RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI)-based texture analysis has been shown to be effective in classifying multiple sclerosis lesions. Regarding the clinical use of texture analysis in multiple sclerosis, our intention was to show which parts of the analysis are sensitive to slight changes in textural data acquisition and which steps tolerate interference. MATERIALS AND METHODS: The MRI datasets of 38 multiple sclerosis patients were used in this study. Three imaging sequences were compared in quantitative analyses, including a comparison of anatomical levels of interest, variance between sequential slices and two methods of region of interest drawing. We focused on the classification of white matter and multiple sclerosis lesions in determining the discriminatory power of textural parameters. Analyses were run with MaZda software for texture analysis, and statistical tests were performed for raw parameters. RESULTS: MRI texture analysis based on statistical, autoregressive-model and wavelet-derived texture parameters provided an excellent distinction between the image regions corresponding to multiple sclerosis plaques and white matter or normal-appearing white matter with high accuracy (nonlinear discriminant analysis 96%-100%). There were no significant differences in the classification results between imaging sequences or between anatomical levels. Standardized regions of interest were tolerant of changes within an anatomical level when intra-tissue variance was tested. CONCLUSION: The MRI texture analysis protocol with fixed imaging sequence and anatomical levels of interest shows promise as a robust quantitative clinical means for evaluating multiple sclerosis lesions. Copyright (c) 2010 AUR. Published by Elsevier Inc. All rights reserved.
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI)-based texture analysis has been shown to be effective in classifying multiple sclerosis lesions. Regarding the clinical use of texture analysis in multiple sclerosis, our intention was to show which parts of the analysis are sensitive to slight changes in textural data acquisition and which steps tolerate interference. MATERIALS AND METHODS: The MRI datasets of 38 multiple sclerosispatients were used in this study. Three imaging sequences were compared in quantitative analyses, including a comparison of anatomical levels of interest, variance between sequential slices and two methods of region of interest drawing. We focused on the classification of white matter and multiple sclerosis lesions in determining the discriminatory power of textural parameters. Analyses were run with MaZda software for texture analysis, and statistical tests were performed for raw parameters. RESULTS: MRI texture analysis based on statistical, autoregressive-model and wavelet-derived texture parameters provided an excellent distinction between the image regions corresponding to multiple sclerosis plaques and white matter or normal-appearing white matter with high accuracy (nonlinear discriminant analysis 96%-100%). There were no significant differences in the classification results between imaging sequences or between anatomical levels. Standardized regions of interest were tolerant of changes within an anatomical level when intra-tissue variance was tested. CONCLUSION: The MRI texture analysis protocol with fixed imaging sequence and anatomical levels of interest shows promise as a robust quantitative clinical means for evaluating multiple sclerosis lesions. Copyright (c) 2010 AUR. Published by Elsevier Inc. All rights reserved.
Authors: S-P Hojjat; M Kincal; R Vitorino; C G Cantrell; A Feinstein; L Zhang; L Lee; P O'Connor; T J Carroll; R I Aviv Journal: AJNR Am J Neuroradiol Date: 2016-03-24 Impact factor: 3.825
Authors: Sami J Savio; Lara C V Harrison; Tiina Luukkaala; Tomi Heinonen; Prasun Dastidar; Seppo Soimakallio; Hannu J Eskola Journal: Biomed Eng Online Date: 2010-10-18 Impact factor: 2.819
Authors: Carlos Fernandez-Lozano; Jose A Seoane; Marcos Gestal; Tom R Gaunt; Julian Dorado; Alejandro Pazos; Colin Campbell Journal: Sci Rep Date: 2016-01-13 Impact factor: 4.379