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Brain tissue magnetic resonance imaging segmentation using anisotropic textural features

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Abstract

One of the most useful diagnostic tests for brain diseases is magnetic resonance imaging (MRI). It is a demanding task to segment brain tissue into cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) for early diagnosis of brain diseases and their causes based on MRI images. This study proposed a novel CSF, GM, and WM segmentation method employing just one modality (FLAIR image). The proposed segmentation is based on an anisotropic textural analysis of brain MRIs. For this purpose, the gray level co-occurrence matrix (GLCM) and curvelet transform were combined. The curvelet transform is an anisotropic multi-resolution method that was fully exploited for brain tissue segmentation. In addition to the information richness of GLCM features, the Relief method was utilized to achieve the best feature subset. Finally, support vector machine (SVM) and fuzzy C-means (FCM) were applied to recognize each pixel's label. FCM provided better segmentation results for CSF, GM, and WM with more selected features than SVM. Furthermore, FCM could track the area changes of scan sequences more accurately than SVM. Our segmentation framework involves analyzing an anisotropic curvelet of the statistical features, feature selection, clustering, and a classification-based method for segmentation. The proposed method outperforms well compared to other methods implemented on the MRBrainS18 challenge dataset. This study's outcomes can automatically detect the area of the brain tissue for all scans and capture the variations, reducing the specialist's burden of evaluating each scan and improving the performance.

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Data availability

The data supporting this study's findings are publicly available from https://mrbrains18.isi.uu.nl/data/.

References

  1. Yousaf T, Dervenoulas G, Politis M (2018) Advances in MRI Methodology. Int Rev Neurobiol 141:31–76

    Google Scholar 

  2. Raichle ME, Mintun MA (2006) Brain work and brain imaging. Annu Rev Neurosci 29:449–476

    Google Scholar 

  3. Goodkind M et al (2015) Identification of a common neurobiological substrate for mental Illness. JAMA Psychiat 72(4):305–315

    Google Scholar 

  4. González-Villà S et al (2019) Brain structure segmentation in the presence of multiple sclerosis lesions. NeuroImage: Clinical 22:101709

    Google Scholar 

  5. Beqiri A et al (2018) Whole-brain 3D FLAIR at 7T using direct signal control. Magn Reson Med 80(4):1533–1545

    Google Scholar 

  6. Roozpeykar S et al (2022) Contrast-enhanced weighted-T1 and FLAIR sequences in MRI of meningeal lesions. Am J Nucl Med Mol Imaging 12(2):63–63

    Google Scholar 

  7. Davis TS et al (2020). Comparison of T1-Post and FLAIR-Post MRI for identification of traumatic meningeal enhancement in traumatic brain injury patients. Plos one, 15(7):e0234881

  8. Azad R et al (2017) Qualitative and quantitative comparison of contrast-enhanced fluid-attenuated inversion recovery, magnetization transfer spin echo, and Fat-saturation T1-weighted sequences in infectious meningitis. Korean J Radiol 18(6):973–982

    Google Scholar 

  9. Singh MK, Singh KK (2021) A review of publicly available automatic brain segmentation methodologies, machine learning models, recent advancements, and their comparison. Ann Neurosci 28(1–2):82–82

    Google Scholar 

  10. Feng Y et al (2021) An interval iteration based multilevel thresholding algorithm for brain MR image segmentation. Entropy 23(11):1429

    MathSciNet  Google Scholar 

  11. Song J, Zhang Z (2021) Magnetic resonance imaging segmentation via weighted level set model based on local kernel metric and spatial constraint. Entropy 23(9):1196

  12. Gefen S, Kiryati N, Nissanov J (2008) Atlas-based indexing of brain sections via 2-D to 3-D image registration. IEEE Trans Biomed Eng 55(1):147–156

    Google Scholar 

  13. Zhu H et al (2020) FCN based label correction for multi-atlas guided organ segmentation. Neuroinformatics 18(2):319–331

    MathSciNet  Google Scholar 

  14. Sun L, Zhang L, Zhang DQ (2019) Multi-atlas based methods in brain MR image segmentation. Chin Med Sci J 34(2):110–119

