A Review on Image Segmentation Techniques for MRI Brain Stroke Lesion


  • Norhashimah Mohd Saad Universiti Teknikal Malaysia Melaka
  • Nor Shahirah Mohd Noor
  • Abdul Rahim Abdullah


Magnetic Resonance Imaging, Segmentation, Stroke


One of the major categories of brain disorders is known as stroke, which it can affect the entire body. The ability to recover from a stroke depends on the severity of the stroke and how quickly the patients receive the medical treatment. Conventionally, the diagnosis of brain stroke is performed manually by professional neuroradiologists during a highly subjective and time-consuming process. This paper reviewed the techniques for automatic magnetic resonance imaging of brain lesions segmentation. The proposed review is important to identify more robust and accurate technique in segmenting the brain stroke lesion for computer-aided diagnosis. This could be an opportunity for the medical and engineering to collaborate in designing a complete end-to-end automated framework in detecting and segmenting stroke lesions. 


C. P. D. Benjamin Wedro, “Stroke: FAST, Symptoms, Causes, Types, Treatment, Prevention,” Nov. 07, 2019. https://www.medicinenet.com/stroke_symptoms_and_treatment/article.htm.

S. L. Liew et al., “A large, open source dataset of stroke anatomical brain images and manual lesion segmentations,” Sci. Data, vol. 5, p. 180011, 2018.

K. W. Loo and S. H. Gan, “Burden of stroke in Malaysia,” Int. J. Stroke, vol. 7, no. 2, pp. 165–167, Feb. 2012. doi: 10.1111/j.1747-4949.2011.00767.x. PMID: 22264370.

M. H. Jali et al., “Joint torque estimation model of sEMG signal for arm rehabilitation device using artificial neural network techniques,” in Lecture Notes in Electrical Engineering, 2015, vol. 315, pp. 671–682.

D. Smajlović, “Strokes in young adults: Epidemiology and prevention,” Vasc. Health Risk Manag., vol. 11, pp. 157–164, Feb. 2015.

J. Peter and A. Hans Justus, “Knowledge and Practices of Stroke Survivors Regarding Secondary Stroke Prevention , Khomas Region , Namibia,” J. Med. Biomed. Appl. Sci., pp. 1–13, 2016.

N. M. Saad, N. S. M. Noor, A. R. Abdullah, S. Muda, A. F. Muda, and H. Musa, “Segmentation and classification analysis techniques for stroke based on diffusion weighted images,” IAENG Int. J. Comput. Sci., vol. 44, no. 3, pp. 388–395, Aug. 2017.

O. Maier, M. Wilms, J. von der Gablentz, U. Krämer, and H. Handels, “Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers,” in Medical Imaging 2014: Computer-Aided Diagnosis, Mar. 2014, vol. 9035, p. 903504.

A. Subudhi, S. S. Jena, and S. Sabut, “Automated Detection of Brain Stroke in MRI with Hybrid Fuzzy C-Means Clustering and Random Forest Classifier,” Int. J. Comput. Intell. Appl., vol. 18, no. 3, 2019.

NAGENTHIRAJA KARTHEEBAN, MOURIDSEN KIM, and RIBE LARS RIISGAARD, “Method for delineation of tissue lesions - Dimensions,” Information and Computing Sciences & Artificial Intelligence and Image Processing, 2017. https://app.dimensions.ai/details/patent/AU-2011344876-B2 (accessed Jun. 12, 2021).

A. R. Mathew and P. B. Anto, “Tumor detection and classification of MRI brain image using wavelet transform and SVM,” in Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, Mar. 2018, vol. 2018-Jan, pp. 75–78.

B. Halalli and A. Makandar, “Computer Aided Diagnosis - Medical Image Analysis Techniques,” in Breast Imaging, InTech, 2018.

L. Alrabghi et al., “Stroke types and management,” Int. J. Community Med. Public Heal., vol. 5, no. 9, p. 3715, Aug. 2018.

