Estimating the number of malaria parasites on blood smears microscopic images using penalized spline nonparametric Poisson regression

Abstract

So far, the detection and calculation of the malaria index has been done manually using thick and thin blood smears. Weaknesses of microscopic examination include the inability to detect low parasitaemia (low titre) so that it is not useful in non-endemic areas of malaria, the possibility of misinterpretation of very low or very high parasitaemia, the inability to detect mixed infections requires time and expertise in preparation for reading. Detection and calculation of parasites using digital imaging has begun to be studied in the world, but its application is still limited, especially in Indonesia. Several statistical models can be used to estimate the parasite index and detect parasite morphology microscopically. In this research, we propose an alternative method, called PSNPR method, to estimate the number of malaria parasites precisely by using a statistical modeling approach, namely, penalized spline nonparametric Poisson regression (PSNPR) model. We use image processing techniques for changing image to numeric, then we reduce dimension by using discrete wavelet transform, and principal component analysis. The results show that the proposed alternative method has high ability to detect and calculate the number of malaria parasites on microscopic image of blood smears. In the future, the results of this study can be used for prediction purpose that is to predict duration of time until the malaria parasites death after the patient is given treatment by a doctor who treats the patient.