Title: Modified region growing segmentation optimised with genetic algorithm for MRI brain images

Authors: K.S. Angel Viji; J. Jayakumari

Addresses: Department of Computer Science and Engineering, Noorul Islam Center for Higher Education, Kumaracoil, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Noorul Islam Center for Higher Education, Kumaracoil, Tamil Nadu, India

Abstract: Image segmentation is an important and challenging factor in the medical image processing. This paper describes a new segmentation method that modifies the region growing method. It makes use of texture constraint in addition to intensity constraint to grow the region for the purpose of segmentation. Also it was optimised using genetic algorithm (GA), i.e., it uses GA for choosing intensity and texture constraint. Texture image is obtained from LBP image. At first the film artefact and noise are removed and the image is enhanced using Gaussian filtering and normalisation. Secondly, features are extracted from the image and it is classified using k-NN classifier. In the third phase optimised region growing (ORGW) segmentation was done, if the image is abnormal. Finally the image is compared with the ground truth image and the segmentation accuracy was calculated. Work was carried out for many images. The segmentation accuracy is 75.31% for RGW method and it is 93.57% for ORGW. This automatic detection of brain tumour through MRI can provide the valuable outlook and accuracy of earlier brain tumour detection.

Keywords: optimised region growing segmentation; k-NN classifier; magnetic resonance imaging; MRI scanning; magnetic resonance imaging; brain scans; genetic algorithms; brain images; image segmentation; medical images; intensity constraints; texture constraints; feature extraction; brain tumours; brain tumour detection.

DOI: 10.1504/IJENM.2016.075173

International Journal of Enterprise Network Management, 2016 Vol.7 No.1, pp.55 - 69

Received: 11 Oct 2014
Accepted: 08 Jan 2015
Published online: 06 Mar 2016 *

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