Related Experiment Video
Updated: Jun 1, 2026
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Segmentation of multiple sclerosis lesions in MR images: a review
Daryoush Mortazavi1, Abbas Z Kouzani, Hamid Soltanian-Zadeh
1School of Engineering, Deakin University, Geelong, Victoria 3216, Australia. dmortaza@deakin.edu.au
Introduction:
Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease.
Methods:
For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past.
Results:
This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques.
Conclusions:
Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.
Insights
This review compares automated methods for segmenting multiple sclerosis (MS) brain lesions in MRI scans. Advanced techniques like Bayesian and intelligent classifiers improve accuracy over manual segmentation.
Area of Science:
- Medical Imaging
- Neurology
- Computer Vision
Background:
- Multiple sclerosis (MS) is an inflammatory demyelinating disease affecting the nervous system, causing various disabilities.
- Early detection and progression estimation of MS are crucial for effective patient management.
Purpose of the Study:
- To review and compare various automated methods for segmenting multiple sclerosis lesions in brain MRI scans.
- To address the limitations of manual lesion segmentation, including time consumption, subjectivity, and potential for human error.
Main Methods:
- The review covers conventional segmentation techniques such as multilevel thresholding and region growing.
- It also examines Bayesian methods, parameter estimation algorithms (expectation maximization, adaptive mixture models), and machine learning approaches (kNN, Parzen window).
Main Results:
- A comparison of diverse MS lesion segmentation methods is presented.
- The review categorizes and analyzes both supervised and unsupervised techniques for lesion detection and segmentation.
Conclusions:
- Integrating knowledge-based methods (e.g., atlas-based) with Bayesian approaches enhances segmentation accuracy.
- Utilizing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks significantly reduces misclassified voxels.