  15. Cabezas M et al (2011) A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Prog Biomed 104(3):e158–e177

  16. Huang A, Abugharbieh R, Tam R (2009) A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI. IEEE Trans Biomed Eng 56(7):1838–1848

    Google Scholar 

  17. Zhao J et al (2019) Supervised brain tumor segmentation based on gradient and context-sensitive features. Front Neurosci 13:144

  18. Ghosal P et al (2021) MhURI: a supervised segmentation approach to leverage salient brain tissues in magnetic resonance images. Comput Methods Prog Biomed 200:105841

    Google Scholar 

  19. Fang F et al (2021) Self-supervised multi-modal hybrid fusion network for brain tumor segmentation. IEEE J Biomed Health Inform 26(11):53105320

  20. Martins SB, Telea AC, Falcão AX (2020) Investigating the impact of supervoxel segmentation for unsupervised abnormal brain asymmetry detection. Comput Med Imaging Graph 85:101770

    Google Scholar 

  21. Baur C et al (2021) Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med Image Anal 69:101952

  22. Halder A, Talukdar NA (2019) Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI. Magn Reson Imaging 62:129–151

    Google Scholar 

  23. Ranjbarzadeh R et al (2021) Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11(1):10930

  24. Dora L et al (2017) State-of-the-art methods for brain tissue segmentation: a review. IEEE Rev Biomed Eng 10:235–249

    Google Scholar 

  25. Upadhyay K, Agrawal M, Vashist P (2020) Unsupervised multiscale retinal blood vessel segmentation using fundus images. IET Image Proc 14(11):2616–2625

    Google Scholar 

  26. Rostami M, Berahmand K, Forouzandeh S (2020) A novel method of constrained feature selection by the measurement of pairwise constraints uncertainty. J Big Data 7(1):1–21

    Google Scholar 

  27. Bal A et al (2019) An efficient wavelet and curvelet-based PET image denoising technique. Med Biol Eng Compu 57:2567–2598

    Google Scholar 

  28. Kaur S, Sahambi JS (2016) Curvelet initialized level set cell segmentation for touching cells in low contrast images. Comput Med Imaging Graph 49:46–57

    Google Scholar 

  29. Esmaeili M et al (2020) Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography. Biomed Opt Express 11(2):586–586

    MathSciNet  Google Scholar 

  30. Krishnammal PM, Raja SS (2019) Medical image segmentation using fast discrete curvelet transform and classification methods for MRI brain images. Multimed Tools Appl 79(15):10099–10122

    Google Scholar 

  31. Biswas S, Sil J (2020) An efficient face recognition method using contourlet and curvelet transform. J King Saud Univ-Comput Inform Sci 32(6):718–729

    Google Scholar 

  32. Imtiaz H, Fattah SA (2012) A curvelet domain face recognition scheme based on local dominant feature extraction. Int Sch Res Notices 2012:1–13

  33. Kanagaraj K, Priya GGL (2022) Curvelet transform based feature extraction and selection for multimedia event classification. J King Saud Univ-Comput Inform Sci 34(2):375–383

    Google Scholar 

  34. Li F et al (2022) Least-squares reverse time migration with curvelet-domain preconditioning operators. IEEE Transact Geosci Remote Sens 60:1–13

  35. Oulhaj H et al (2017) Anisotropic discrete dual-tree wavelet transform for improved classification of trabecular bone. IEEE Trans Med Imaging 36(10):2077–2086

    Google Scholar 

  36. Thakral S, Manhas P (2019) Image processing by using different types of discrete wavelet transform. Commun Comput Inform Sci 955:499–507

    Google Scholar 

  37. Himanshi et al (2016) Medical image fusion in curvelet domain employing PCA and maximum selection rule. Adv Intell Syst Comput 379:1–9

    Google Scholar 

  38. Candès E et al (2006) Fast discrete curvelet transforms. Multiscale Model Simul. https://doi.org/10.1137/05064182X.5(3):p.861-899

    Article  MathSciNet  Google Scholar 

  39. Srivastava D et al (2020) Pattern-based image retrieval using GLCM. Neural Comput Appl 32:10819–10832

    Google Scholar 

  40. Khan MA et al (2021) VGG19 network assisted joint segmentation and classification of lung nodules in CT images. Diagnostics 11(12):2208

