Segmentation Based on Morphological Approach for Enhanced Malaria Parasites Detection

Authors

  • Wan Azani Mustafa Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia. School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • Aimi Salihah Abdul-Nasir Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia.
  • Zeehaida Mohamed Department of Microbiology & Parasitology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
  • Haniza Yazid School of Mechatronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia

Keywords:

Detection, Malaria, Morphological, Normalization, Segmentation,

Abstract

Malaria is one of the serious medical issues in the world, with a high frequency of cases in tropical and subtropical regions; further driven by dilapidated living conditions. In 2015, there were approximately 214 million cases of malaria and 438,000 deaths estimated globally, mostly among African children. Malaria develops to become life-threatening without immediate action. Therefore, this paper proposes an image segmentation technique via morphological approach in order to automate the detection of the presence of malaria parasites in malaria image. This technique based on a combination of filtering image and the morphological operator. The effectiveness of the proposed image segmentation approach has been measured by comparing this technique with other segmentation techniques namely, Otsu, Niblack, local adaptive, and Feng methods. Overall, the experimental results indicate that the proposed morphological approach has produced the best segmentation performance with segmentation accuracy and specificity of 98.52% and 99.62%.

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Published

2018-05-30

How to Cite

Mustafa, W. A., Abdul-Nasir, A. S., Mohamed, Z., & Yazid, H. (2018). Segmentation Based on Morphological Approach for Enhanced Malaria Parasites Detection. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 15–20. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4068

Issue

Vol. 10 No. 1-16: Recent Trends in Biomedical Engineering Systems and Technologies I

Section

Articles

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