Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method

Brain tumor is a mass of abnormal growth of cells in the brain which disturbs the normal functioning of the brain. MRI is a powerful diagnostic tool providing excellent soft tissue contrast and high spatial resolution. However, imperfections arising in the radio frequency field and scanner-related intensity artifacts in MRI produce intensity inhomogeneity. These intensity variations pose major challenges for subsequent image processing and analysis techniques. To mitigate this effect in the intensity correction process, an enhanced homomorphic unsharp masking (EHUM) method is proposed in this chapter. The main idea of the proposed EHUM method is determination of region of interest, intensity correction based on homomorphic filtering, and linear gray scale mapping followed by cutoff frequency selection of low pass filter used in the filtering process. This method first determines the ROI to overcome the halo effect between foreground and background regions. Then the intensity correction is carried out using homomorphic filtering and linear gray scale mapping.

[1]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[2]  L. Axel,et al.  Intensity correction in surface-coil MR imaging. , 1987, AJR. American journal of roentgenology.

[3]  Régis Guillemaud,et al.  Uniformity correction with homomorphic filtering on region of interest , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[4]  Richard A. Robb,et al.  Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction , 1998, IEEE Transactions on Medical Imaging.

[5]  S Clare,et al.  Compensating for B(1) inhomogeneity using active transmit power modulation. , 2001, Magnetic resonance imaging.

[6]  Bostjan Likar,et al.  Retrospective correction of MR intensity inhomogeneity by information minimization , 2000, IEEE Transactions on Medical Imaging.

[7]  Jayaram K. Udupa,et al.  New methods of MR image intensity standardization via generalized scale , 2005, SPIE Medical Imaging.

[8]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.

[9]  Roberto Pirrone,et al.  Bias artifact suppression on MR volumes , 2008, Comput. Methods Programs Biomed..

[10]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[11]  Edoardo Ardizzone,et al.  Illumination Correction on Biomedical Images , 2014, Comput. Informatics.

[12]  J. Gore,et al.  Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. , 2014, Magnetic resonance imaging.

[13]  Marco Ganzetti,et al.  Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters , 2016, Front. Neuroinform..

[14]  Bouchaib Cherradi,et al.  Parallel Implementation of Bias Field Correction Fuzzy C-Means Algorithm for Image Segmentation , 2016 .

[15]  Ashish Verma,et al.  Enhancement and Intensity Inhomogeneity Correction of Diffusion-Weighted MR Images of Neonatal and Infantile Brain Using Dynamic Stochastic Resonance , 2017 .

[16]  Maryjo M. George,et al.  A non-iterative multi-scale approach for intensity inhomogeneity correction in MRI. , 2017, Magnetic resonance imaging.

[17]  P. Asbach,et al.  Patient-adapted respiratory training: Effect on navigator-triggered 3D MRCP in painful pancreatobiliary disorders. , 2018, Magnetic Resonance Imaging.