Call for Paper - October 2019 Edition
IJCA solicits original research papers for the October 2019 Edition. Last date of manuscript submission is September 20, 2019. Read More

Hybrid Visualization of the Medical Images Data Sets

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Safa A. Najim, Widad Abdulsamad Mansour
10.5120/ijca2016908617

Safa A Najim and Widad Abdulsamad Mansour. Article: Hybrid Visualization of the Medical Images Data Sets. International Journal of Computer Applications 136(8):1-5, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Safa A. Najim and Widad Abdulsamad Mansour},
	title = {Article: Hybrid Visualization of the Medical Images Data Sets},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {8},
	pages = {1-5},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

This paper presents new method to visualize medical images data sets by using the properties of continuity and trustworthy dimensional reduction methods. Continuity and trustworthy dimensional reduction methods are well-known promising nonlinear methods are used to visualize different data sets, as medical images. However, their visualizations face the problem of false colors which lead the specialist to make wrong analysis of patient status. To overcomes these errors, we will combine these two methods in one to generate hybrid method has continuity and trustworthiness properties. The proposed method produces best visualization by perfect preserving the corresponding color distances between visualization and original data sets in high-dimensional space. The application of hybrid method shows it is interested for visualizing medical images data sets. It has been compared with the continuity methods (as Isomap) and the trustworthy method (as curvilinear distance analysis (CDA)). The results proves the efficiency of of the proposed method in visualizing medical images data sets, where the false colors in the visualization are overcome as well as possible. The experiments shows the hybrid visualization has more chances to discover the true colors of the medical images data sets.

References

  1. Samuel Gerber, Tolga Tasdizen, P. Thomas Fletcher, Sarang Joshi, Ross Whitaker, and the Alzheimers Disease Neuroimaging Initiative (ADNI). Manifold modeling for brain population analysis. Medical Image Analysis, 14:643–653, 2010.
  2. Patric Hagmann, Lisa Jonasson, Philippe Maeder, Jean philippe Thiran, Van J.Wedeen, and Reto Meuli. Understanding diffusion mr imaging techniques: From scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radio Graphic, 26:205–223, 2006.
  3. Ghassan Hamarneh, Senior Member, Chris McIntosh, and Mark S. Drew. Perception-based visualization of manifoldvalued medical images using distance-preserving dimensionality reduction. IEEE Transactions on Medical Imaging., 30:1314–1327, 2011.
  4. Jihun Hamma, Dong Hye Ye, Ragini Verma, and Christos Davatziko. Gram: A framework for geodesic registration on anatomical manifolds. Medical Image Analysis, 14:633–642, 2010.
  5. Zhanli Hu, Jing Zou, Jianbao Gui, Junyan Rong, Yanming Li, Dongxing Xi, and Hairong Zheng. Real-time visualization and interaction of threedimensional human ct images. Computers, 5:1335–1342, 2010.
  6. Michel Verleysen John Aldo Lee, Amaury Lendasse. Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing, 57:49–76, 2004.
  7. I.T. Jolliffe. Principal Component Analysis. Springer Verlag, New York, 2002.
  8. Vin de Silva Joshua B. Tenenbaum and John C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000.
  9. Aljabar P., Wolz R., Srinivasan L., Counsell S. J., Rutherford M. A., Edwards A. D., Hajnal J. V., and Rueckert D. A combined manifold learning analysis of shape and appearance to characterize neonatal brain development. IEEE Transaction on Medical Imaging, 30:2072–2086, 2011.
  10. Parmeshwar Khurd Ragini Verma and Christos Davatzikos. On analyzing diffusion tensor images by identifying manifold structure using isomaps. IEEE Transactions on Medical Imaging., 26:772–778, 2007.
  11. Richard Souvenir and Robert Pless. Image distance functions for manifold learning. Image and Vision Computing, 25:365– 373, 2007.
  12. M. Steyvers. Multidimensional scaling. Encyclopedia of Cognitive Science, 2002.
  13. Michal Aupetit Sylvain Lespinats. Checkviz: Sanity check and topological clues for linear and non-linear mappings. Comput. Graph. Forum, 30:113–125, 2011.
  14. Silva D.D. Langford J. Tenenbaum, J. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500:23192323, 2000.
  15. Adam ?wito?ski, Marcin Michalak, Henryk Josi?ski, and Konrad Wojciechowski. Detection of tumor tissue based on the multispectral imaging. In Computer Vision and Graphics, Lecture Notes in Computer Science, Part 2, of Springer, 6375:325–333, 2010.
  16. Chris M. Clark Yong Fan, Nematollah Batmanghelich and Christos Davatzikos. Spatial patterns of brain atrophy in mci patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage, 39:1731–1743, 2008.
  17. Xin Zhao and Arie E Kaufman. Multi-dimensional reduction and transfer function design using parallel coordinates. In Volume Graphics, 2010.

Keywords

Visualization, Medical images, Dimensionality reduction, Isomap, CDA