    Google Scholar 

  41. Zulfira FZ, Suyanto S, Septiarini A (2021) Segmentation technique and dynamic ensemble selection to enhance glaucoma severity detection. Comput Biol Med 139:104951

    Google Scholar 

  42. Riana D, Rahayu S, Hasan M (2021) Comparison of segmentation and identification of swietenia mahagoni wood defects with augmentation images. Heliyon 7(6):e07417

    Google Scholar 

  43. Bommert A et al (2022) Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Brief Bioinform 23(1):1–13

  44. El-Kenawy ESM et al (2020) Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images. IEEE Access 8:179317–179335

  45. Liu Z et al (2021) Using embedded feature selection and CNN for classification on CCD-INID-V1-A New IoT dataset. Sensors 21(14):4834

  46. Mandal M et al (2021) A tri-stage wrapper-filter feature selection framework for disease classification. Sensors 21(16):5571

  47. Dinsdale NK et al (2021) Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 224:117401

    Google Scholar 

  48. John JP et al (2015) A systematic examination of brain volumetric abnormalities in recent-onset schizophrenia using voxel-based, surface-based and region-of-interest-based morphometric analyses. J Negat Results BioMed 14(1):1–15

  49. Riddle K, Cascio CJ, Woodward ND (2017) Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE). Brain Imaging Behav 11(2):541–551

    Google Scholar 

  50. Cole JH et al (2018) Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury. Brain 141(3):822–822

    MathSciNet  Google Scholar 

  51. Faria AV et al (2017) Brain MRI pattern recognition translated to clinical scenarios. Front Neurosci 11:578

    Google Scholar 

  52. Aghdasi N et al (2017) Efficient orbital structures segmentation with prior anatomical knowledge. J Med Imaging 4(3):034501

    Google Scholar 

  53. Ward PGD et al (2018) Combining images and anatomical knowledge to improve automated vein segmentation in MRI. Neuroimage 165:294–305

    Google Scholar 

  54. Chang H et al (2017) A new variational method for bias correction and its applications to rodent brain extraction. IEEE Trans Med Imaging 36(3):721–733

    Google Scholar 

  55. Kilsdonk ID et al (2013) Improved differentiation between MS and vascular brain lesions using FLAIR* at 7 Tesla. Eur Radiol 24(4):841–849

    Google Scholar 

  56. Jin T et al (2021) Utility of contrast-enhanced T2 FLAIR for imaging brain metastases using a half-dose high-relaxivity contrast agent. AJNR Am J Neuroradiol 42(3):457–463

    Google Scholar 

  57. Ségonne F et al (2004) A hybrid approach to the skull stripping problem in MRI. Neuroimage 22(3):1060–1075

    Google Scholar 

  58. Biratu ES et al (2021) Enhanced region growing for brain tumor MR image segmentation. J Imaging 7(2):22

    Google Scholar 

  59. Villanueva-Meyer JE, Mabray MC, Cha S (2017) Current clinical brain tumor imaging. Neurosurgery 81(3):397–397

    Google Scholar 

  60. Turesky TK, Vanderauwera J, Gaab N (2021) Imaging the rapidly developing brain: current challenges for MRI studies in the first five years of life. Dev Cogn Neurosci 47:100893

  61. Starck J-L, Murtagh F, Fadili JM (2010) Sparse image and signal processing: wavelets, curvelets, morphological diversity. Cambridge University Press, Cambridge

  62. Wang H et al (2019) Reconstruction of compressively sampled MR images based on a local shrinkage thresholding algorithm with curvelet transform. Med Biol Eng Comput 57(10):2145–2158

    Google Scholar 

  63. Nayak DR et al (2019) Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities. Comput Med Imaging Graph 77:101656–101656

    Google Scholar 

  64. Ramamurthy K, Menaka R (2019) Delineation of ischemic lesion from brain MRI using symmetric bit plane pattern and curvelet co-occurrence matrix. Int J Innov Technol Explor Eng 8:201–206

    Google Scholar 

  65. You Q et al (2022) Curvelet transform-based sparsity promoting algorithm for fast ultrasound localization microscopy. IEEE Transact Med Imaging 41(9):2385–2398

