New Region Growing based on Thresholding Technique Applied to MRI Data

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A. Afifi 1,* S. Ghoniemy 1 E.A. Zanaty 1 S. F. El-Zoghdy 1

1. College of Computers and Information Technology/Computer Engineering Department, Taif, 888, KSA

* Corresponding author.


Received: 3 Nov. 2014 / Revised: 1 Jan. 2015 / Accepted: 16 Feb. 2015 / Published: 8 Jun. 2015

Index Terms

Image segmentation, Hybrid techniques, Region growing, Magnetic Resonance Imaging


This paper proposes an optimal region growing threshold for the segmentation of magnetic resonance images (MRIs). The proposed algorithm combines local search procedure with thresholding region growing to achieve better generic seeds and optimal thresholds for region growing method. A procedure is used to detect the best possible seeds from a set of data distributed all over the image as a high accumulator of the histogram. The output seeds are fed to the local search algorithm to extract the best seeds around initial seeds. Optimal thresholds are used to overcome the limitations of region growing algorithm and to select the pixels sequentially in a random walk starting at the seed point. The proposed algorithm works automatically without any predefined parameters. The proposed algorithm is applied to the challenging application “gray matter/white matter” segmentation datasets. The experimental results compared with other segmentation techniques show that the proposed algorithm produces more accurate and stable results.

Cite This Paper

A. Afifi, S. Ghoniemy, E.A. Zanaty, S. F. El-Zoghdy, "New Region Growing based on Thresholding Technique Applied to MRI Data", International Journal of Computer Network and Information Security(IJCNIS), vol.7, no.7, pp. 61-67, 2015. DOI:10.5815/ijcnis.2015.07.08


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