A novel automated absolute intensity difference based technique for optimal MR brain image thresholding

Abstract

The traditional Otsu technique for thresholding based on 2D histogram does not give accurate results. A bi-level thresholding case partitions the 2D histogram into four quadrants. This converts the global probability distribution to a probability distribution of two regions only resulting in partial loss of information. The brain regions (white matter (WM), gray matter (GM), cerebrospinal fluid (CSF)) have closely defined gray values, which require the intensity difference information for thresholding. So we propose a novel automated absolute intensity difference based (AIDB) technique for optimal MR brain image thresholding using adaptive coral reef optimization (ACRO). The basic idea is to extract the intensity difference information of the brain image from the 2D histogram matrix. A first-hand objective function, depending on the classical between class variance concepts, is investigated, which is maximized by ACRO. The key achievements of our technique are (i) improved thresholding results, (ii) obtaining more homogenous regions of the image and (iii) obtaining more precise shape of the edges. The proposed technique is tested with one hundred slices of the axial T2 – weighted MR brain images of Harvard medical dataset. The Otsu method for multilevel thresholding using the 2D histogram is considered for a comparison. Several performance measures are considered for the evaluation. It is observed that our technique outperforms the other standard methods.