  66. Shinde AA, Rahulkar AD, Patil CY (2017) Fast discrete curvelet transform-based anisotropic feature extraction for biomedical image indexing and retrieval. Int J Multimed Info Retr 6(4):281–288

    Google Scholar 

  67. Liu Y et al (2020) Fibrillar collagen quantification with curvelet transform based computational methods. Front Bioeng Biotechnol 8:198

  68. Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684

    MathSciNet  Google Scholar 

  69. Francis SV, Sasikala M, Saranya S (2014) Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J Med Syst 38(4):1–9

  70. Ghribi O et al (2018) Multiple sclerosis exploration based on automatic MRI modalities segmentation approach with advanced volumetric evaluations for essential feature extraction. Biomed Signal Process Control 40:473–487

    Google Scholar 

  71. Shanmuganathan M et al (2020) Review of advanced computational approaches on multiple sclerosis segmentation and classification. IET Signal Proc 14(6):333–341

    Google Scholar 

  72. Despotović I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015:1–23

  73. Jia J et al (2013) Object-oriented feature selection of high spatial resolution images using an improved relief algorithm. Math Comput Model 58(3–4):619–626

    Google Scholar 

  74. Nayak DR et al (2017) Automated pathological brain detection system: a fast discrete curvelet transform and probabilistic neural network based approach. Expert Syst Appl 88:152–164

    Google Scholar 

  75. Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med 112:103375

    Google Scholar 

  76. Rostami M, Berahmand K, Forouzandeh S (2021) A novel community detection based genetic algorithm for feature selection. J Big Data 8(1):1–27

    Google Scholar 

  77. Hosseinipanah S et al (2019) Multiple sclerosis lesions segmentation in magnetic resonance imaging using Ensemble Support Vector Machine (ESVM). J Biomed Phys Eng 9(6):699–710

    Google Scholar 

  78. Nayak D, Dash R, Majhi B (2017) Stationary wavelet transform and AdaBoost with SVM based pathological brain detection in MRI scanning. CNS Neurol Disord: Drug Targets 16(2):137–149

    Google Scholar 

  79. Kaya IE et al (2017) PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Programs Biomed 140:19–28

    Google Scholar 

  80. Urbanowicz RJ et al (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189–203

    Google Scholar 

  81. López NC et al (2021) Evaluation of feature selection techniques for breast cancer risk prediction. Int J Environ Res Public Health 18(20):1

    Google Scholar 

  82. Urbanowicz RJ et al (2018) Benchmarking relief-based feature selection methods for bioinformatics data mining. J Biomed Inform 85:168–188

    Google Scholar 

  83. Le TT et al (2019) Statistical Inference Relief (STIR) feature selection. Bioinformatics 35(8):1358–1365

    Google Scholar 

  84. Qian W et al (2020) Multi-label feature selection based on label distribution and feature complementarity. Appl Soft Comput 90:106167

    Google Scholar 

  85. Hussein AF et al (2021) An automated high-accuracy detection scheme for myocardial ischemia based on multi-lead long-interval ECG and Choi-Williams Time-Frequency analysis incorporating a multi-class SVM classifier. Sensors 21(7):2311

  86. AvuÇLu E (2022) COVID-19 detection using X-ray images and statistical measurements. Measurement 201:111702

    Google Scholar 

  87. Hu M et al (2021) Fuzzy system based medical image processing for brain disease prediction. Front Neurosci 15:714318

  88. Rout R et al (2021) Skin lesion extraction using multiscale morphological local variance reconstruction based watershed transform and fast fuzzy c-means clustering. Symmetry 13(11):2085

  89. Chang-Chien SJ, Nataliani Y, Yang MS (2021) Gaussian-kernel c-means clustering algorithms. Soft Comput 25(3):1699–1716

    Google Scholar 

  90. Huang H et al (2019) Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7:12386–12396

    Google Scholar 

  91. Bai X et al (2018) Intuitionistic center-free FCM clustering for MR brain image segmentation. IEEE J Biomed Health Inform 23(5):2039–2051

    Google Scholar 

  92. Bai X et al (2018) Similarity measure-based possibilistic FCM with label information for brain MRI segmentation. IEEE Transact Cybern 49(7):2618–2630

