Author(s): T. Abimala, R. Joylin Rini

Email(s): abimala.rajendran@gmail.com , joylinrini@gmail.com

DOI: 10.5958/0974-360X.2019.00385.8   

Address: T. Abimala1, R. Joylin Rini2
1,2Assistant Professor, ICE Department, St. Joseph’s College of Engineering, Chennai
*Corresponding Author

Published In:   Volume - 12,      Issue - 5,     Year - 2019


ABSTRACT:
Malaria is a mosquito-borne irresistible chronic sickness of humans and other creatures brought about by parasitic protozoans which belong to Plasmodium type. Malaria causes side effects that incorporate fever, fatigue, vomiting and cerebral pains. If not properly treated, it can bring about yellow skin, unconsciousness and even death. Malaria is induced by five species of plasmodium- P. Falciparum, P. Vivax, P. Malariae, P. Ovale and P. Knowlesi. In this paper, a venture has been formulated to develop an automated diagnosis strategy for classifying the malarial parasites. The blood smear images obtained from CDC database were segmented by utilizing Fuzzy C Means (FCM) and kapur segmentation strategies. The segmented image has been further utilized to extract features and the extracted measurements have been utilized for classifying the plasmodium species using SVM (Support Vector Machine) classification technique.


Cite this article:
T. Abimala, R. Joylin Rini. Classification of Distinct Plasmodium Species in Thin Blood Smear Images using Kapur Segmentation Strategy. Research J. Pharm. and Tech. 2019; 12(5):2309-2316. doi: 10.5958/0974-360X.2019.00385.8

Cite(Electronic):
T. Abimala, R. Joylin Rini. Classification of Distinct Plasmodium Species in Thin Blood Smear Images using Kapur Segmentation Strategy. Research J. Pharm. and Tech. 2019; 12(5):2309-2316. doi: 10.5958/0974-360X.2019.00385.8   Available on: https://rjptonline.org/AbstractView.aspx?PID=2019-12-5-44


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RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

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