Classification of Plasmodium Malariae dan Plasmodium Ovale in Microscopic Thin Blood Smear Digital Images

Hanung Adi Nugroho, Aulia Darojatun, Igi Ardiyanto, Ratna L.B Buana

Abstract


Malaria is one of the global diseases, which mostly found in eastern Indonesia. It is caused by Plasmodium parasite infection, with four type of common species that are Plasmodium ovale (PO), Plasmodium Malaria (PM), Plasmodium falciparum (PF) and Plasmodium vivax (PV). Malaria can be detected by taking a microscopic analysis from a patient blood sample. Although it is a gold standard of malaria identification according to the WHO, this method has a risk of miss diagnosis due to the human factors. This study proposed a classification method with morphological features of PM and PO in order to help the medical expertise in identifying the malaria parasite from a thin blood smear digital microscopic image. The data used are digital images that have been through the Region of Interest (ROI) determination process. Furthermore, the process followed by improving the morphological and feature extraction of shapes and colors. Based on these obtained features, the parasites are classified by using the multilayer perceptron method. From this study, we found that the classification system has the accuracy of 95%, the sensitivity of 93%, and the specificity of 97%.


Keywords


Malaria; Plasmodium malariae; Plasmodium ovale; multilayer perceptron; feature extraction; Computer Aided Diagnosis (CAD)

Full Text:

PDF

References


WHO, "World Malaria Report 2017," WHO Press, Geneva, 2017.

P. D. d. I. K. K. RI, InfoDatin Malaria, 2016.

B. Singh and C. Danesvar, "Human infections and detection of plasmodium knowlesi," Clin. Microbiol. Rev. , vol. 26, pp. 165-184, 2013.

N. I. o. Health, "Wikipedia," 20 Mei 2009. [Online]. Available: https://id.wikipedia.org/wiki/Berkas:Life_Cycle_of_the_Malaria_Parasite.jpg. [Accessed 4 Januari 2018].

Hulden L, Hulden L. “Activation of the hypnozoite: a part of Plasmodium vivax life cycle and survival,â€. Malaria Journal, 10:90, 2011.

G. Diaz, F. A. Gonzalez, and E. Romero, “A semi-automatic method for quantification and classification of red blood cells infected with malaria parasites in microscopic images,†J. Biomed. Inform., vol. 42, no. 2, pp. 296–307, 2009.

F. B. Tek, A. Dempster, and I. Kale, “Malaria parasite detection in peripheral blood images,†in British Machine Vision Conference 2006 (BVMC2006), 2006, pp. 347–356.

M. Elter, E. Hasslmeyer, and T. Zerfass, “Detection of malaria parasites in thick blood films,†in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011, pp. 5140–5144.

J. Frean, “Reliable enumeration of malaria parasites in thick blood films using digital image analysis,†Malar. J., vol. 8, no. 1, p. 218, 2009.

A. Bashir, Z. A. Mustafa, I. Abdelhameid, and R. Ibrahem, “Detection of Malaria Parasites Using Digital Image Processing,†Int. Conf. Commun. Control. Comput. Electron. Eng. 2017, no. c, 2017.

H. A. Nugroho, M. S. Wibawa, N. A. Setiawan, E. E. H. Murhandarwati, R. L. B. Buana, “Identification of Plasmodium falciparum and Plasmodium vivax on Digital Image of Thin Blood Films,†J. of Telecomunication, Electronic and Computing, 2017

K. Imaroh, Sistem Berbasis Komputer untuk Deteksi Spesies Parasit Plasmodium ovale dan malariae pada Citra Mikroskopis Digital Sediaan Darah Tipis, Yogyakarta, 2017.

A. k. Jain, Fundamental Digital Image Processing, Prentice-Hall, 1989.

I. M. D. Maysanjaya, Identifikasi Fase Plasmodium Vivax pada CItra Mikroskopis Digital Sediaan Darah Tipis, Universitas Gadjah Mada, 2016.

Yuan Hai, Ling Li, Jia Gu, "Image enhancement based on contrast limited adaptive histogram equalization for 3D images of stereoscopic endoscopy", Information and Automation 2015 IEEE International Conference on, pp. 668-672, 2015.

Gunawan, F. Halim and E. Wijaya, "Perangkat Lunak Segmentasi CItra Dengan Metode WAtershed," Jurnal SIFO Mikroskill, vol. 12, no. 2, 2011.

Harendra Upraity, K V Arya, "Efficient face recognition using morphological operations", Industrial and Information Systems (ICIIS) 2014 9th International Conference on, pp. 1-6, 2014.

W. A. Saputra, H. A. Nugroho and A. E. Permanasari, "Toward development of automated plasmodium detection for Malaria diagnosis in thin blood smear image: An overview," International Conference of Information Technology and System Innovation, ICITSI 2016, 2017

R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 1977.

H. White, "Learning in artificial neural networks: A statistical perspective", Neural Comput., vol. 1, pp. 425-464, 1989.




DOI: http://dx.doi.org/10.18517/ijaseit.8.6.6514

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development