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Updated: Apr 7, 2026
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Application of Texture Analysis in Diagnosis of Multiple Sclerosis by Magnetic Resonance Imaging
Ali Abbasian Ardakani, Akbar Gharbali1, Yalda Saniei
1Medical Physics Department, Medical Faculty, Urmia University of Medical Sciences, Urmia, Iran. gharbali@yahoo.com.
Introduction:
Visual inspection by magnetic resonance (MR) images cannot detect microscopic tissue changes occurring in MS in normal appearing white matter (NAWM) and may be perceived by the human eye as having the same texture as normal white matter (NWM). The aim of the study was to evaluate computer aided diagnosis (CAD) system using texture analysis (TA) in MR images to improve accuracy in identification of subtle differences in brain tissue structure.
Material & Methods:
The MR image database comprised 50 MS patients and 50 healthy subjects. Up to 270 statistical texture features extract as descriptors for each region of interest. The feature reduction methods used were the Fisher method, the lowest probability of classification error and average correlation coefficients (POE+ACC) method and the fusion Fisher plus the POE+ACC (FFPA) to select the best, most effective features to differentiate between MS lesions, NWM and NAWM. The features parameters were used for texture analysis with principle component analysis (PCA) and linear discriminant analysis (LDA). Then first nearest-neighbour (1-NN) classifier was used for features resulting from PCA and LDA. Receiver operating characteristic (ROC) curve analysis was used to examine the performance of TA methods.
Results:
The highest performance for discrimination between MS lesions, NAWM and NWM was recorded for FFPA feature parameters using LDA; this method showed 100% sensitivity, specificity and accuracy and an area of Az=1 under the ROC curve.
Conclusion:
TA is a reliable method with the potential for effective use in MR imaging for the diagnosis and prediction of MS.
Insights
Computer-aided diagnosis using texture analysis (TA) in MRI can detect subtle differences in brain tissue. This method accurately differentiates multiple sclerosis (MS) lesions, normal appearing white matter (NAWM), and normal white matter (NWM).
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Biomedical Engineering
Background:
- Standard magnetic resonance (MR) imaging struggles to detect microscopic tissue changes in normal-appearing white matter (NAWM) in multiple sclerosis (MS) patients.
- Visual inspection of MR images may not distinguish subtle textural differences between NAWM and normal white matter (NWM).
Purpose of the Study:
- To evaluate a computer-aided diagnosis (CAD) system employing texture analysis (TA) for improved accuracy in identifying subtle brain tissue variations in MR images.
- To differentiate between MS lesions, NAWM, and NWM using advanced image analysis techniques.
Main Methods:
- Utilized a dataset of MR images from 50 MS patients and 50 healthy subjects.
- Extracted up to 270 statistical texture features and applied feature reduction methods (Fisher, POE+ACC, FFPA).
- Employed principal component analysis (PCA) and linear discriminant analysis (LDA) for feature analysis, with a 1-NN classifier and ROC curve analysis for performance evaluation.
Main Results:
- The FFPA feature parameters combined with LDA demonstrated superior performance in discriminating between MS lesions, NAWM, and NWM.
- Achieved 100% sensitivity, specificity, and accuracy with an Area Under the Curve (Az) of 1.
Conclusions:
- Texture analysis (TA) is a dependable method for MR imaging in the diagnosis and prediction of MS.
- The developed CAD system shows significant potential for enhancing the detection of MS-related tissue changes.
