Journal of Visualization

, Volume 14, Issue 4, pp 353–359 | Cite as

Visualisation in biomedicine as a means of data evaluation

  • Herbert F. Jelinek
  • David J. Cornforth
  • Karen Blackmore
Regular Paper


Visualisation of complex phenomena can aid in understanding the interactions of multiple feature parameters that underlie such phenomena. One such example is the study of dementia, where fractal image measures obtained from post-mortem cortex images have been found useful in studying the relationship between micro-vascular structure and disease. In this research, we analyse the correlation differences in these measures between cases classified as control (non-diseased) and those classified as having either Alzheimer’s disease, small vessel disease or both (diseased). Correlations between feature parameters within these groups indicate that a relationship exists between vessel structure and the parietal and occipital brain regions not identified previously. A simple visualisation method allows these differences to be readily identified. These differences may lead to new insights about the difference in disease progression in different brain areas, and could assist in identifying useful parameters for automated classification.

Graphical abstract


Dementia Visualisation Multifractal Lacunarity Correlation 



The authors would like to thank Patricia Waley, The University of Sydney, Australia for providing the microscope images and Cherryl Kolbe for technical assistance.


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Copyright information

© The Visualization Society of Japan 2011

Authors and Affiliations

  • Herbert F. Jelinek
    • 1
  • David J. Cornforth
    • 2
  • Karen Blackmore
    • 1
  1. 1.Centre for Research in Complex SystemsCharles Sturt UniversityAlburyAustralia
  2. 2.University of New South Wales, Australian Defence Force AcademyKensingtonAustralia

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