Open Access Open Access  Restricted Access Subscription or Fee Access

An MRI Based Algorithm for Detecting Multiple Sclerosis

(*) Corresponding author

Authors' affiliations



Early diagnosis of brain disorders can significantly reduce the devastating consequences of these disorders. Physician’s diagnostic capabilities and diagnosis time can be improved using computerized diagnosis techniques. Magnetic Resonance (MR) images are used for diagnosing multiple sclerosis, which is a disease that occurs when the immune system eats away at the protective covering of nerves. MR images segmentation is a complicated task due to the variability in the lesion’s shape, location and patients’ anatomy. This study proposes a new computerized diagnosis technique for detecting brain disorders based on features extracted from MR images. Data of 121 cases were used, including healthy and patients with brain disorders. The cases were classified into normal and abnormal, with abnormal representing brain disorders cases. The abnormal cases were fed into a classifier to identify brain disorders. Classification accuracies in the two stages were 82.7% and 70%, respectively; indicating a significant improvement over methods found in literature. The automated structure of the proposed algorithm is suitable for use in hospitals at low cost.
Copyright © 2022 Praise Worthy Prize - All rights reserved.


Diagnosis; Multiple Sclerosis; Magnetic Resonance Imaging (MRI); Jordan

Full Text:



T. Lewis, Human Brain: Facts, Functions & Anatomy, 2016. [Online, Accessed 24 May 2018].

S. Arachchige, R. Burch V, H. Chander, A. Turner and K. A.C., The use of wearable devices in cognitive fatigue: current trends and future intentions, Theoretical Issues in Ergonomics Science, vol. On line, 2021.

T. Newman, All about the central nervous system, 2017. [Online, Accessed 24 May 2018]. Available:

R. Taiar, C. Adel, G. Belassian, D. Lamare, R. J. DumontGRESPI and A. Chené, Can a new ergonomical ankle-foot orthosis (AFO) device improve patients' daily life? A preliminary study, Theoretical Issues in Ergonomics Science , vol. 20, no. 6, pp. 763-772, 2019.

V. Rawat and Suryakant, A classification system for diabetic patients with machine learning techniques, International Journal of Mathematical, Engineering and Management Sciences, vol. 4, no. 3, p. 729-744, 2019.

Prabir Mukhopadhyay & Durwesh Jhodkar (2020) Tire Tread Removing Units in Central India: Risk Factors and Potential Interventions, IISE Transactions on Occupational Ergonomics and Human Factors, 8:4, 204-208.

P. Wildner, M. Stasiołek and M. Matysiak, Differential diagnosis of multiple sclerosis and other inflammatory CNS diseases, Multiple Sclerosis and Related Disorders, 2020.

M. Hasty, Early detection is the key to successful treatment, The Journal of Cardiovascular Management : The Official Journal of the American College of Cardiovascular Administrators, vol. 6, no. 4, pp. 24-25, 1995.

Bataineh, A., Batayneh, W., Harahsheh, T., Hijazi, K., Alrayes, A., Olimat, M., Bataineh, A., Early Detection of Cardiac Diseases from Electrocardiogram Using Artificial Intelligence Techniques, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 128-136.

N. Nabizadeh and M. Kubat, Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features, Computers & Electrical Engineering, vol. 45, pp. 286-301, 2015.

A. Rae-Grant, R. Fox and F. Béthoux, Multiple sclerosis and related disorders, 1st ed., New York, NY: Demos Medical Publishing, 2013.

A. Kavaliunas, A. Manouchehrinia, L. Stawiarz, R. Ramanujam, J. Agholme, A. Hedström and O. G. A. H. J. Beiki, Importance of early treatment initiation in the clinical course of multiple sclerosis, Multiple Sclerosis Journal, vol. 23, no. 9, pp. 1233-1240, 2017.

S. Thebault, G. Bose, R. Booth and M. Freedman, Serum neurofilament light in MS: The first true blood-based biomarker?, Multiple Sclerosis Journal, pp. 1-7, 2021.

M. Friese and L. Fugger, Autoreactive CD8+ T cells in multiple sclerosis: a new target for therapy?, Brain, vol. 128, no. 8, pp. 1747-63, 2005.

M. Sospedra and R. Martin, Immunology of multiple sclerosis, Annual Review of Immunology , vol. 23, pp. 683-747, 2005.

M. Pender and J. Greer, Immunology of multiple sclerosis, Current Allergy and Asthma Reports, vol. 7, no. 4, pp. 285-292, 2007.

J. Noseworthy, Progress in determining the causes and treatment of multiple sclerosis, Nature, vol. 399, pp. A40-A47, 1999.

M. Tintore, À. Rovira, J. Río, S. Otero-Romero, G. Arrambide, C. Tur, M. Comabella, N. C. M. Arévalo, N. L. I. Galán, A. Vidal-Jordana, J. Castilló, F. Palavra, E. Simon, R. Mitjana, C. Auger, J. Sastre-Garriga and X. Montalban, Defining high, medium and low impact prognostic factors for developing multiple sclerosis, Brain, vol. 138, no. Pt 7, pp. 1863-1874, 2015.