J. Gomes and A. M. Wachsman, “Types of strokes,” in Handbook of Clinical Nutrition and Stroke, Humana Press Inc., 2013, pp. 15–31.

R. Kanchana and R. Menaka, “Computer reinforced analysis for ischemic stroke recognition: A review,” in Indian Journal of Science and Technology, vol. 8, no. 35, Indian Society for Education and Environment, 2015, pp. 1–9.

S. C. Larsson, A. Wallin, A. Wolk, and H. S. Markus, “Differing association of alcohol consumption with different stroke types: A systematic review and meta-analysis,” BMC Med., vol. 14, no. 1, p. 178, 2016.

D. D. T. Robert P. Lisak, International Neurology - Google Books. 2016.

U. K. Bodanapally, C. Sours, J. Zhuo, and K. Shanmuganathan, “Imaging of Traumatic Brain Injury,” Radiologic Clinics of North America, vol. 53, no. 4. W.B. Saunders, pp. 695–715, Jul. 01, 2015.

G. S. Chilla, C. H. Tan, C. Xu, and C. L. Poh, “Diffusion weighted magnetic resonance imaging and its recent trend-a survey.,” Quant. Imaging Med. Surg., vol. 5, no. 3, pp. 407–422, 2015.

E. O. Stejskal and J. E. Tanner, “Spin diffusion measurements: Spin echoes in the presence of a time-dependent field gradient,” J. Chem. Phys., vol. 42, no. 1, pp. 288–292, 1965.

H. Y. Wey, V. R. Desai, and T. Q. Duong, “A review of current imaging methods used in stroke research,” Neurological Research. pp. 1092–1102, 2013.

T. Domi et al., “The Potential for Advanced Magnetic Resonance Neuroimaging Techniques in Pediatric Stroke Research,” Pediatric Neurology, vol. 69. Elsevier Inc., pp. 24–36, Apr. 01, 2017.

C. S. Kidwell and M. Wintermark, “Imaging of intracranial haemorrhage,” Lancet Neurol., vol. 7, no. 3, pp. 256–267, Mar. 2008.

L. M. Allen, A. N. Hasso, J. Handwerker, and H. Farid, “Sequence-specific MR imaging findings that are useful in dating ischemic stroke,” Radiographics, vol. 32, no. 5, pp. 1285–1297, 2012.

A. Vural, R. Gocmen, K. K. Oguz, M. A. Topcuoglu, and E. M. Arsava, “Bright and dark vessels on stroke imaging: Different sides of the same coin?,” Diagnostic Interv. Radiol., vol. 22, no. 3, pp. 284–290, 2016.

M. G. Lansberg, M. Wintermark, C. S. Kidwell, S. Warach, and G. W. Albers, “Magnetic Resonance Imaging of Cerebrovascular Diseases,” in Stroke: Pathophysiology, Diagnosis, and Management, 2015, pp. 768–789.

V. Valentini, L. Boldrini, A. Damiani, and L. P. Muren, “Recommendations on how to establish evidence from auto-segmentation software in radiotherapy,” Radiother. Oncol., vol. 112, no. 3, pp. 317-320., 2014.

N. Wang, L. E. White, Y. Qi, G. Cofer, and G. A. Johnson, “Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging,” Neuroimage, vol. 216, p. 116876, Aug. 2020.

X. Lladó et al., “Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches,” Inf. Sci. (Ny)., vol. 186, no. 1, pp. 164–185, Mar. 2012.

B. a. Abdullah, “Segmentation of Multiple Sclerosis Lesions in Brain MRI,” Ph.D. Thesis, vol. 15, pp. 154–169, 2012.

A. Işin, C. Direkoǧlu, and M. Şah, “Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods,” in Procedia Computer Science, 2016, vol. 102, pp. 317–324.

Angulakshmi M and Deepa M, “A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation,” Curr. Med. Imaging Former. Curr. Med. Imaging Rev., vol. 17, no. 6, pp. 695–706, Jan. 2021.

C. S. Rao and K. Karunakara, “A comprehensive review on brain tumor segmentation and classification of MRI images,” Multimed. Tools Appl., vol. 80, no. 12, pp. 17611–17643, 2021.

M. Tabatabaei, Z. Khazaei, K. Tavakol, and A. Tavakol, “Machine and Deep Learning Methods Enable the Accurate and Efficient Segmentation, Grading, Diagnosis and Prognosis of Brain Tumors,” J. Radiol. Clin. Imaging, vol. 3, no. 2, 2020.

K. Venu, P. Natesan, N. Sasipriyaa, and S. Poorani, “Review on brain tumor segmentation methods using convolution neural network for MRI images,” in Proceedings of IEEE International Conference on Intelligent Computing and Communication for Smart World, I2C2SW 2018, 2018, pp. 291–295.

G. Khan and J. Howells, “A review of the current and future use of artificial intelligence (AI) in diagnostic radiology,” Clin. Radiol., vol. 75, no. e21, 2020.

C. Delon-Martin, J. Plailly, P. Fonlupt, A. Veyrac, and J. P. Royet, “Perfumers’ expertise induces structural reorganization in olfactory brain regions,” Neuroimage, vol. 68, pp. 55–62, 2013.

E. Maltbie et al., “Asymmetric bias in user guided segmentations of brain structures,” Neuroimage, vol. 59, no. 2, pp. 1315–1323, 2012.

A. Fedorov et al., “3D Slicer as an image computing platform for the Quantitative Imaging Network,” Magn. Reson. Imaging, vol. 30, no. 9, pp. 1323–1341, 2012.

N. Sharma et al., “Automated medical image segmentation techniques,” J. Med. Phys., vol. 35, no. 1, pp. 3–14, 2010.

A. Tiwari, S. Srivastava, and M. Pant, “Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,” Pattern Recognit. Lett., vol. 131, pp. 244–260, 2020.

J. Liu, M. Li, J. Wang, F. Wu, T. Liu, and Y. Pan, “A survey of MRI-based brain tumor segmentation methods,” Tsinghua Sci. Technol., vol. 19, no. 6, pp. 578–595, 2014.

P. A. Yushkevich et al., “User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP,” Neuroinformatics, vol. 17, no. 1, pp. 83–102, 2019.

J. E. Iglesias and M. R. Sabuncu, “Multi-atlas segmentation of biomedical images: A survey,” Med. Image Anal., vol. 24, no. 1, pp. 205–219, 2015.

A. De Brébisson and G. Montana, “Deep neural networks for anatomical brain segmentation,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 20–28.

G. C. S. Ruppert et al., “Medical image registration based on watershed transform from greyscale marker and multi-scale parameter search,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 5, no. 2, pp. 138–156, 2017.

M. Bai and R. Urtasun, “Deep watershed transform for instance segmentation,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, pp. 2858–2866.

S. Yadav and M. Biswas, “Improved color-based K-mean algorithm for clustering of satellite image,” in 2017 4th International Conference on Signal Processing and Integrated Networks, SPIN 2017, 2017, pp. 468–472.

S. K. Choy, S. Y. Lam, K. W. Yu, W. Y. Lee, and K. T. Leung, “Fuzzy model-based clustering and its application in image segmentation,” Pattern Recognit., vol. 68, pp. 141–157, 2017.

A. R. Jasmine Begum and T. Abdul Razak, “A proposed Hybrid Fuzzy C-means Algorithm with cluster center estimation for Leukemia Image Segmentation,” Int. J. Control Theory Appl., vol. 9, no. 26, pp. 335–342, 2016.

J. Fenshia Singh and V. Magudeeswaran, “A machine learning approach for brain image enhancement and segmentation,” Int. J. Imaging Syst. Technol., vol. 27, no. 4, pp. 311–316, 2017.

E. Abdel-Maksoud, M. Elmogy, and R. Al-Awadi, “Brain tumor segmentation based on a hybrid clustering technique,” Egypt. Informatics J., vol. 16, no. 1, pp. 71–81, 2015.

