(School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)(2) Juhaida Abu Bakar
(School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)(3) Hamirulaini’ Hambali
(School of Computing, College of Arts and Science, Universiti Utara Malaysia, Malaysia)(4) Nurnadia Mohd Khair
(Faculty of Engineering Technology, Universiti Malaysia Perlis, Malaysia)(5) Mohd. Yusoff Mashor
(School of Mechatronics Engineering, Universiti Malaysia Perlis, Malaysia)(6) Roseline Hassan
(Department of Hematology, Universiti Sains Malaysia, Malaysia)*corresponding author
AbstractSegmentation is the major area of interest in the field of image processing stage. In an automatic diagnosis of acute leukemia disease, the crucial process is to achieve the accurate segmentation of acute leukemia blood image. Generally, there are three requirements of image segmentation for medical purposes, namely; accuracy, robustness and effectiveness which have received considerable critical attention. As such, we propose a new (modified) dark contrast enhancement technique to enhance and automatically segment the acute leukemic cells. Subsequently, we used a fusion 7 × 7 median filter as well as the seeded region growing area extraction (SRGAE) algorithm to minimise the salt-and-pepper noise, apart from preserving the post-segmentation edge. As per the outcomes, the accuracy, sensitivity, and specificity of this method were 91.02%, 83.68%, and 91.57% respectively.
KeywordsAcute leukemia; Median filter; Seeded region growing area; Extraction; Modified dark contrast enhancement
|
DOIhttps://doi.org/10.26555/ijain.v4i3.276 |
Article metricsAbstract views : 16223 | PDF views : 253 |
Cite |
Full Text Download
|
References
[1] N. H. Harun, M. Y. Mashor, and R. Hassan, “Automated blasts segmentation techniques based on clustering algorithm for acute leukaemia blood samples,” J. Adv. Comput. Sci. Technol. Res., vol. 1, pp. 96–109, 2011, available at : http://www.sign-ific-ance.co.uk/index.php/JACSTR/article/view/375.
[2] S. Mishra, B. Majhi, and P. K. Sa, “A survey on automated diagnosis on the detection of Leukemia: A hematological disorder,” in Recent Advances in Information Technology (RAIT), 2016 3rd International Conference on, 2016, pp. 460–466, doi: https://doi.org/10.1109/RAIT.2016.7507945.
[3] H. G. Kaganami and Z. Beiji, “Region-Based Segmentation versus Edge Detection,” in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp. 1217–1221, doi: https://doi.org/10.1109/IIH-MSP.2009.13.
[4] I. Karoui, R. Fablet, J. M. Boucher, and J. M. Augustin, “Unsupervised region-based image segmentation using texture statistics and level-set methods,” in 2007 IEEE International Symposium on Intelligent Signal Processing, 2007, pp. 1–5, doi: https://doi.org/10.1109/WISP.2007.4447617.
[5] Y. Zhou, S. Jiang, and M. Yin, “A Region-Based Image Segmentation Method with Mean-Shift Clustering Algorithm,” in 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, pp. 366–370, doi: https://doi.org/10.1109/FSKD.2008.363.
[6] A. M. Mendonca and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imaging, vol. 25, no. 9, pp. 1200–1213, Sep. 2006, doi: https://doi.org/10.1109/TMI.2006.879955.
[7] J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Trans. Med. Imaging, vol. 25, no. 9, pp. 1214–1222, Sep. 2006, doi: https://doi.org/10.1109/TMI.2006.879967.
[8] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-Based Vessel Segmentation in Color Images of the Retina,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501–509, Apr. 2004, doi: https://doi.org/10.1109/TMI.2004.825627.
[9] S. Lakshmi, D. V Sankaranarayanan, and others, “A study of edge detection techniques for segmentation computing approaches,” IJCA Spec. Issue “Computer Aided Soft Comput. Tech. Imaging Biomed. Appl. CASCT, pp. 35–40, 2010, available at : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.206.3030&rep=rep1&type=pdf.
[10] M. Sharif, S. Mohsin, M. Y. Javed, and M. A. Ali, “Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms.,” Int. Arab J. Inf. Technol., vol. 9, no. 6, pp. 562–570, 2012, available at : https://pdfs.semanticscholar.org/03df/e517766a2cb914070c438a9396fb556158e8.pdf.
[11] M. Sharif, M. A. Ali, M. Raza, and S. Mohsin, “Face recognition using edge information and DCT,” Sindh Univ. Res. Journal-SURJ (Science Ser., vol. 43, no. 2, 2015, available at : Google Scholar.
[12] S. Yuheng and Y. Hao, “Image Segmentation Algorithms Overview,” arXiv Prepr. arXiv1707.02051, 2017, available at : https://arxiv.org/abs/1707.02051.
[13] S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, “Pixel-level image fusion: A survey of the state of the art,” Inf. Fusion, vol. 33, pp. 100–112, Jan. 2017, doi: https://doi.org/10.1016/j.inffus.2016.05.004.
[14] N. Dhanachandra, K. Manglem, and Y. J. Chanu, “Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm,” Procedia Comput. Sci., vol. 54, pp. 764–771, 2015, doi: https://doi.org/10.1016/j.procs.2015.06.090.
