Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability

@inproceedings{Loizou2011BrainWM,
  title={Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability},
  author={Christos P. Loizou and Efthyvoulos C. Kyriacou and Ioannis Seimenis and Marios Pantziaris and Christodoulos S. Christodoulou and Constantinos S. Pattichis},
  booktitle={EANN/AIAI},
  year={2011}
}
This study investigates the application of classification methods for the prognosis of future disability on MRI-detectable brain white matter lesions in subjects diagnosed with clinical isolated syndrome CIS of multiple sclerosis MS. In order to achieve these we had collected MS lesions from 38 subjects, manually segmented by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans. The patients have been divided into two groups, those… 
Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability
TLDR
Evidence is provided that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MR images in MS.
LOCALIZING MULTIPLE SCLEROSIS LESIONS FROM T2W MRI BY UTILIZING IMAGE HISTOGRAM FEATURES
TLDR
A fast localization and segmentation algorithms to localize MS lesions based on histogram features besides morphological features such as place, area, and intensity of the lesions appeared in the monitored places are proposed.
Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks
TLDR
Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor and exhibited outstanding performance as compared to other research outcomes.
Supervised and Unsupervised Machine Learning Techniques for Multiple Sclerosis Identification: A Performance Comparative Analysis
TLDR
It has been verified that MSD identification from healthy and unhealthy brain MR images based on the proposed methodology using supervised machine learning techniques yields accuracy of 96.55% which is better than existing state-of-the-art techniques and unsupervised machineLearning techniques.
Brain MR image normalization in texture analysis of multiple sclerosis
TLDR
Six different MRI intensity normalization methods are investigated and the most appropriate for the pre-processing of brain T2-weighted MR images acquired from 22 symptomatic untreated multiple sclerosis (MS) subjects and 10 healthy volunteers are proposed.
A Combination of Global and Local Features for Brain White Matter Lesion Classification
TLDR
The proposed approach combines the global and local features and yields the best tumor detection performance, using the combination of magnitude and phase features of the descriptor angular radial transform.
Classification of Hydrocephalus using TAN
TLDR
This survey will use the Tree augmented Naive Bayes classification technique to detect and classify one of the children brain diseases, and classify the hydrocephalus type depending on MRI, and it's expected to achieve a high accuracy in Hydrocephalus detection to help the radiologist in the disease detection process.
Brain tumor segmentation using DE embedded OTSU method and neural network
TLDR
This work has proposed an algorithm to obtain a global thresholding value for a particular image and used Differential Evolution algorithm embedded with OTSU method and trained neural network to find out an optimal threshold value.
Image descriptors in radiology images: a systematic review
TLDR
Over 70 studies related to the application of image descriptors of different natures—e.g., intensity, texture, shape—in medical image analysis are analyzed, featuring four imaging modalities: mammography, PET, CT and MRI.
THE AUTOMATED SEGMENTATION TECHNIQUES OF T2-WEIGHTED MRI IMAGES USING K-MEANS CLUSTERING AND OTSU-BASED THRESHOLDING METHOD
TLDR
The k-means clustering and Otsu-based thresholding of MRI images segmentation are widely used to cluster the lesions in human brain and it is justified that the proposed approaches are able to efficiently illustrate good segmentation results.
...
...

References

SHOWING 1-10 OF 41 REFERENCES
Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability
TLDR
Evidence is provided that texture features of MRI-detectable brain white matter lesions may have an additional potential role in the clinical evaluation of MR images in MS.
Quantitative analysis of brain white matter lesions in multiple sclerosis subjects
TLDR
The results showed that there was no significant difference for most of the shape features and for all of the texture features between MS lesions at 0 and 6–12 months and for some texture features there was significant difference between normal or normal appearing tissue and MS lesions.
Quantitative analysis of brain white matter lesions in multiple sclerosis subjects: Preliminary findings
TLDR
Shape and texture analysis was carried out in normal and diseased lesions in transverse sections of T2-weighted magnetic resonance (MR) images acquired from 10 symptomatic untreated subjects with clinically isolated syndrome scanned twice, with an interval of 6-12 months.
Texture analysis of multiple sclerosis: a comparative study.
Feature Reduction and Texture Classification in MRI-Texture Analysis of Multiple Sclerosis
  • Jing Zhang, Lei Wang, Longzheng Tong
  • Medicine
    2007 IEEE/ICME International Conference on Complex Medical Engineering
  • 2007
TLDR
It is demonstrated that MRI texture analysis can achieve high classification accuracy in tissue discrimination between MS lesions and NAWM or NWM, which is valuable in supporting early diagnosis of MS.
The contribution of magnetic resonance imaging to the diagnosis of multiple sclerosis
TLDR
Following a review of the typical appearance and pattern of MS lesions including differential diagnostic considerations, it is suggested economic MRI examination protocols for the brain and spine to help optimize and standardize the use of MRI in the diagnosis of MS.
Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images
TLDR
Evidence is provided that AM-FM features may have a potential role as surrogate markers of lesion load in MS and provide complementary information to classical texture analysis features like the gray-scale median, contrast, and coarseness.
...
...