Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/8939
Title: Optical Identification of Plasmodium falciparum Malarial Byproduct for Parasite Density Estimation
Authors: Opoku-Ansah, Jerry
Eghan, Moses Jojo
Anderson, Benjamin
Boampong, Johnson Nyarko
Edziah, Raymond
Adueming, Peter Osei-Wusu
Amuah, Charles Lloyd Yeboah
Issue Date: 11-Nov-2019
Publisher: Hindawi
Abstract: Plasmodium falciparum (P. falciparum) malarial degree of infection, termed as parasite density (PD), estimation is vital for point- of-care diagnosis and treatment of the disease. In this work, we present application of optical techniques: optical absorption and multispectral imaging for P. falciparum malarial byproduct (hemozoin) detection in human-infected blood samples to estimate PD. The blood samples were collected from volunteers who were tested positive for P. falciparum infections (i-blood), and after treatment, another set of blood samples (u-blood) were also taken. The i-blood samples were grouped based on PD (+, ++, +++, and ++++). Optical densities (ODs) of u-blood samples and i-blood samples at blood absorption bands of 405 nm, 541 nm, and 577 nm showed different optical absorption characteristics. Empirical computation of ratio of the ODs for the blood absorption bands revealed reduction in the ODs with increasing PD. Multispectral images containing uninfected red blood cells (u-RBCs) and P. falciparum-infected red blood cells (i-RBCs) on unstained blood smear slides exhibited spectrally determined decrease in both reflected and scattered pixel intensities and increase in transmitted pixel intensities with increasing PD. We further propose a linear classification model based on Fisher’s approach using reflected, scattered, and transmitted pixel intensities for easy and inexpensive estimation of PD as an alternative to manual estimation of PD, currently, the widely used technique. Application of the optical techniques and the proposed linear classification model are therefore recommended for improved malaria diagnosis and therapy
URI: http://hdl.handle.net/123456789/8939
Appears in Collections:School of Allied Health Sciences

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