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Comparative Analysis of different types of Malaria Diseases using First Order Features

Ajala, Funmilola. A, Fenwa, Olusayo. D, Aku, Micheal. A. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
10.5120/ijais15-451297
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  1. Funmilola. A Ajala, Olusayo. D Fenwa and Micheal. A Aku. Article: Comparative Analysis of different types of Malaria Diseases using First Order Features. International Journal of Applied Information Systems 8(3):20-26, February 2015. BibTeX

    @article{key:article,
    	author = "Ajala,Funmilola. A and Fenwa,Olusayo. D and Aku,Micheal. A.",
    	title = "Article: Comparative Analysis of different types of Malaria Diseases using First Order Features",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 3,
    	pages = "20-26",
    	month = "February",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Micro-biologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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Keywords

Plasmodium, Segmentation, Energy, Kurtosis, Skewness, Entropy