A Robust Segmentation of Malaria Parasites Detection using Fast k-Means and Enhanced k-Means Clustering Algorithms

Image segmentation is the crucial stage in image analysis since it represents the first step towards extracting important information from the image. In summary, this paper presents several clustering approach to obtain fully malaria parasite cells segmented images of Plasmodium Falciparum and Plasmodium Vivax species on thick smear images. Despite k-means is a renowned clustering approach, its effectiveness is still unreliable due to some vulnerabilities which leads to the need of a better approach. To be specific, fast k-means and enhanced k-means are the adaptation of existing k-means. Fast k-means eliminates the requirement to retraining cluster centres, thus reducing the amount of time it takes to train image cluster centres. While, enhanced k-means introduces the idea of variance and a revised edition of the transferring method for clustered members to aid the distribution of data to the appropriate centre throughout the clustering action. Hence, the goal of this study is to explore the efficacy of k-means, fast k-means and enhanced k-means algorithms in order to achieve a clean segmented image with ability to correctly segment whole region of parasites on thick smear images. Practically, about 100 thick blood smear images were analyzed, and the verdict demonstrate that segmentation via fast k-means clustering algorithm has splendid segmentation performance, with an accuracy of 99.91%, sensitivity of 75.75%, and specificity of 99.93%.