Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences
Volume 42 No. 1, February 2018, Pages 1-15
Aimi Salihah Abdul Nasir1,2,*, Mohd Yusoff Mashor2, Zeehaida Mohamed3
1Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
2Electronic & Biomedical Intelligent Systems (EBItS) Research Group, School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
3Department of Microbiology & Parasitology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
*Corresponding author: firstname.lastname@example.org
Clustering, enhanced k-Means, image segmentation, Malaria
Clustering is one of the most commonly used digital image segmentation technique in multifarious fields including medical image segmentation. In essence, this study proposes clustering algorithm to acquire good segmented images of Plasmodium Vivax malaria parasite species via unsupervised pixel segmentation. In this study, enhanced k-means (EKM) clustering algorithm which is an enhanced version of the conventional k-means (KM) clustering algorithm has been proposed for malaria slide image segmentation. In the proposed EKM clustering algorithm, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented image can be generated. The satisfactory sensitivity together with the high specificity and accuracy values obtained from an average of 100 malaria images, indicates that the EKM algorithm has provided good segmentation performances as compared to k-means, fuzzy c-means and moving k-means clustering algorithms. Good segmented malaria parasite and clean segmented malaria image has been acquired using the proposed clustering algorithm. Hence, the proposed EKM clustering can be considered as an image segmentation tool for segmenting the malaria images.
CITE THIS ARTICLE
Abdul Nasir, Aimi Salihah, et al. “Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation.” Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 42.1 (2018): 1-15.
Abdul Nasir, A. S., Mashor, M. Y., & Mohamed, Z. (2018). Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 42(1), 1-15.
Abdul Nasir, Aimi Salihah, Mohd Yusoff Mashor, and Zeehaida Mohamed. “Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation.” Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 42, no. 1 (2018): 1-15.
Abdul Nasir, A.S., Mashor, M.Y. and Mohamed, Z., 2018. Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 42(1), pp.1-15.
Abdul Nasir, AS, Mashor, MY, Mohamed, Z. Enhanced k-Means Clustering Algorithm for Malaria Image Segmentation. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. 2018;42(1):1-15.
 World Malaria Report 2015, World Health Organization, Switzerland, 2016.
 Azikiwe, C. C. A., C. C. Ifezulike, I. M. Siminialayi, L. U. Amazu, J. C. Enye, and O. E. Nwakwunite. “A comparative laboratory diagnosis of malaria: microscopy versus rapid diagnostic test kits.” Asian Pacific journal of tropical biomedicine 2, no. 4 (2012): 307-310.
 Sio, Selena WS, Weiling Sun, Saravana Kumar, Wong Zeng Bin, Soon Shan Tan, Sim Heng Ong, Haruhisa Kikuchi, Yoshiteru Oshima, and Kevin SW Tan. “MalariaCount: an image analysis-based program for the accurate determination of parasitemia.” Journal of microbiological methods 68, no. 1 (2007): 11-18.
 Somasekar, J., and B. Eswara Reddy. “Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging.” Computers & Electrical Engineering 45 (2015): 336-351.
 Das, D. K., R. Mukherjee, and C. Chakraborty. “Computational microscopic imaging for malaria parasite detection: a systematic review.” Journal of microscopy 260, no. 1 (2015): 1-19.
 Rao, KNR Mohana, and A. G. Dempster. “Modification on distance transform to avoid over-segmentation and under-segmentation.” In Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom, pp. 295-301. IEEE, 2002.
 Das, Dev Kumar, Madhumala Ghosh, Mallika Pal, Asok K. Maiti, and Chandan Chakraborty. “Machine learning approach for automated screening of malaria parasite using light microscopic images.” Micron 45 (2013): 97-106.
 Preedanan, Wongsakorn, Montri Phothisonothai, Wongwit Senavongse, and Suchada Tantisatirapong. “Automated detection of plasmodium falciparum from Giemsa-stained thin blood films.” In Knowledge and Smart Technology (KST), 2016 8th International Conference on, pp. 215-218. IEEE, 2016.
 Muda, T. Zalizam T., and Rosalina Abdul Salam. “Comparative Analysis on Blood Cell Image Segmentation.” In 2nd International Symposium on Computer, Communication, Control and Automation, pp. 474-477. Atlantis Press, 2013.
 Abdul-Nasir, Aimi Salihah, Mohd Yusoff Mashor, and Zeehaida Mohamed. “Segmentation of Malaria Parasite Based on Stained Blood Cells Detection.” In Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 24, pp. 43-55. Trans Tech Publications, 2015.
 Harun, Nor Hazlyna, Mohd Yusof Mashor, and Rosline Hassan. “Segmentation Technique for Acute Leukemia Blood Cells Images using Saturation Component and Moving K-Mean Clustering Procedures.”
 Kaur, Ishmeet, and Lalit Mann Singh. A method of disease detection and segmentation of retinal blood vessels using fuzzy c-means and neutrosophic approach. Infinite Study, 2016.
 Kockara, Sinan, Mutlu Mete, Bernard Chen, and Kemal Aydin. “Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images.” In BMC bioinformatics, vol. 11, no. 6, p. S26. BioMed Central, 2010.
 MacQueen, James. “Some methods for classification and analysis of multivariate observations.” In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281-297. 1967.
 Khan, Shehroz S., and Amir Ahmad. “Cluster center initialization algorithm for K-means clustering.” Pattern recognition letters 25, no. 11 (2004): 1293-1302.
 D. Li, J. Chen, and L. Qian, “A new k-means algorithm for data clustering, Int. J. Sensing,” Computing & Control, pp. 38–48, 2011.
 J Bezdek, James C. “Objective Function Clustering.” In Pattern recognition with fuzzy objective function algorithms, pp. 43-93. Springer, Boston, MA, 1981.
 Hung, Ming-Chuan, and Don-Lin Yang. “An efficient fuzzy c-means clustering algorithm.” In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, pp. 225-232. IEEE, 2001.
 Mashor, Mohd Yusoff. “Hybrid training algorithm for RBF network.” International Journal of the computer, the Internet and Management 8, no. 2 (2000): 50-65.
 Bagirov, Adil M. “Modified global k-means algorithm for minimum sum-of-squares clustering problems.” Pattern Recognition 41, no. 10 (2008): 3192-3199.
 Yusoff, Intan Aidha, and Nor Ashidi Mat Isa. “Two-dimensional clustering algorithms for image segmentation.” WSEAS transactions on computers 10, no. 10 (2011): 332-342.
 Abdul-Nasir, Aimi Salihah, Mohd Yusoff Mashor, and Zeehaida Mohamed. “Modified global and modified linear contrast stretching algorithms: New colour contrast enhancement techniques for microscopic analysis of malaria slide images.” Computational and mathematical methods in medicine 2012 (2012).