Unsupervised identification of malaria parasites using computer vision | [Pakistan Journal of Pharmaceutical Sciences • 2017]

Author(s):

1. Najeed Ahmed Khan: Department of Computer Science & Software Engineering, NED University of Engineering & Technology,Karachi,Pakistan

2. Hassan Pervaz: Department of Computer Science & Software Engineering, NED University of Engineering & Technology,Karachi,Pakistan

3. Arsalan Latif: Department of Computer Science & Software Engineering, NED University of Engineering & Technology,Karachi,Pakistan

4. Ayesha Musharaff: Department of Computer Science & Software Engineering, NED University of Engineering & Technology,Karachi,Pakistan

Abstract:

Malaria in human is a serious and fatal tropical disease. This disease results from Anopheles mosquitoes that are infected by Plasmodium species. The clinical diagnosis of malaria based on the history, symptoms and clinical findings must always be confirmed by laboratory diagnosis. Laboratory diagnosis of malaria involves identification of malaria parasite or its antigen / products in the blood of the patient. Manual diagnosis of malaria parasite by the pathologists has proven to become cumbersome. Therefore, there is a need of automatic, efficient and accurate identification of malaria parasite. In this paper, we proposed a computer vision based approach to identify the malaria parasite from light microscopy images. This research deals with the challenges involved in the automatic detection of malaria parasite tissues. Our proposed method is based on the pixel-based approach. We used K-means clustering (unsupervised approach) for the segmentation to identify malaria parasite tissues.

Page(s): 223-228

DOI: DOI not available

Published: Journal: Pakistan Journal of Pharmaceutical Sciences, Volume: 30, Issue: 1, Year: 2017

Keywords:

Computer vision , Malaria parasite detection , unsupervised identification

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