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Updated: Jul 9, 2026
Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
Published on: July 19, 2019
A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images
Rasoul Khayati1, Mansur Vafadust, Farzad Towhidkhah
1Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Abstract:
It is very important to detect stages of multiple sclerosis (MS) lesions in order to exactly quantify involved voxels. In this paper, a novel method is proposed for automatic detection of different stages of MS lesions in the brain magnetic resonance (MR) images, in fluid attenuated inversion recovery (FLAIR) studies. In the proposed method, firstly, MS lesion voxels are segmented in FLAIR images based on adaptive mixtures method (AMM) and Markov Random Field (MRF) model. Then, signal intensity of each lesion voxel is modeled as a linear combination of signals related to the normal and also abnormal parts, in the voxel. By applying an optimal threshold, voxels with new intensities are primarily classified into two stages: previously destructed (chronic) and on going destruction (acute) lesions. Finally, the acute lesions, according to their activities, are classified, by another optimal threshold, into two new stages, early and recent acute. Evaluation of the proposed method was performed by manual segmentation of chronic and enhanced (early) acute lesions in gadolinium enhanced T1-weighted (Gad-E-T1-w) images by studying T1-weighted (T1-w) and T2-weighted (T2-w) images, using similarity criteria. The results showed a good correlation between the lesions segmented by the proposed method and by experts manually. Thus, the suggested method is useful to reduce the need for paramagnetic materials in contrast enhanced MR imaging which is a routine procedure for separation of acute and chronic lesions.
Insights
This study introduces an automated method to detect multiple sclerosis (MS) lesion stages in brain MRI scans. The novel approach accurately differentiates acute and chronic MS lesions, potentially reducing the need for contrast agents.
Area of Science:
- Medical Imaging
- Neurology
- Computer Science
Background:
- Accurate quantification of multiple sclerosis (MS) lesions is crucial for treatment monitoring.
- Distinguishing between acute and chronic MS lesions is essential but often requires contrast-enhanced MRI.
Purpose of the Study:
- To propose a novel, automated method for detecting and classifying different stages of MS lesions in brain MR images.
- To reduce reliance on contrast-enhanced MRI for lesion staging.
Main Methods:
- MS lesion voxels are segmented using adaptive mixtures method (AMM) and Markov Random Field (MRF) models in FLAIR images.
- Lesion voxels are classified into chronic and acute stages based on signal intensity modeling and optimal thresholding.
- Acute lesions are further sub-classified into early and recent acute stages using another optimal threshold.
Main Results:
- The proposed method demonstrated good correlation with manual segmentation by experts.
- The automated method successfully differentiated between various stages of MS lesions.
- The technique shows promise in reducing the need for paramagnetic contrast agents in routine MRI procedures.
Conclusions:
- The developed automated method provides an effective means for staging MS lesions in brain MR imaging.
- This approach offers a valuable alternative for lesion characterization, potentially improving efficiency and reducing costs associated with contrast-enhanced scans.
