Factual foreground segregation and gradient based bias correction in brain MR images

Abstract

The bias field is an inadmissible image glitch that evolved during the procedure of image acquisition. N3 (Non parametric Non uniform intensity Normalization) is one of the prominent and publicly available bias correction algorithms. N3 algorithm’s efficiency is limited by the imprecise foreground segregation. To handle this dilemma this paper is proposing a technique by comprising kernel construction and gradient based bias correction, which efficiently extracts the foreground from the bias corrupted image and erodes the present bias. The dataset used here are collected from BrainWeb website. The performance indicators Coefficient of Variation for white, grey matter and Coefficient of Joint Variation are used in disclosing the conclusion in quantitative aspect.