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Computer Optics, 2015, Volume 39, Issue 4, Pages 592–599
DOI: https://doi.org/10.18287/0134-2452-2015-39-4-592-599
(Mi co21)
 

This article is cited in 1 scientific paper (total in 1 paper)

IMAGE PROCESSING, PATTERN RECOGNITION

Retroperitoneal space organ segmentation from CT images based on the level set function

R. V. Eruslanova, M. N. Orehovaa, V. N. Dubrovinb

a Volga State University of Technology
b Republican Clinical Hospital of the Mary-El Republic
References:
Abstract: This article presents a method for solving a problem of segmentation of the retroperitoneal space organs from tomographic images. The method relies on the level set function. We also discuss a method of image preprocessing based on a nonlinear anisotropic diffusion filter, which operates by smoothing the image, while maintaining boundaries between the segments. A tomographic-image segmentation algorithm based on the level set function is synthesized.
Keywords: segmentation, computer tomography, retroperitoneal space organs, CT (computed tomography), image processing, anisotropic diffusion, nonlinear filtration, level set, active contour.
Funding agency Grant number
Foundation for Assistance to Small Innovative Enterprises in Science and Technology 4334ГУ1/2014, код 000790
The work was performed as part of the program UMNIK 2014 conducted by the Federal State Organization "Fund for Assistance to Small Innovative Enterprises in Science and Technology" (state contract number 4334GU1 / 2014, code 000790).
Received: 10.07.2015
Revised: 15.08.2015
Document Type: Article
Language: Russian
Citation: R. V. Eruslanov, M. N. Orehova, V. N. Dubrovin, “Retroperitoneal space organ segmentation from CT images based on the level set function”, Computer Optics, 39:4 (2015), 592–599
Citation in format AMSBIB
\Bibitem{EruOreDub15}
\by R.~V.~Eruslanov, M.~N.~Orehova, V.~N.~Dubrovin
\paper Retroperitoneal space organ segmentation from CT images based on the level set function
\jour Computer Optics
\yr 2015
\vol 39
\issue 4
\pages 592--599
\mathnet{http://mi.mathnet.ru/co21}
\crossref{https://doi.org/10.18287/0134-2452-2015-39-4-592-599}
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  • https://www.mathnet.ru/eng/co/v39/i4/p592
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Optics
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