Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis

Zahra Hosseinpour, Laura Jonkman, Olayinka Oladosu, Glen Pridham, G. Bruce Pike, Matilde Inglese, Jeroen J. Geurts, Yunyan Zhang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Background: Multiple sclerosis (MS) is a complex disease of the central nervous system involving several types of brain pathology that are difficult to characterize using conventional imaging methods. New method: We originated novel texture analysis and machine learning approaches for classifying MS pathology subtypes as compared with 2 common advanced MRI measures: magnetization transfer ratio (MTR) and fractional anisotropy (FA). Texture analysis used an optimized grey level co-occurrence matrix method with histology-informed 7T T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) from 15 MS and 12 control brain specimens. DTI analysis took an innovative approach that assessed the texture across diffusion directions upsampled from 30 to 90. Tissue types included de- and re-myelinated lesions and normal-appearing areas in both grey and white matter, and diffusely abnormal white matter. Data analyses were stepwise, including: (1) group-wise classification using random forest algorithms based on all or individual imaging parameters; (2) parameter importance ranking; and (3) pairwise analysis using top-ranked features. Results: Texture analysis performed better than MTR and FA, with T2 texture performed the best. T2 texture measures ranked the highest in classifying most grey and white matter tissue types, including de- versus re-myelinated lesions and among grey matter lesion subtypes (accuracy=0.86–0.59; kappa=0.60–0.41). Diffusion texture best differentiated normal appearing and control white matter. Comparison with existing methods: There is no established method in imaging for differentiating MS pathology subtypes. In combined texture analysis and machine learning studies, there is also no direct evidence comparing conventional with advanced MRI measures for assessing MS pathology. Further, this study is unique in conducting innovative texture analysis with DTI following data-augmentation using robust methods. Conclusions: T2 and diffusion MRI texture analysis integrated with machine learning may be valuable approaches for characterizing MS pathology.

Original languageEnglish
Article number109671
JournalJournal of Neuroscience Methods
StatePublished - 1 Sep 2022


  • Diffusion tensor imaging
  • Multiple sclerosis
  • Myelin integrity
  • Random forest
  • T2-weighted MRI
  • Texture analysis


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