Segmentation of Brain MR Images by Using Fully Convolutional Network and Gaussian Mixture Model with Spatial Constraints

Accurate segmentation of brain tissue from magnetic resonance images (MRIs) is a critical task for diagnosis, treatment, and clinical research. In this paper, a novel algorithm (GMMD-U) that incorporates the modified full convolutional neural network U-net and Gaussian-Dirichlet mixture model (GMMD) with spatial constraints is presented. The proposed GMMD-U considers the local spatial relationships by assuming that the prior probability obeys the Dirichlet distribution. Specifically, GMMD is applied for extracting brain tissue that has a distinct intensity region and modified U-net is exploited to correct the wrong-classification areas caused by GMMD or other conventional approaches. The proposed GMMD-U is designed to take advantage of the statistical model-based segmentation techniques and deep neural network. We evaluate the performance of GMMD-U on a publicly available brain MRI dataset by comparing it with several existing algorithms, and the results reported reveal that the proposed framework can accurately detect the brain tissue from MRIs. The proposed learning-based integrated framework could be effective for brain tissue segmentation, which will be helpful for surgeons in brain disease diagnosis.

[1]  Deniz Erdogmus,et al.  Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging , 2017, IEEE Transactions on Medical Imaging.

[2]  Shy Shoham,et al.  Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions , 2002, Pattern Recognit..

[3]  Stéphane Chrétien,et al.  A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model , 2018, Comput. Biol. Medicine.

[4]  Iker Gondra,et al.  MRI segmentation fusion for brain tumor detection , 2017, Inf. Fusion.

[5]  Lixu Gu,et al.  Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set , 2017, IEEE Transactions on Biomedical Engineering.

[6]  Kwanghoon Sohn,et al.  Non-parametric human segmentation using support vector machine , 2016, 2016 IEEE International Conference on Consumer Electronics (ICCE).

[7]  Konstantinos N. Plataniotis,et al.  Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[8]  Jamuna Kanta Sing,et al.  Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation , 2018, Appl. Soft Comput..

[9]  Dong Xu,et al.  Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey , 2018, IEEE Signal Processing Magazine.

[10]  Yuhui Zheng,et al.  Image segmentation using a hierarchical student's-t mixture model , 2017, IET Image Process..

[11]  Nizar Bouguila,et al.  Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application , 2004, IEEE Transactions on Image Processing.

[12]  Q. M. Jonathan Wu,et al.  Bounded Asymmetrical Student's-t Mixture Model , 2014, IEEE Transactions on Cybernetics.

[13]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[14]  Deniz Erdogmus,et al.  Auto-context Convolutional Neural Network for Geometry-Independent Brain Extraction in Magnetic Resonance Imaging , 2017, ArXiv.

[15]  Daniel Sierra-Sosa,et al.  Tissue classification and segmentation of pressure injuries using convolutional neural networks , 2018, Comput. Methods Programs Biomed..

[16]  Stijn Bonte,et al.  Machine learning based brain tumour segmentation on limited data using local texture and abnormality , 2018, Comput. Biol. Medicine.

[17]  Shuying Shen,et al.  Fine-grained leukocyte classification with deep residual learning for microscopic images , 2018, Comput. Methods Programs Biomed..

[18]  Hyung Jeong Yang,et al.  Text line segmentation using a fully convolutional network in handwritten document images , 2018, IET Image Process..

[19]  Hyun-Han Kwon,et al.  A spatial downscaling of soil moisture from rainfall, temperature, and AMSR2 using a Gaussian-mixture nonstationary hidden Markov model , 2017, Journal of Hydrology.

[20]  Jiawei Lai,et al.  A Fusion Algorithm: Fully Convolutional Networks and Student'S-$T$ Mixture Model for Brain Magnetic Resonance Imaging Segmentation , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[21]  Shie Mannor,et al.  A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..

[22]  Peizhong Liu,et al.  Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval. , 2017, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[23]  Kalyani R. Joshi,et al.  A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer's disease , 2018, Biomed. Signal Process. Control..

[24]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[25]  Yuichiro Hayashi,et al.  An application of cascaded 3D fully convolutional networks for medical image segmentation , 2018, Comput. Medical Imaging Graph..

[26]  Guang Yang,et al.  Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels# , 2018, Comput. Methods Programs Biomed..

[27]  Niva Das,et al.  Variable Variance Adaptive Mean-Shift and possibilistic fuzzy C-means based recursive framework for brain MR image segmentation , 2018, Expert Syst. Appl..

[28]  M. Viergever,et al.  Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. , 2016, IEEE transactions on medical imaging.

[29]  Carlos Alberto Silva,et al.  Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields , 2016, Journal of Neuroscience Methods.

[30]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[31]  Hongqing Zhu,et al.  Merging Student's-t and Rayleigh distributions regression mixture model for clustering time-series , 2017, Neurocomputing.

[32]  Sicheng Wang,et al.  Lung vessel segmentation based on random forests , 2017 .

[33]  Dagan Feng,et al.  Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks , 2017, IEEE Transactions on Biomedical Engineering.

[34]  Nikolas P. Galatsanos,et al.  A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures , 2010, IEEE Transactions on Image Processing.

[35]  Yi Zhou,et al.  Image Segmentation Using a Trimmed Likelihood Estimator in the Asymmetric Mixture Model Based on Generalized Gamma and Gaussian Distributions , 2018 .

[36]  Q. M. Jonathan Wu,et al.  Robust Student's-t Mixture Model With Spatial Constraints and Its Application in Medical Image Segmentation , 2012, IEEE Transactions on Medical Imaging.