Automatic White Matter Hyperintensities Segmentation from Brain Magnetic Resonance Images using Polar Transform

White matter hyperintensities (WMH) are areas of lost cells found in the white matter of the brain presenting hyperintensities. WMH segmentation is the initial step to diagnose many brain diseases. Here, we propose an automated method of WMH segmentation designed to deal with brain magnetic resonance imaging (MRI) in polar coordinate system. Moreover, the pattern of clustering used in segmentation is adjusted to achieve the desired cluster properly. Experimental results on cross-sectional images of fluid-attenuated inversion recovery (FLAIR) datasets reveal better performance of the proposed method with simplicity and robustness. The method provides good average accuracies (0.808, 0.803, 0.981 of dice similarity coefficient (DSC), sensitivity (sens) and specificity (spec) respectively) of WHH segmentation in comparison with other method.

[1]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[2]  Annupan Rodtook,et al.  Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities , 2018, Pattern Recognit..

[3]  Jin Young Kim,et al.  White Matter Hyperintensities Extraction Based T2-FLAIR MRI Using Non-Local Means Filter and Nearest Neighbor Algorithm , 2014, 2014 International Conference on IT Convergence and Security (ICITCS).

[4]  P. Kalavathi,et al.  Methods on Skull Stripping of MRI Head Scan Images—a Review , 2016, Journal of Digital Imaging.

[5]  Max A. Viergever,et al.  A survey of medical image registration - under review , 2016, Medical Image Anal..

[6]  Alex Rovira,et al.  Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..

[7]  Daniel Rueckert,et al.  Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation , 2016, SASHIMI@MICCAI.

[8]  C. Reynolds,et al.  A fully automated method for quantifying and localizing white matter hyperintensities on MR images , 2006, Psychiatry Research: Neuroimaging.

[9]  Ji Won Han,et al.  Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images , 2014, Neuroradiology.

[10]  Mikael Persson,et al.  Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization , 2015, Journal of Digital Imaging.

[11]  V.R.S Mani,et al.  Survey of Medical Image Registration , 2013 .

[12]  Ludovica Griffanti,et al.  BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.

[13]  Bruno M. Carvalho,et al.  Multiseeded Segmentation Using Fuzzy Connectedness , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  D. Chaudhuri,et al.  A simple least squares method for fitting of ellipses and circles depends on border points of a two-tone image and their 3-D extensions , 2010, Pattern Recognit. Lett..

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

[16]  Yongzhao Zhan,et al.  Multimodal spatial-based segmentation framework for white matter lesions in multi-sequence magnetic resonance images , 2017, Biomed. Signal Process. Control..

[17]  Shuxu Guo,et al.  An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. , 2017, Magnetic resonance imaging.

[18]  Liang Chen,et al.  A large margin algorithm for automated segmentation of white matter hyperintensity , 2018, Pattern Recognit..

[19]  Estela Díaz-López,et al.  Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features , 2017, Neuroinformatics.

[20]  Ludovica Griffanti,et al.  Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding , 2018, NeuroImage.

[21]  Carlo Caltagirone,et al.  White matter hyperintensities segmentation: a new semi-automated method , 2013, Front. Aging Neurosci..

[22]  Peter A. Calabresi,et al.  A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions , 2010, NeuroImage.

[23]  Bostjan Likar,et al.  A Novel Public MR Image Dataset of Multiple Sclerosis Patients With Lesion Segmentations Based on Multi-rater Consensus , 2017, Neuroinformatics.

[24]  Alex Rovira,et al.  Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding , 2014, Comput. Methods Programs Biomed..

[25]  D. Louis Collins,et al.  Evaluating intensity normalization on MRIs of human brain with multiple sclerosis , 2011, Medical Image Anal..

[26]  F. Barkhof,et al.  Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)☆ , 2013, NeuroImage: Clinical.

[27]  Yutaka Hata,et al.  Transcranial ultrasonography system for visualizing skull and brain surface aided by fuzzy expert system , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Wei-Shi Zheng,et al.  Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images , 2018, NeuroImage.

[29]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[30]  Alex Rovira,et al.  Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach , 2017, NeuroImage.

[31]  Nico Karssemeijer,et al.  Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease. , 2016, Medical physics.

[32]  Rosella Cataldo,et al.  Robust Intensity Standardization in Brain Magnetic Resonance Images , 2015, Journal of Digital Imaging.

[33]  B. Mazoyer,et al.  An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases , 2007, Neuroradiology.

[34]  Sumiaki Matsumoto,et al.  A fast way to visualize the brain surface with volume rendering of MRI data , 1999, Journal of Digital Imaging.

[35]  R. Wiest,et al.  Introduction to Magnetic Resonance Imaging for Neurologists , 2016, Continuum.

[36]  Frederik Barkhof,et al.  Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset , 2019, Scientific Reports.

[37]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[38]  Cornelis H. Slump,et al.  Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis. , 2008, The MIDAS Journal.