Moment invariant features for automatic identification of critical malaria parasites
| Autor: | Ahalya Ravendran, Ransalu Senanayake, K.W.T. Roshali T. de Silva |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
business.industry
Gaussian Plasmodium parasite Pattern recognition Plasmodium falciparum Biology medicine.disease biology.organism_classification RGB color space Naive Bayes classifier symbols.namesake parasitic diseases medicine Gametocyte symbols Computer vision Artificial intelligence Invariant (mathematics) business Malaria |
| Zdroj: | 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS). |
| DOI: | 10.1109/iciinfs.2015.7399058 |
| Popis: | Malaria is a globally widespread mosquito-borne disease which is caused by Plasmodium parasite. Plasmodium falciparum is the most ubiquitous among few species of Plasmodium and its Gametocyte stage is the most virulent among all stages and species. Although blood films are stained for better visualisation through the microscope, the color difference between red blood cells and parasites is barely identifiable for a computer as images are represented in the RGB color space. Moreover, there are several parasites spread throughout the blood film in various orientations and sizes. The automatic classification becomes further challenging due to the presence of many artefacts in the blood film. A photomicrograph analysis method to determine the presence of the most critical parasite —Gametocyte stage of Plasmodium falciparum — in Giemsa-stained blood films is presented. Having extracted the parasite from the background of the image after a series of pre-processing operations, it is classified using both K-nearest neighbors (K-NN) and Gaussian naive Bayes classifiers. As the key element of the research, moment invariant features are utilised to make the input features invariant to translation, rotation and scale (TRS). Based on leave-one-out cross-validation, true positive rates of 77.78% and 88.89% and, true negative rates of 95.24% and 80.95% were achieved for K-NN and Gaussian naive Bayes classifiers respectively. Since a higher true positive rate is desirable in this application, Gaussian naive Bayes qualifies as the classifier while moment invariant features provide robust covariates for classifying Plasmodium falciparum from other Plasmodium species. |
| Databáze: | OpenAIRE |
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