Abstract:
The article gives a detailed description of the techniques that developed by the authors for primary and deep processing of magnetic resonance imaging, directed on detecting areas ischemic defeat in the brain of rats. The composition tools include techniques to bring MRI images of different samples to the normalized form (size, shape, brightness). Another set of tools associated with the detection of anomalies by T2 and MDC images with using of artificial neural networks and specific metrics. To improve the accuracy of recognition tools are configured into sections based on atlases and anatomical features of the rat brain. Tools providing cognitive illumination of the interest zones, in particular ischemic lesion areas. It is assumed that created algorithms and programs will be part of the developing research software system that oriented to support of medical researchers decision-making.
Citation:
V. P. Fralenko, M. V. Khachumov, M. V. Shustova, “The tools for automatically finding and visualization of interest areas in the MRI data to support of medical researchers decision-making”, Artificial Intelligence and Decision Making, 2016, no. 4, 27–37; Scientific and Technical Information Processing, 44:6 (2017), 397–405
\Bibitem{FraKhaShu16}
\by V.~P.~Fralenko, M.~V.~Khachumov, M.~V.~Shustova
\paper The tools for automatically finding and visualization of interest areas in the MRI data to support of medical researchers decision-making
\jour Artificial Intelligence and Decision Making
\yr 2016
\issue 4
\pages 27--37
\mathnet{http://mi.mathnet.ru/iipr301}
\elib{https://elibrary.ru/item.asp?id=27723584}
\transl
\jour Scientific and Technical Information Processing
\yr 2017
\vol 44
\issue 6
\pages 397--405
\crossref{https://doi.org/10.3103/S0147688217060053}
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https://www.mathnet.ru/eng/iipr301
https://www.mathnet.ru/eng/iipr/y2016/i4/p27
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