PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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