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.
Copyright © 2021 A. Farzana, M. Mohamed Sathik, S. Shajun Nisha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article
A. Farzana, M. M. Sathik, S. S. Nisha, Factual foreground segregation and gradient based bias correction in brain MR images, J. Math. Comput. Sci. 11 (2021), 3037–3051. https://doi.org/10.28919/jmcs/5613
BibTeX
@article{Farzana2021,
author = {Farzana, A. and Sathik, M. Mohamed and Nisha, S. Shajun},
title = {Factual foreground segregation and gradient based bias correction in brain MR images},
journal = {J. Math. Comput. Sci.},
volume = {11},
year = {2021},
pages = {3037--3051},
doi = {10.28919/jmcs/5613}
}