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: email@example.com
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.
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