author InformationCorresponding author. Tel.: +39 045 8027988; Fax: +39 045 8027068.
    • ,
  • Marco Cristani

      Affiliations

    • Dipartimento di Informatica, Università degli Studi di Verona, Ca’ Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy
    • ,
  • Carlo Combi

      Affiliations

    • Dipartimento di Informatica, Università degli Studi di Verona, Ca’ Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy
    • ,
  • Vittorio Murino

      Affiliations

    • Dipartimento di Informatica, Università degli Studi di Verona, Ca’ Vignal 2, Strada Le Grazie 15, 37134 Verona, Italy
    • ,
  • Andrea Sbarbati

      Affiliations

    • Dipartimento di Scienze Morfologiche Biomediche, Università degli Studi di Verona, P.le Scuro, 10 Policlinico B.go Roma, 37134 Verona, Italy
    • ,
  • Pasquina Marzola

      Affiliations

    • Dipartimento di Scienze Morfologiche Biomediche, Università degli Studi di Verona, P.le Scuro, 10 Policlinico B.go Roma, 37134 Verona, Italy

Received 24 November 2006 ,Revised 27 June 2008 ,Accepted 27 June 2008.

References 

  1. Ferreira de Oliveira MC, Levkowitz H. From visual data exploration to visual data mining: a survey. IEEE Trans Vis Comput Graph. 2003;9(3):207–216
  2. Spence R. Information visualization. New York, USA: ACM Press/Addison-Wesley; 2001;
  3. Card SK, Mackinglay JD, Shneiderman B. Readings in information visualization—using vision to think. San Francisco, USA: Kaufmann; 1999;
  4. Tufte ER. Envisioning information. Cheshire, USA: Graphics Press; 1990;
  5. Stytz MR, Frieder G, Frieder O. Three-dimensional medical imaging: algorithms and computer systems. ACM Comput Surv. 1991;23(4):421–499
  6. Chittaro L. Information visualization and its application to medicine. Artif Intell Med. 2001;22(2):81–88
  7. Han J, Kamber M. Data mining: concepts and techniques. San Francisco, USA: Morgan Kaufmann; 2001;
  8. Lavrac N. Selected techniques for data mining in medicine. Artif Intell Med. 1999;16(1):3–23
  9. Keim DA. Information visualization and visual data mining. IEEE Trans Vis Comput Graph. 2002;7(1):100–107
  10. Chittaro L, Combi C, Trapasso G. Clinical data mining: a visual approach and its application to hemodialysis. J Vis Lang Comput. 2003;14(6):591–620
  11. Van der Heyden JE, Inkpen KM, Atkins MS, Sheelagh M, Carpendale T. Exploring presentation methods for tomographic medical image viewing. Artif Intell Med. 2001;22(2):89–109
  12. Lundstrom C, Ljung P, Persson A, Ynnerman A. Uncertainty visualization in medical volume rendering using probabilistic animation. IEEE Trans Vis Comput Graph. 2007;3(6):1645–1648
  13. Sorensen TS, Therkildsen SV, Makowski P, Knudsen JL, Pedersen EM. A new virtual reality approach for planning of cardiac interventions. Artif Intell Med. 2001;22(3):193–214
  14. Sorensen TS, Pedersen EM, Hansen OK, Sorensen K. Visualization of morphological details in congenitally malformed hearts: virtual, three-dimensional reconstruction from magnetic resonance imaging. Cardiol Young. 2003;13(5):451–460
  15. Englmeier KH, Hellwig G, Griebel J, Delorme S, Sieber M, Brix G. Dynamic MR-mammography in virtual reality. Stud Health Technol Inform. 2003;94:72–78
  16. Windishberger C, Barth M, Lamm C, Shroeder L, Bauer H, Gur R, et al. Fuzzy cluster analysis of high-field functional MRI data. Artif Intell Med. 2003;29(3):203–223
  17. Dimitriadou E, Barth M, Windshberger C, Hornik K, Moser E. A quantitative comparison of functional MRI. Artif Intell Med. 2004;31(1):57–71
  18. Zhang DQ, Chen SC. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif Intell Med. 2004;32(1):37–50
  19. Marzola P, Farace P, Calderan L, Crescimanno C, Lunati E, Nicolato E, et al. In vivo mapping of fractional plasma volume (fpv) and endothelial transfer coefficient (kps) in solid tumors using a macromolecular contrast agent: correlation with histology and ultrastructure. Int J Cancer. 2003;104(April (4)):462–468
  20. Marzola P, Degrassi A, Calderan L, Farace P, Crescimanno C, Nicolato E, et al. In vivo assessment of antiangiogenic activity of SU6668 in an experimental colon carcinoma model. Clin Cancer Res. 2004;2(10):739–750
  21. Comaniciu D, Meer P. Mean Shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell. 2002;24(5):603–619
  22. Norman DA. Thing that make us smart. New York, USA: Addison-Wesley; 1993;
  23. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999;31(3):264–323
  24. Pelleg D, Moore A. X-means: extending K-means with efficient estimation of the number of clusters. In:  Langley P editors. Proceedings of the 17th international conference on machine learning. San Francisco, USA: Morgan Kaufmann; 2000;p. 727–734
  25. Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans Pattern Anal Mach Intell. 2003;25(2):281–288
  26. Twellmann T, Lichte O, Nattkemper TW. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance. IEEE Trans Med Imaging. 2005;24(10):1256–1266
  27. Nattkemper TW, Arnrich B, Lichte O, Timm W, Degenhard A, Pointon L, et al. Evaluation of radiological features for breast tumor classification in clinical screening with machine learning methods. Artif Intell Med. 2005;34(2):129–139
  28. Varini C, Degenhard A, Nattkemper TW. Visual exploratory analysis of DCE-MRI data in breast cancer by dimensional data reduction: a comparative study. Biomed Signal Process Control. 2006;1(1):56–63
  29. Ogan MD. Albumin labeled with Gd-DTPA. An intravascular contrast-enhancing agent for magnetic resonance blood pool imaging: preparation and characterization. Invest Radiol. 1988;12(23):961
  30. Daldrup H, Shames DM, Wendland M, Okuhata Y, Link TM, Rosenau W, et al. Correlation of dynamic contrast-enhanced magnetic resonance imaging with histologic tumor grade: comparison of macromolecular and small-molecular contrast media. Pediatr Radiol. 1998;(28):67–78
  31. Fukunaga K. Statistical pattern recognition. New York, USA: Academic Press; 1990;
  32. Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. In:  Little J,  Lowe D editor. Proceedings of the IEEE international conference on computer vision. Los Alamitos, USA: IEEE CS Press; 2001;p. 438–445
  33. Sheather S, Jones M. A reliable data-based bandwidth selection for kernel density estimation. J R Stat Soc. 1991;53(3):683–690
  34. Wand MP, Jones MC. Kernel smoothing. London, UK: Chapman and Hall; 1995;

PII: S0933-3657(08)00086-9

doi: 10.1016/j.artmed.2008.06.006

Artificial Intelligence in Medicine
Volume 44, Issue 3 , Pages 183-199 , November 2008

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