Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear

Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast k-means clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.

[1]  Manjinder Singh,et al.  Enhanced Color Correction Using Histogram Stretching Based On Modified Gray World and White Patch Algorithms , 2014 .

[2]  H. Jaafar,et al.  A Fast and Efficient Segmentation of Soil-Transmitted Helminths Through Various Color Models and k-Means Clustering , 2020 .

[3]  Chun-Chieh Chen,et al.  Fast K-means algorithm based on a level histogram for image retrieval , 2014, Expert Syst. Appl..

[4]  Daniel Maitethia Memeu.  A Rapid Malaria Diagnostic Method Based On Automatic Detection And Classification Of Plasmodium Parasites In Stained Thin Blood Smear Images , 2014 .

[5]  Aimi Salihah Abdul Nasir,et al.  Automated Cells Counting for Leukaemia and Malaria Detection Based on RGB and HSV Colour Spaces Analysis , 2021 .

[6]  A B Shahriman,et al.  Colour Component Analysis Approach for Malaria Parasites Detection Based on Thick Blood Smear Images , 2019, IOP Conference Series: Materials Science and Engineering.

[7]  M. Sangeetha,et al.  Detection of Malarial Parasites using Image Processing Techniques from Blood Smear Slides , 2018 .

[8]  Zeehaida Mohamed,et al.  Modified Global and Modified Linear Contrast Stretching Algorithms: New Colour Contrast Enhancement Techniques for Microscopic Analysis of Malaria Slide Images , 2012, Comput. Math. Methods Medicine.

[9]  Haryati Jaafar,et al.  Analysis of Colour Constancy Algorithms for Improving Segmentation of Malaria Images , 2018 .

[10]  Muhammad Imran Razzak,et al.  Automatic Detection and Classification of Malarial Parasite , 2015 .

[11]  P. K. Mishra,et al.  Understanding Color Models: A Review , 2012 .

[12]  B. Pritt,et al.  Update on Malaria Diagnostics and Test Utilization , 2017, Journal of Clinical Microbiology.

[13]  Zazilah May,et al.  Automated quantification and classification of malaria parasites in thin blood smears , 2013, 2013 IEEE International Conference on Signal and Image Processing Applications.

[14]  Juan Manuel Górriz,et al.  Digital image analysis for automatic enumeration of malaria parasites using morphological operations , 2015, Expert Syst. Appl..

[15]  Wan Azani Mustafa,et al.  Analysis of Distance Transforms for Watershed Segmentation on Chronic Leukaemia Images , 2018 .

[16]  Munendra Singh,et al.  Automated Nucleus Segmentation of Leukemia Blast Cells : Color Spaces Study , 2020, 2nd International Conference on Data, Engineering and Applications (IDEA).