A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

Due to their simplicity and flexibility, the unsupervised statistical models such as Gaussian mixture model (GMM) are powerful tools to address the brain magnetic resonance (MR) images segmentation problems. However, the GMM is based only on the intensity information, which makes it sensitive to noise. Lots of GMM-based segmentation algorithms have the low segmentation accuracy because of the influence caused by noise and intensity inhomogeneity. To further improve the segmentation accuracy, a robust spatial information-theoretic GMM algorithm is proposed in this paper to simultaneously estimate the intensity inhomogeneity and segment brain MR images. First, a novel spatial factor containing the non-local spatial information is incorporated to reduce the impact of noise. The proposed spatial factor not only takes into account the local neighboring information, but also considers the spatial structure information of pixels. Thus, our algorithm can retain more image details while reducing the influence of noise. Second, the mutual information (MI) maximization method is used to identify and eliminate outliers. Finally, to overcome the impact of intensity inhomogeneity, we use a linear combination of a set of orthogonal polynomials to approximate the bias field. The objective function is integrated with the bias field estimation model to segment the images and estimate the bias field simultaneously. The experimental results on both synthetic and clinical brain MR images show that the proposed algorithm can overcome the influence of noise and intensity inhomogeneity, and achieve more accurate segmentation results.

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

[2]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[3]  Aly A. Farag,et al.  Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Yiteng Pan,et al.  A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation , 2019, Multimedia Tools and Applications.

[5]  Jianping Gou,et al.  A unified Bayesian mixture model framework via spatial information for grayscale image segmentation , 2016, J. Vis. Commun. Image Represent..

[6]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[7]  Yuhui Zheng,et al.  Robust generative asymmetric GMM for brain MR image segmentation , 2017, Comput. Methods Programs Biomed..

[8]  Qiang Chen,et al.  Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation , 2014, Pattern Recognit..

[9]  Theo Gevers,et al.  A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation , 2007, IEEE Transactions on Neural Networks.

[10]  Lalit Gupta,et al.  A gaussian-mixture-based image segmentation algorithm , 1998, Pattern Recognit..

[11]  Yuhao Zhang,et al.  A modified MRF segmentation of brain MR images , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[12]  Arnau Oliver,et al.  A review on brain structures segmentation in magnetic resonance imaging , 2016, Artif. Intell. Medicine.

[13]  Fernando Flores-Mangas,et al.  Classification of anatomical structures in mr brain images using fuzzy parameters , 2004, IEEE Transactions on Biomedical Engineering.

[14]  Lena Costaridou,et al.  Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine , 2009, IEEE Transactions on Biomedical Engineering.

[15]  Hui Zhang,et al.  A Bayesian Bounded Asymmetric Mixture Model With Segmentation Application , 2014, IEEE Journal of Biomedical and Health Informatics.

[16]  Zexuan Ji,et al.  Bounded asymmetric Gaussian mixture model with spatial constraint for image segmentation , 2016, 2016 International Conference on Progress in Informatics and Computing (PIC).

[17]  Xiao Chen,et al.  A matting method based on full feature coverage , 2018, Multimedia Tools and Applications.

[18]  Keith D. Paulsen,et al.  Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data , 2005, IEEE Transactions on Biomedical Engineering.

[19]  Nikolas P. Galatsanos,et al.  A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[20]  Nikolas P. Galatsanos,et al.  A spatially constrained mixture model for image segmentation , 2005, IEEE Transactions on Neural Networks.

[21]  Qing Song.  A Robust Information Clustering Algorithm , 2005, Neural Computation.

[22]  Arthur W. Toga,et al.  Segmentation of Brain MR Images Using a Charged Fluid Model , 2007, IEEE Transactions on Biomedical Engineering.

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

[24]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[25]  J. Gore,et al.  A robust parametric method for bias field estimation and segmentation of MR images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Jian Li,et al.  Non-local-based spatially constrained hierarchical fuzzy C-means method for brain magnetic resonance imaging segmentation , 2016, IET Image Process..

[27]  P. Deb.  Finite Mixture Models , 2008 .

[28]  Thomas J. Hebert,et al.  Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm , 1998, IEEE Trans. Image Process..

[29]  Yiteng Pan,et al.  A novel region-based active contour model via local patch similarity measure for image segmentation , 2018, Multimedia Tools and Applications.

[30]  Q. M. Jonathan Wu,et al.  Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[32]  Yiteng Pan,et al.  A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation , 2018, Multimedia Tools and Applications.

[33]  Zhimin Wang,et al.  Adaptive spatial information-theoretic clustering for image segmentation , 2009, Pattern Recognit..