[15] N. M. Zaitoun and M. J. Aqel, “Survey on Image Segmentation Techniques,” Procedia Comput. Sci., vol. 65, pp. 797–806, 2015, doi: https://doi.org/10.1016/j.procs.2015.09.027.
[16] Hongmin Cai, Zhong Yang, Xinhua Cao, Weiming Xia, and Xiaoyin Xu, “A New Iterative Triclass Thresholding Technique in Image Segmentation,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1038–1046, Mar. 2014, doi: https://doi.org/10.1109/TIP.2014.2298981.
[17] N. Senthilkumaran and S. Vaithegi, “Image segmentation by using thresholding techniques for medical images,” Comput. Sci. Eng. An Int. J., vol. 6, no. 1, pp. 1–13, 2016, available at : Google Scholar.
[18] S. Zhou, J. Wang, S. Zhang, Y. Liang, and Y. Gong, “Active contour model based on local and global intensity information for medical image segmentation,” Neurocomputing, vol. 186, pp. 107–118, Apr. 2016, doi: https://doi.org/10.1016/j.neucom.2015.12.073.
[19] P. Ghosh, M. Mitchell, J. A. Tanyi, and A. Y. Hung, “Incorporating priors for medical image segmentation using a genetic algorithm,” Neurocomputing, vol. 195, pp. 181–194, Jun. 2016, doi: https://doi.org/10.1016/j.neucom.2015.09.123.
[20] H. Gao, L. Dou, W. Chen, and G. Xie, “The applications of image segmentation techniques in medical CT images,” in Proceedings of the 30th Chinese Control Conference, 2011, pp. 3296–3299, available at: https://ieeexplore.ieee.org/document/6001546.
[21] B. M. Ghafour, N. H. Salman, and G. M. Hadi, “Medical Image Segmentation Based on Edge Detection Techniques,” J. Adv. Comput. Sci. Technol. Res., vol. 5, no. 1, pp. 16–27, 2015, available at : http://www.sign-ific-ance.co.uk/index.php/JACSTR/article/download/1042/1009.
[22] A. N. A. Salihah, M. Y. Mashor, N. H. Harun, A. A. Abdullah, and H. Rosline, “Improving colour image segmentation on acute myelogenous leukaemia images using contrast enhancement techniques,” in Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on, 2010, pp. 246–251, doi: https://doi.org/10.1109/IECBES.2010.5742237.
[23] N. R. Mokhtar et al., “Image Enhancement Techniques Using Local, Global, Bright, Dark and Partial Contrast Stretching For Acute Leukemia Images,” in Proceedings of the World Congress on Engineering 2009, 2009, vol. 1, available at : http://www.iaeng.org/publication/WCE2009/WCE2009_pp807-812.pdf.
[24] E. U. Francis, M. Y. Mashor, A. A. Azamimi, and H. Roseline, “Image enhancement technique using partial contrast stretching on abnormal Bone Marrow images for Leukemia screening,” in International Postgraduate Conference On Engineering (IPCE 2010), 2010, available at : http://dspace.unimap.edu.my/xmlui/handle/123456789/21637.
[25] M. Zhou, K. Jin, S. Wang, J. Ye, and D. Qian, “Color retinal image enhancement based on luminosity and contrast adjustment,” IEEE Trans. Biomed. Eng., vol. 65, no. 3, pp. 521–527, 2018, doi: https://doi.org/10.1109/TBME.2017.2700627.
[26] N. J. Willett, T. Thote, M. Hart, S. Moran, R. E. Guldberg, and R. V. Kamath, “Quantitative pre-clinical screening of therapeutics for joint diseases using contrast enhanced micro-computed tomography,” Osteoarthr. Cartil., vol. 24, no. 9, pp. 1604–1612, Sep. 2016, doi: https://doi.org/10.1016/j.joca.2016.04.021.
[27] J. Khan, A. S. Malik, N. Kamel, S. C. Dass, and A. M. Affandi, “Contrast Enhancement of Color Images with Color Morphing Approach,” World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng., vol. 10, no. 2, pp. 437–440, 2017, available at: https://waset.org/publications/10006937/contrast-enhancement-of-color-images-with-color-morphing-approach.
[28] E. Fröhlich, R. Muller, X.-W. Cui, D. Schreiber-Dietrich, and C. F. Dietrich, “Dynamic Contrast-Enhanced Ultrasound for Quantification of Tissue Perfusion,” J. Ultrasound Med., vol. 34, no. 2, pp. 179–196, 2015, doi: https://doi.org/10.7863/ultra.34.2.179.
[29] A. R. Weeks, Fundamentals of electronic image processing. SPIE Optical Engineering Press Bellingham, 1996, available at: Google Scholar.
[30] N. H. Harun, M. Y. Mashor, L. H. Nee, and R. Hassan, “Automated white blood cells counting system for acute leukemia based on blood images,” J. Teknol., vol. 78, no. 6--4, pp. 91–98, 2016, doi: https://doi.org/10.11113/jt.v78.8982.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0