Segmentation of Malaria Parasite Candidate from Thickblood Smear Microscopic Images using Watershed and Adaptive Thresholding

Authors

  • Umi Salamah Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. Departement of Informatics, Universitas Sebelas Maret, Surakarta, Indonesia.
  • Riyanarto Sarno Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
  • Agus Zainal Arifin Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
  • Sarimuddin Sarimuddin Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
  • Anto Satriyo Nugroho Center for Info. and Comm. Tech. Agency for the Assessment and Application of Technology, Indonesia.
  • Ismail Ekoprayitno Rozi Eijkman Institute for Molecular Biology, Indonesia.
  • Puji Budi Setia Asih Eijkman Institute for Molecular Biology, Indonesia.

Keywords:

Adaptive Thresholding, Low Quality, Malaria Parasite, Thick Blood Smear, Watershed,

Abstract

Segmentation of malaria parasite on thick blood smear is a critical intermediate step in automation process of malaria detection. Most of the thick blood smear have low quality that characterized by high noise, the low-intensity difference between background and foreground, and the presence of artifacts. This situation makes the segmentation process becomes difficult. In this paper we proposed a new segmentation strategy for microscopic images of malaria parasite obtained from thick blood smear using watershed and adaptive thresholding. The proposed method consists of two main stages: image enhancement and segmentation. Enhancement process used Low-pass filtering and contrast stretching. Meanwhile, the segmentation used combination watershed segmentation and adaptive thresholding. The performance was evaluated on 253 parasite candidates, cropped from 22 thick blood smear microphotographs. The experimental results showed that the average segmentation accuracy of the proposed algorithm was 95.2%. Further analysis showed that the nucleus and cytoplasm of the malaria parasite were successfully extracted, thus the method is suitable for being used on detection of malaria parasites.

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Published

2018-07-03

How to Cite

Salamah, U., Sarno, R., Zainal Arifin, A., Sarimuddin, S., Nugroho, A. S., Rozi, I. E., & Asih, P. B. S. (2018). Segmentation of Malaria Parasite Candidate from Thickblood Smear Microscopic Images using Watershed and Adaptive Thresholding. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 113–117. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4327

Issue

Vol. 10 No. 2-4: Enhancing Digital Trust for Sustainable Development

Section

Articles

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