A new method for MR grayscale inhomogeneity correction

Authors:
M. A. Balafar;A. R. Ramli;S. Mashohor
Affiliations:
Department of Computer and Communication Systems, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia 43400;Department of Computer and Communication Systems, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia 43400;Department of Computer and Communication Systems, Faculty of Engineering, University Putra Malaysia, Serdang, Malaysia 43400
Venue:
Artificial Intelligence Review
Year:
2010

Citing 7
Cited 3

Exploiting Voxel Correlation for Automated MRI Bias Field Correction by Conditional Entropy Minimization

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Entropy Minimization for Automatic Correction of Intensity Nonuniformity

MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Nearest prototype classification of noisy data

Artificial Intelligence Review
A review on the combination of binary classifiers in multiclass problems

Artificial Intelligence Review
Reusable components for partitioning clustering algorithms

Artificial Intelligence Review
D-Separation and computation of probability distributions in Bayesian networks

Artificial Intelligence Review
Review of brain MRI image segmentation methods

Artificial Intelligence Review

New spatial based MRI image de-noising algorithm

Artificial Intelligence Review
Gaussian mixture model based segmentation methods for brain MRI images

Artificial Intelligence Review
Fuzzy C-mean based brain MRI segmentation algorithms

Artificial Intelligence Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

Intensity inhomogeneity is a smooth intensity change inside originally homogeneous regions. Filter-based inhomogeneity correction methods have been commonly used in literatures. However, there are few literatures which compare effectiveness of these methods for inhomogeneity correction. In this paper, a new filter-based inhomogeneity correction method is proposed and the effectiveness of the proposed method and other filter-based inhomogeneity correction methods are compared. The methods with different kernel sizes are applied on MRI brain images and the quality of inhomogeneity correction of different methods are compared quantitatively. Experimental results show the proposed method in a kernel size of 20 * 20 performs almost better than or equal the performance of other methods in all kernel sizes.