We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Unsupervised identification of malaria parasites using computer vision.
- Authors
Khan, Najeed Ahmed; Pervaz, Hassan; Latif, Arsalan; Musharaff, Ayesha
- 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.
- Subjects
MALARIA diagnosis; PLASMODIUM; MICROORGANISM identification; COMPUTER vision; MOSQUITO vectors
- Publication
Pakistan Journal of Pharmaceutical Sciences, 2017, Vol 30, Issue 1, p223
- ISSN
1011-601X
- Publication type
Article