M. Filippi, M. Rocca, F. Barkhof, BrückW., J. Chen, G. Comi, G. DeLuca, N. Stefano, B. Erickson, N. Evangelou, F. Fazekas, J. L. Geurts, D. Miller, D. Pelletier, B. Popescu and H. Lassmann, Association between pathological and MRI findings in multiple sclerosis, Lancet Lassmann, vol. 11, no. 4, pp. 349-360, 2012.

M. Tintoré, A. Rovira, J. Río, C. Nos, E. Grivé, N. Téllez, R. Pelayo, M. Comabella, J. Sastre-Garriga and X. Montalban, Baseline MRI predicts future attacks and disability in clinically isolated syndromes., Neurology, vol. 67, no. 6, pp. 968-972, 2006.

P. Kempe, M. Hammar and J. Brynhildsen, Symptoms of multiple sclerosis during use of combined hormonal contraception, European Journal of Obstetrics & Gynecology and Reproductive Biology, vol. 193, pp. 1-4, 2015.

J. Chan, Early diagnosis, monitoring, and treatment of optic neuritis, Neurologist, vol. 18, no. 1, pp. 23-31, 2012.

R. Sakai, D. Feller, K. Galetta, S. Galetta and L. Balcer, Vision in multiple sclerosis: the story, structure-function correlations, and models for neuroprotection, Journal of Neuro-Ophthalmology, vol. 31, no. 4, pp. 362-373, 2011.

L. Stone, "Symptomps and Signs of Multiple Sclerosis, in Multiple Sclerosis and Related Disorders: Diagnosis, Medical Management, and Rehabilitation, B. Barry, Ed., New York, NY, Demos Medical Publishing , 2013, pp. 42-48.

A. Rae-Grant, N. Eckert, S. Bartz and J. Reed, Sensory symptoms of multiple sclerosis: a hidden reservoir of morbidity, Multiple Sclerosis Journal, vol. 5, no. 3, pp. 179-83, 1999.

A. Osterberg and J. Boivie, Central pain in multiple sclerosis - sensory abnormalities, European Journal of Pain, vol. 14, no. 1, pp. 104-10, 2010.

E. A. EL-Dahshan, H. M. Mohsen, K. Revett and S. A.M., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm, Expert Systems with Applications, vol. 41, no. 11, p. 5526-5545, 2014.

O. Qasim and Z. Algamal, Feature selection using different Ttansfer functions for binary bat algorithm, International Journal of Mathematical, Engineering and Management Sciences, vol. 5, no. 4, pp. 697-706, 2020.

H. Banaee, M. Ahmed and A. Loutfi, Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges, Sensors, vol. 13, pp. 17472-17500, 2013.

B. Patel, S. Prajapati and K. Lakhtaria, Efficient Classification of Data Using Decision Tree, Bonfring International Journal of Data Mining, vol. 2, no. 1, 2012.

V. Podgorelec, P. Kokol, B. Stiglic and I. Rozman, Decision trees: An overview and their use in medicine, Journal of Medical Systems, vol. 26, no. 5, pp. 445-463, 2002.

T. R Core, R: A language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical Computing, 2017.

M. Jenkinson, C. Beckmann, T. Behrens, M. Woolrich and S. Smith, FSL, NeuroImage, pp. 782-790, 2012.

S. Smith, Fast robust automated brain extraction, Human Brain Mapping, vol. 17, no. 3, p. 143-155, 2002.

Z. Ji, Q. Sun, Y. Xia, Q. Chen, D. Xia and D. Feng, Generalized rough fuzzy c-means algorithm for brain MR image segmentation, Computer Methods and Programs in Biomedicine, vol. 108, no. 2, p. 644-655, 2012.

J. Sled, A. Zijdenbos and A. Evans, A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Transactions on Medical Imaging, vol. 17, no. 1, pp. 87-97, 1998.

N. Tustison, B. Avants, P. Cook, Y. Zheng, A. Egan, P. Yushkevich and J. Gee, N4ITK: Improved N3 bias correction, IEEE Transactions on Medical Imaging, vol. 29, no. 6, p. 1310-1320, 2010.

X. Sun, L. Shi, Y. Luo, W. Yang, H. Li, P. Liang, K. Li, V. C. Mok, W. C. Chu and D. Wang, Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions, BioMedical Engineering OnLine, vol. 14, no. 1, 2015.

M. Wyawahare, P. Patil and H. Abhyankar, Image registration techniques: An overview, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 2, no. 3, pp. 11-28, 2009.

S. Roy, D. Bhattacharyya, S. K. Bandyopadhyay and T. Kim, An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI, Computer Methods and Programs in Biomedicine, vol. 140, pp. 307-320, 2017.


Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize