Abstract In this paper, we will describe a method that distinguishes the ischemic stroke from Computed Tomography (CT) brain images by extracting the statistical and textural features. First, preprocessing of the CT images is done followed by image enhancement. Segmentation of the CT images is performed by Marker Controlled Watershed. After the segmentation, we get the Grey Level Co-occurrence matrix (GLCM) and extract the textural and statistical features. The disadvantage of watershed is the over-segmentation caused by noise and solved by Marker Controlled Watershed as shown experimentally. The features extracted are contrast, correlation, standard deviation, variance, homogeneity, energy and mean. We noticed in our results that the values of homogeneity, energy and mean are bigger in normal CT images than in abnormal CT images where the contrast, correlation, standard deviation and variance of normal CT images are less than those of abnormal CT images (Ischemic Stroke).

References

[1] Ali A., Abdulsalam S., and Nema I., “Detection and Segmentation of Ischemic Stroke Using Textural Analysis on Brain CT Images,” International Journal of Scientific and Engineering Research, vol. 6, no. 2, pp. 396-400, 2015.

[2] De Carvalho I., Silva C., De Freitas D., Ambrosano G., Bóscolo F., and Almeida S., “Bone Density: Comparative Evaluation of Hounsfield Units In Multislice And Cone-Beam Computed Tomography,” Brazilian Oral Research, vol. 26 no. 6, pp. 550-556, 2012.

[3] Gupta V., Ambrosius W., Qian G., Blazejewska A., Kazmierski R., Urbanik A, and Nowinski W., “Automatic Segmentation of Cerebrospinal Fluid, White and Gray Matter in Unenhanced Computed Tomography Images,” Academic Radiology, vol. 17, no. 11, pp. 1350-1358, 2010.

[4] Gonzalez R., Woods R., Eddins S., “Morphological Reconstruction from Digital Image Processing Using Matlab,” MatlabDigest,https://ww2.mathworks.cn/tagteam /64199_91822v00_eddins_final.pdf, Last Visited, 2019.

[5] Lucas C., Kemmling A., Mamlouk A., and Heinrich M., “Multi Scale Neural Network for Automatic Segmentation of Ischemic Stroke on Acute Perfusion,” in Proceedings of 15th International Symposium on Biomedical Imaging, Washington, pp. 1118-1121, 2018.

[6] Meyer F., “The Watershed Concept and Its Use in Segmentation: A Briefhistory,” https://www.researchgate.net/publication/221661 175_The_watershed_concept_and_its_use_in_se gmentation_a_brief_history, Last Visited, 2019.

[7] Mathworks,https://www.mathworks.com/help/im ages/texture-analysis-using-the-gray- levelcooccurrence-matrix-glcm.html, Last Visited, 2019.

[8] Peter R., Korfiatis P., Blezek D., Beitia A., Buksakowska I., Horinek D., Flemming K., and Erickson B., “A Quantitative Symmetry-Based Analysis of Hyperacute Ischemic Stroke Lesions in Non-Contrast Computed Tomography,” Medical Physics, vol. 44, no. 1, 2017.

[9] Radiopaedia,https://radiopaedia.org/articles/ischa emic-stroke, Last Visited, 2019.

[10] Stroke Association, http://www.strokeassociation.org/STROKEORG/ AboutStroke/Impact-of-Stroke-Stroke- statistics_UCM_310728_Article.jsp#.W1DWGW xNCZ8, Last Visited, 2019.

[11] StrokeAssociation,Internet:http://www.strokeasso ciation.org/STROKEORG/AboutStroke/Impact- of-Stroke-Stroke- statistics_UCM_310728_Article.jsp#.W1DWGW xNCZ8, Last Visited, 2019.

[12] Shapiro L., Stockman G., “Computer Vision,” 2010.

[13] Sharma P., Singh G., and Kaur A., “Different Techniques of Edge Detection in Digital Image Processing,” International Journal of Engineering Research and Applications, vol. 3, no. 3, pp. 458- 461, 2013. 676 The International Arab Journal of Information Technology, Vol. 17, No. 4A, Special Issue 2020

[14] Shih F., Image Processing and Pattern Recognition: Fundamentals and Techniques, Wiley, 2010.

[15] Tyan Y., Wu M., Chin C., Kuo Y., Lee M., and Chang H., “Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method,” International Journal of Biomedical Imaging, vol. 2014, pp. 12, 2014.

[16] Umbaugh S., Digital Image Processing and Analysis Human and Computer Vision applications with CVIPtools, CRC Press, 2010.

[17] World Health Organization, Internet: http://www.who.int/topics/cerebrovascular_accid ent/en/, Last Visited, 2019.

[18] Yahiaoui A. and Bessaid A., “Segmentation of Ischemic Stroke Area from CT Brain Images,” International Symposium on Signal, Image, Video and Communications, Tunisia, 2016. Mohammed Ajam is an experienced 39 years old biomedical engineer born in Tripoli, Lebanon and holds a master degree of biomedical engineering from Islamic University of Lebanon in 2019. Mohammed has three publications mainly in neuro imaging and oncology. Mohammed works as a regional director of Scientech Intervention Company and has an experience more than 10 years in management of sales, marketing, clinical and research of Interventional Neuro and Peripheral medical products in the Middle East. Hussein Kanaan He received the B.S. degree in biomedical engineering in 2008. In 2012, he received the M.S. degree in communication engineering from Shahed University, Tehran, Iran. He received Ph.D. degree in electronic engineering from, Shahed University, Tehran, Iran in 2017. Currently, he is an associate professor of Biomedical Engineering Department, Islamic University, Lebanon. His research fields are image processing, signal processing, data mining and machine learning. Lina El Khansa obtained a bachelor degree of engineering in biomedical from the Islamic University of Lebanon in 2003. She received the DEA in signals and images from the University of Paris XII, France in 2004. She received the Ph.D. degree in signal processing from the University of Paris EST, France in 2009. She is currently the head of biomedical department at the faculty of engineering at the Islamic university of Lebanon. Her research interests include signal and image processing techniques. Mohammed Ayache obtained a bachelor degree of engineering in biomedical from the Islamic University of Lebanon. He received the DEA in Signals and Images in biology and medicine from the University of Angers, France in 2004. He received the Ph.D. degree in medical Image Processing from the University of Tours, France, in 2007. He was the head of department of biomedical at the faculty of engineering at the Islamic University of Lebanon from 2009 to 2017. He was also vice dean of the faculty of engineering from 2014 to 2017. Currently, he is the head of graduate studies at the faculty of engineering. His research interests include advanced neural networks software development and advanced signal and image processing techniques.