Multidimensional medical data visualization methods based on generalized graphic images

Olga Grigor evna Berestneva, Vitaly Alekseevich Volovodenko, Olga Mikhaylovna Gerget, Konstantin Sharopin, Irina Anatol evna Osadchaya

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The work devoted to the problem of analysis and interpretation of multi-dimensional medical data based on the generalized graphic images. The existing methods and approaches, including those proposed by the authors were considered. The main problem of data visualization is the problem of obtaining a visual image that is uniquely relevant to the data set. The authors propose an approach that allows to perform visualization of the major linear structures: segment, polyline, simplex in multidimensional spaces. Presentation of multivariate observations in a two-dimensional image (the curve) ensures that to the close by the values observation will correspond visually similar image-curves; for very different by the values observations its image-curves will be noticeably different. The results of applying this approach to the problems of practical medicine were proposed: analysis of the pregnant women physiological state dynamics, identifying hidden patterns in the structure of the physiological parameters for patients with various forms of asthma.

Original languageEnglish
Pages (from-to)18-23
Number of pages6
JournalWorld Applied Sciences Journal
Issue number24
Publication statusPublished - 1 Jan 2013


  • Cognitive graphics
  • Generalized graphic images
  • Multi-dimensional medical data
  • Visualization techniques

ASJC Scopus subject areas

  • General

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