TY - JOUR
T1 - Segmentation algorithm based on square blocks propagation
AU - Danilov, V. V.
AU - Skirnevskiy, I. P.
AU - Manakov, R. A.
AU - Kolpashchikov, D. Yu
AU - Gerget, O. M.
N1 - Funding Information:
This work was supported in part by the Russian Federation Governmental Program “Nauka” № 12.8205.2017/БЧ (additional number: 4.1769.ГЗБ.2017).
Publisher Copyright:
Copyright © 2019 for this paper by its authors.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Brain tumor segmentation
KW - Left ventricle segmentation
KW - Region growing
KW - Square blocks propagation
KW - Superpixels
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U2 - 10.30987/graphicon-2019-2-148-154
DO - 10.30987/graphicon-2019-2-148-154
M3 - Conference article
AN - SCOPUS:85074715415
VL - 2485
SP - 148
EP - 154
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 29th International Conference on Computer Graphics and Vision, GraphiCon 2019
Y2 - 23 September 2019 through 26 September 2019
ER -