MATEC Web of Conferences
Volume 150, 2018Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
|Number of page(s)||6|
|Section||Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM)|
|Published online||23 February 2018|
The Cascaded Enhanced k-Means and Fuzzy c-Means Clustering Algorithms for Automated Segmentation of Malaria Parasites
Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
2 Department of Microbiology & Parasitology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
* Corresponding author: firstname.lastname@example.org
Malaria continues to be one of the leading causes of death in the world, despite the massive efforts put forth by World Health Organization (WHO) in eradicating it, worldwide. Efficient control and proper treatment of this disease requires early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes a malaria parasite segmentation approach via cascaded clustering algorithms to automate the malaria diagnosis process. The comparisons among the cascaded clustering algorithms have been made by considering the accuracy, sensitivity and specificity of the segmented malaria images. Based on the qualitative and quantitative findings, the results show that by using the final centres that have been generated by enhanced k-means (EKM) clustering as the initial centres for fuzzy c-means (FCM) clustering, has led to the production of good segmented malaria image. The proposed cascaded EKM and FCM clustering has successfully segmented 100 malaria images of Plasmodium Vivax species with average segmentation accuracy, sensitivity and specificity values of 99.22%, 88.84% and 99.56%, respectively. Therefore, the EKM algorithm has given the best performance compared to k-means (KM) and moving k-means (MKM) algorithms when all the three clustering algorithms are cascaded with FCM algorithm.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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