PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

• Afruz, J., Wilson, V., & Umbaugh, S. E. (2010). Frequency domain pseudo-colour to enhance ultrasound images. Computer and Information Science, 3(4), 24-34.

• Chen, H. C., & Wang, S. J. (2004). The use of visible color difference in the quantitative evaluation of color image segmentation. In 2004 IEEE International Conference on Acoustics, Speech and Signal Processing (Vol. 3, pp. 3-593). IEEE Conference Publication. https: //doi.org/10.1109/ICASSP.2004.1326614

• Dhankar, S., Tyagi, S., & Prasad, T. V. (2010). Brain MRI segmentation using K-means algorithm. In National Conference on Advances in Knowledge Management, NCAKM 2010 (pp. 1-5). Lingaya’s University. https: //doi.org/10.13140/RG.2.1.4979.0567

• Gautam, A., & Raman, B. (2019). Segmentation of ischemic stroke lesion from 3D MR images using random forest. Multimedia Tools and Applications, 78(6), 6559 - 6579. https: //doi.org/10.1007/s11042-018-6418-2

• Gonzalez, R., & Schwamm, L. (2016). Imaging acute stroke. In Handbook of clinical neurology, (pp 293-315). Elsevier’s ScienceDirect. https: //doi.org/10.1016/B978-0-444-53485-9.00016-7

• Graves, M. J., & Mitchell, D. G. (2013). Body MRI artifacts in clinical practice: A physicist’s and radiologits’ perspective. Journal Magnetic Resonance Imaging, 38(2), 269-287. https: //doi.org/10.1002/jmri.24288

• Jinlong, H. U., Xianrong, P., & Zhiyong, X. U. (2012). Study of grey image pseudo-colour processing algorithms. In International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes. (Vol. 8415, p. 841519). International Society for Optics and Photonics. https: //doi.org/10.1117/12.977197

• Kalavathi, P., & Prasath, V. B. S. (2016). Methods on skull stripping of MRI head scan images - A review. Journal of Digital Imaging, 29(3), 365-379. https: //doi.org/10.1007/s10278-015-9847-8

• Khalil, Y. A., & Ali, P. J. M. (2013). A proposed method for colorizing grayscale images. International Journal of Computer Science and Engineering, 2(2), 109-114.

• Kipli, K., & Kouzani, A. Z. (2015). Degree of contribution (DoC) feature selection for structural brain MRI volumetric features in depression detection. International Journal of Computer Assisted Radiology and Surgery, 10(7), 1003-1016. https: //doi.org/10.1007/s11548-014-1130-9

• Krupa, K., & Bekiesińska-Figatowska, M. (2015). Artifacts in magnetic resonance imaging. Polish Journal of Radiology, 80, 93-106. https: //doi.org/10.12659/PJR.892628

• Li, C., Gore, J. C., & Davatzikos, C. (2014). Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging, 32(7), 913-923. https: //doi.org/10.1016/j.mri.2014.03.010

• Li, H., Chen, C., Feng, S., & Zhao, S. (2017). Brain MR image segmentation using NAMS in pseudo-colour. Computer Assisted Surgery, 22(0), 170-175. https: //doi.org/10.1080/24699322.2017.1389395

• Liew, S. L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., & Stroud, A. (2018). A large, open-source dataset of stroke anatomical brain images and manual lesion segmentations. Scientific data, 5(1), 1-11. https: //doi.org/10.1038/sdata.2018.11

• Merino, J. G., & Warach, S. (2010). Imaging of acute stroke. Nature Reviews Neurology, 6(10), 560-571. https: //doi.org/10.1038/nrneurol.2010.129

• Muda, A. F., Saad, N. M., Abu Bakar, S. A. R., Muda, S., & Abdullah, A. R. (2017, March 15-17). Automated stroke lesion detection and diagnosis system. In Proceedings of the International MultiConference of Engineers and Computer Scientists. Hong Kong.

• Nag, M. K., Koley, S., China, D., Sadhu, A. K., Balaji, R., Ghosh, S., & Chakraborty, C. (2017). Computer-assisted delineation of cerebral infarct from diffusion-weighted MRI using Gaussian mixture model. International Journal of Computer Assisted Radiology and Surgery, 12(4), 539-552. https: //doi.org/10.1007/s11548-017-1520-x

• Nitta, G. R., Sravani, T., Nitta, S., & Muthu, B. (2020). Dominant gray level based K-means algorithm for MRI images. Health and Technology, 10, 281-287. https: //doi.org/10.1007/s12553-018-00293-1

• Purushotham, A., Campbell B. C. V., Straka, M., Mlynash, M., Olivo, J., Bammer, R., Kemp, S. M., Albers, G. W., & Lansberg M. G. (2015). Apparent diffusion coefficient threshold for delineation of ischemic core. International Journal of Stroke, 10(3), 348-353. https: //doi.org/10.1111/ijs.12068

• Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3d medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15(29), 1-28. https: //doi.org/10.1186/s12880-015-0068-x

• Tyan, Y. S., Wu, M. C., Chin, C. L., Kuo, Y. L., Lee, M. S., & Chang, H. Y. (2014). Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method. International Journal of Biomedical Imaging, 2014, 1- 24. https: //doi.org/10.1155/2014/947539

• Vupputuri, A., Ashwal, S., Tsao, B., Haddad, E., & Ghosh, N. (2017). MRI based objective ischemic core-penumbra quantification in adult clinical stroke. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3012-3015). IEEE Conference Publication. https: //doi.org/10.1109/EMBC.2017.8037491

• Zou, K. H., Warfield, S. K., Bharatha, A., Tempany, C. M., Kaus, M. R., Haker, S. J., Wells, W. M., Jolesz, F. A., & Kikinis, R. (2004). Statistical validation of image segmentation quality based on a spatial overlap index. Academic Radiology, 11(2), 178-189. https: //doi.org/10.1016/S1076-6332(03)00671-8