Seed-based region growing study for brain abnormalities segmentation

Abstract

This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used controlled experimental data as our testing data. The data is designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation was done with twenty data of each category. The knowledge of the size of the abnormalities by the number of pixels were then used as the ground truth to compare with the SBRG segmentation results. The proposed SBRG technique was found to produce potential solutions to the current difficulties in detecting abnormalities in the human brain tissue area.