A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images

Umi Salamah, Riyanarto Sarno, Agus Zainal Arifin, Anto Satriyo Nugroho, Ismail Ekoprayitno Rozi, Puji Budi Setia Asih


Parasite Detection on thick blood smears is a critical step in Malaria diagnosis. Most of the thick blood smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, blood component segmentation using intensity slicing and morphological operation, blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick blood smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection.


Detection; intensity slicing; malaria parasites; morphological operation; thick blood smear

Full Text:



WHO (2014) World Malaria Report 2014. [Online]. Available: http://www.who.int/malaria/publications/world_malaria_report_2014/en/

I. Hammami, A. Garcia, and G. Nuel, “Evidence for overdispersion in the distribution of malaria parasites and leukocytes in thick blood smears,” Malaria Journal, vol. 12, pp. 1–15, 2013.

D. Syafruddin, P. B. Asih, I. E. Rozi, K. Chand, and S. Wangsamuda, Diagnosis mikroskopik malaria, 1st ed. Lembaga Biologi Molekuler Eijkman, 2010.

WHO, Basic Malaria Microscopy, 2nd ed. Switzerland: WHO Press, 2010.

N. Linder, R. Turkki, M. Williander, A. Mårtensson, V. Diwan, E. Rahtu, M. Pletikäinen, M. Lundin, J. Lundin, “A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears,” PLOS ONE, vol. 9, no. 8, pp. 1–12, 2014.

D. Anggraini, A. S. Nugroho, C. Pratama, I. E. Rozi, V. Pragesjvara, and M. Gunawan, “Automated status identification of microscopic images obtained from malaria thin blood smears using bayes decision: A study case in plasmodium falciparum,” in Proc. International Conference on Advanced Computer Science and Information Systems, 2011, pp. 347–352.

D. K. Das, M. Ghosh, M. Pal, A. K. Maiti, and C. Chakraborty, “Machine learning approach for automated screening of malaria parasite using light microscopic images,” Micron, vol. 45, pp. 97–106, 2013.

E. Dekel, A. Rivkin, M. Heidenreich, Y. Nadav, Y. Ofir-Birin, Z. Porat, N. Regev-Rudzki, “Identification and classification of the malaria parasite blood developmental stages, using imaging flow cytometry,” Methods, vol 112, pp. 157-166, 2016.

G. Díaz, F. A. González, and E. Romero, “A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images,” Journal of Biomedical Informatics, vol. 42, no. 2, pp. 296–307, 2009.

Z. May and M. Aziz, “Automated quantification and classification of malaria parasites in thin blood smears,” in Proc. International Conference on Signal and Image Processing Applications, 2013, pp. 369–373.

M. I. Razzak, “Automatic Detection and Classification of Malarial Parasite,” International Journal of Biometrics and Bioinformatics, vol. 9, pp. 1–12, 2015.

S. S. Savkare and S. P. Narote, “Automatic System for Classification of Erythrocytes Infected,” in Proc. 2nd International Conference on Communication, Computing & Security, 2012, vol. 6, pp. 405–410.

V. V. Makkapati and R. M. Rao, “Ontology-based malaria parasite stage and species identification from peripheral blood smear images,” in Proc. International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6138–6141, 2011.

F. B. Tek, A. G. Dempster, and I. Kale, “Parasite detection and identification for automated thin blood film malaria diagnosis,” Computer Vision and Image Understanding, vol. 114, pp. 21–32, 2010.

K. Bhowmik and P. Rakshit, “Detection of the presence of Parasites in Human RBC In Case of Diagnosing Malaria,” in Proc. Second International Conference on Image Information Processing, 2013, pp. 329–334.

D. Mas, B. Ferrer, D. Cojoc, S. Finaurini, V. Mico, and J. Garcia, “Novel image processing approach to detect malaria,” Optics Communications, vol. 350, pp. 13–18, 2015.

M. Le, T. R. Bretschneider, C. Kuss, and P. R. Preiser, “A Novel semi-automatic image processing approach to Determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears,” BMC Cell Biology, vol. 12, pp. 1–12, 2008.

S. Kaewkamnerd, A. Intarapanich, M. Pannarat, S. Chaotheing, C. Uthaipibull, and S. Tongsima, “Detection and Classification Device for Malaria Parasites in Thick-Blood Films,” in Proc. The 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2011, pp. 435–438.

M. Elter, E. Haßlmeyer, and T. Zerfaß, “Detection of malaria parasites in thick blood films,” in Proc. 33rd Annual International Conference of the IEEE EMBS, 2011, pp. 5140–5144.

J. E. Arco, J. M. Górriz, J. Ramírez, I. Álvarez, and C. G. Puntonet, “Digital image analysis for automatic enumeration of malaria parasites using morphological operations,” Expert Systems with Applications, vol. 42, no. 6, pp. 3041–3047, 2015.

J. Kaur and A. Choudhary, “Comparison of Several Contrast Stretching Techniques on Acute Leukemia Images,” International Journal Engineering Innovation Technology, vol. 2, pp. 332–335, 2012.

R. E. Putri, A. Yahya, N. M. Adam, and S. A. Aziz, “Correlation of Moisture Content to Selected Mechanical Properties of Rice Grain Sample,” International Journal on Advanced Science, Engineering & Information Technology, vol. 5, no. 5, pp. 264–267, 2015.

S. Y. Jiang and L. X. Wang, “Efficient feature selection based on correlation measure between continuous and discrete features,” Information Processing Letters, vol. 116, no. 2, pp. 203–215, 2016.

DOI: http://dx.doi.org/10.18517/ijaseit.9.4.4843


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development