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Network Biology, 2018, 8(1): 1-11
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Article

Improved methods for analyzing MRI brain images

Jyotsna Dogra, Navdeep Prashar, Shruti Jain, Meenakshi Sood
Department of Electronic and Communication Engineering, Jaypee Institute of Information Technology, Solan-173234, India

Received 13 October 2017;Accepted 20 December 2017;Published 1 March 2018
IAEES

Abstract
Image segmentation is a part of image processing for region or object extraction from the background area. Owing to the complex background, contrast of the infected portion, low intensity difference values, intricate inner body parts etc.; the problem of region extraction in segmentation is very challenging. Among various image segmentation techniques, thresholding is one of the simplest techniques, in which the region of interest is extracted from the background by comparing the pixel values with the threshold value. The threshold value is obtained from histogram of the image. The technique presented in the paper involves graph cut method in which the initial centroids are automatically selected by exploiting the symmetrical nature of the MRI images. The results obtained by the thresholding technique in this research work shows that any abnormality can be localized easily in horizontal divided MRI brain image rather than in vertical divided MRI image. Graph cut results show better segmentation than thresholding technique which is justified by PSNR and SSIM values.

Keywords segmentation;fuzzy c-mean clustering;k-mean clustering;split and merge;graph-cut.

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