Open AccessBrain Image Segmentation with Gradient Information
Author(s) -
Sri Purwani,
Julita Nahar,
Carole J. Twining
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.38.27882
Subject(s) - histogram , artificial intelligence , pattern recognition (psychology) , segmentation , computer science , mixture model , ground truth , image segmentation , representation (politics) , image (mathematics) , computer vision , politics , political science , law
Segmentation is the process of extracting structures within the images. The purpose is to simplify the representation of the image into something meaningful and easier to analyse. A magnetic resonance (MR) brain image can be represented as three main tissues, e.g. cerebrospinal fluid (CSF), grey matter and white matter. Although various segmentation methods have been developed, such images are generally segmented by modelling the intensity histogram by using a Gaussian Mixture Model (GMM). However, the standard use of 1D histogram sometimes fails to find the mean for Gaussians. We hence solved this by including gradient information in the 2D intensity and intensity gradient histogram. We applied our methods on real data of 2D MR brain images. We then compared the methods with the previous published method of Petrovic et al. on their dataset, as well as on our larger datasets extracted from the same database of 3D MR brain mages, where the ground-truth annotations are available. This shows that our method performs better than the previous method.