W. Zhang et al., “Deep convolutional neural networks for multi-modality isointense infant brain image segmentation,” Neuroimage, vol. 108, pp. 214–224, 2015.

T. Haeck, F. Maes, and P. Suetens, “ISLES challenge 2015: Automated model-based segmentation of ischemic stroke in MR images,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9556, pp. 246–253, 2016, doi: 10.1007/978-3-319-30858-6_21.

S. M. S. Reza, L. Pei, and K. M. Iftekharuddin, “Ischemic Stroke Lesion Segmentation Using Local Gradient and Texture Features,” Proc. MICCAI-ISLES 2015, pp. 13–16, 2015.

L. Chen, P. Bentley, and D. Rueckert, “A novel framework for sub-acute stroke lesion segmentation based on random forest,” Proc. MICCAI-ISLES 2015, pp. 13–16, 2015, Accessed: Jun. 15, 2021. [Online]. Available: http://www.isles-challenge.org/ISLES2015/pdf/20150930_ISLES2015_Proceedings.pdf#page=17.

S. R. Telrandhe, A. Pimpalkar, and A. Kendhe, “Implementation of Brain Tumor Detection using Segmentation Algorithm & SVM Image Classification View project Sentiment Analysis View project Swapnil R Telrandhe Implementation of Brain Tumor Detection using Segmentation Algorithm & SVM,” Int. J. Comput. Sci. Eng. , vol. 8, no. 7, pp. 278–284, 2016, Accessed: Jun. 15, 2021. [Online]. Available: https://www.researchgate.net/publication/328784370.

D. Robben et al., “A voxel-wise, cascaded classification approach to ischemic stroke lesion segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9556, pp. 254–265, Accessed: Jun. 15, 2021. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-30858-6_22.

S. Yahiya, A. Yousif, and M. Bakri, “Classification of Ischemic Stroke using Machine Learning Algorithms,” Int. J. Comput. Appl., vol. 149, no. 10, pp. 26–31, 2016.

B. Kiranmayee, “Effective Analysis of Brain Tumor Using Hybrid Data Mining Techniques,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 7, pp. 286–293, 2017.

M. Havaei et al., “Brain tumor segmentation with Deep Neural Networks,” Med. Image Anal., vol. 35, pp. 18–31, 2017.

A. Batra and G. Kaushik, “SECTUBIM : Automatic Segmentation And Classification of Tumeric Brain MRI Images using FHS ( FCM , HWT and SVM ),” Int. J. Eng. Sci. Comput., vol. 7, no. 6, pp. 13190–13194, 2017.

M. T. El-Melegy, K. M. A. El-Magd, S. A. Ali, K. F. Hussain, and Y. B. Mahdy, “A comparative study of classification methods for automatic multimodal brain tumor segmentation,” in Proceedings of 2018 International Conference on Innovative Trends in Computer Engineering, ITCE 2018, 2018, pp. 36–41.

A. Subudhi, S. Jena, and S. Sabut, “Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI,” Med. Biol. Eng. Comput., vol. 56, no. 5, pp. 795–807, 2018.

T. Ruba, R. Tamilselvi, M. Parisa Beham, and N. Aparna, “Accurate Classification and Detection of Brain Cancer Cells in MRI and CT Images using Nano Contrast Agents,” Biomed. Pharmacol. J., vol. 13, no. 3, pp. 1227–1237, 2020.

F. J. Díaz-Pernas, M. Martínez-Zarzuela, D. González-Ortega, and M. Antón-Rodríguez, “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,” Healthc., vol. 9, no. 2, p. 153, 2021.




How to Cite

Mohd Saad, N., Mohd Noor, N. S. ., & Abdullah, A. R. (2021). A Review on Image Segmentation Techniques for MRI Brain Stroke Lesion. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 13(4), 27–34. Retrieved from https://jtec.utem.edu.my/jtec/article/view/6145