    Google Scholar 

  93. Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40(3):825–838

    Google Scholar 

  94. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  95. Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recogn 46(3):914–924

    Google Scholar 

  96. Crammer K, Singer Y (2022) On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res 2:265–292

    Google Scholar 

  97. Zhang YJ (2001) A review of recent evaluation methods for image segmentation. Proceedings of the Sixth International Symposium on Signal Processing and its Applications 1:148–151

  98. Polak M, Zhang H, Pi M (2009) An evaluation metric for image segmentation of multiple objects. Image Vis Comput 27(8):1223–1227

    Google Scholar 

  99. Kavitha MS, Shanthini J, Sabitha R (2019) ECM-CSD: an efficient classification model for cancer stage diagnosis in CT lung images using FCM and SVM techniques. J Med Syst 43:1–9

    Google Scholar 

  100. Zhang J et al (2020) Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI. Brain Imaging Behav 14(6):2333–2340

    Google Scholar 

  101. Mendes SL et al (2021) Estimating gender and age from brain structural MRI of children and adolescents: a 3D convolutional neural network multitask learning model. Comput Intell Neurosci 2021:5550914

    Google Scholar 

  102. Deng Y et al (2019) A new framework to reduce doctor’s workload for medical image annotation. IEEE Access 7:107097–107104

    Google Scholar 

  103. Li H et al (2020) Superpixel-guided label softening for medical image segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science 12264:227–237

  104. Weiss DA et al (2021) Automated multiclass tissue segmentation of clinical brain MRIs with lesions. NeuroImage Clin 31:102769

  105. Dorent R et al (2021) Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets. Med Image Anal 67:101862

    Google Scholar 

  106. Sun Q et al (2021) HybridCTrm: bridging CNN and transformer for multimodal brain image segmentation. J Healthc Eng 2021:7467261

    Google Scholar 

  107. Gozzi N et al (2022) XAI for myo-controlled prosthesis: explaining EMG data for hand gesture classification. Knowl-Based Syst 240:108053

    Google Scholar 

  108. Papadomanolakis TN et al (2023) Tumor diagnosis against other brain diseases using T2 MRI brain images and CNN binary classifier and DWT. Brain Sci 13(2):348

    Google Scholar 

  109. Zhao Y et al (2023) WRANet: wavelet integrated residual attention U-Net network for medical image segmentation. Complex Intell Syst pp 1–13

  110. Pilli R et al (2023) Association of white matter volume with brain age classification using deep learning network and region wise analysis. Eng Appl Artif Intell 125:106596

    Google Scholar 

  111. Giedd JN et al (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 2(10):861–863

    Google Scholar 

  112. Borys D et al (2021) Non-parametric MRI brain atlas for the Polish population. Front Neuroinform 15:684759

    Google Scholar 

  113. Xu X et al (2022) Spatiotemporal atlas of the fetal brain depicts cortical developmental gradient. J Neurosci 42(50):9435–9449

    Google Scholar 

  114. Choi JY et al (2023) Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting. Cereb Cortex 33(7):3562–3574

    Google Scholar 

  115. Rao AT, Chou KL, Patil PG (2023) Localization of deep brain stimulation trajectories via automatic mapping of microelectrode recordings to MRI. J Neural Eng 20(1):016056

    Google Scholar 

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Authors and Affiliations

  1. Medical Informatics Department, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Afrooz Arzehgar

  2. Biomedical Engineering Department, Semnan University, Semnan, Iran

    Fatemeh Davarinia

  3. Biomedical Engineering Department, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Mohammad Mahdi Khalilzadeh

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  1. Afrooz Arzehgar
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  2. Fatemeh Davarinia
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  3. Mohammad Mahdi Khalilzadeh
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A. Arzehgar.: Methodology, Writing- Original draft preparation, Software, Validation. F. Davarina: Supervision, Visualization, Reviewing and Editing. M.M. Khalilzadeh: Conceptualization, Methodology

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Correspondence to Fatemeh Davarinia.

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Arzehgar, A., Davarinia, F. & Khalilzadeh, M.M. Brain tissue magnetic resonance imaging segmentation using anisotropic textural features. Multimed Tools Appl 83, 49195–49212 (2024). https://doi.org/10.1007/s11042-023-17259-9

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