Segmentation algorithm based on square blocks propagation

V. V. Danilov, I. P. Skirnevskiy, R. A. Manakov, D. Yu Kolpashchikov, O. M. Gerget

Research output: Contribution to journalConference articlepeer-review


This research is devoted to the segmentation of heart and brain anatomical structures. In the study, we present a segmentation algorithm based on the square blocks (superpixels) propagation. The square blocks propagation algorithm checks two criteria. For the first criteria, the current intensity of the pixel is compared to the average intensity of the segmented region. For the second criterion, the intensity difference of the pixels lying on the superpixel sides is compared to the threshold. Once these criteria are successfully checked, the algorithm merges homogeneous superpixels into one region. Then the following superpixels are attached to the final superpixel set. The last step of the proposed method is the spline generation. The spline delineates the borders of the region of interest. The main parameter of the algorithm is the size of a square block. The cardiac MRI dataset of the University of York and the brain tumor dataset of Southern Medical University were used to estimate the segmentation accuracy and processing time. The highest Dice similarity coefficients obtained by the presented algorithm for the left ventricle and the brain tumor are 0.93±0.03 and 0.89±0.07 respectively. One of the most important features of the border detection step is its scalability. It allows implementing different one-dimensional methods for border detection.

Original languageEnglish
Pages (from-to)148-154
Number of pages7
JournalCEUR Workshop Proceedings
Publication statusPublished - 2019
Event29th International Conference on Computer Graphics and Vision, GraphiCon 2019 - Bryansk, Russian Federation
Duration: 23 Sep 201926 Sep 2019


  • Brain tumor segmentation
  • Left ventricle segmentation
  • Region growing
  • Square blocks propagation
  • Superpixels

ASJC Scopus subject areas

  • Computer Science(all)

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