An automatic device for detection and classification of malaria parasite species in thick blood film

Abstract

BackgroundCurrent malaria diagnosis relies primarily on microscopic examination of Giemsa-stained thick and thin blood films. This method requires vigorously trained technicians to efficiently detect and classify the malaria parasite species such as Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) for an appropriate drug administration. However, accurate classification of parasite species is difficult to achieve because of inherent technical limitations and human inconsistency. To improve performance of malaria parasite classification, many researchers have proposed automated malaria detection devices using digital image analysis. These image processing tools, however, focus on detection of parasites on thin blood films, which may not detect the existence of parasites due to the parasite scarcity on the thin blood film. The problem is aggravated with low parasitemia condition. Automated detection and classification of parasites on thick blood films, which contain more numbers of parasite per detection area, would address the previous limitation.ResultsThe prototype of an automatic malaria parasite identification system is equipped with mountable motorized units for controlling the movements of objective lens and microscope stage. This unit was tested for its precision to move objective lens (vertical movement, z-axis) and microscope stage (in x- and y-horizontal movements). The average precision of x-, y- and z-axes movements were 71.481 ± 7.266 μm, 40.009 ± 0.000 μm, and 7.540 ± 0.889 nm, respectively. Classification of parasites on 60 Giemsa-stained thick blood films (40 blood films containing infected red blood cells and 20 control blood films of normal red blood cells) was tested using the image analysis module. By comparing our results with the ones verified by trained malaria microscopists, the prototype detected parasite-positive and parasite-negative blood films at the rate of 95% and 68.5% accuracy, respectively. For classification performance, the thick blood films with Pv parasite was correctly classified with the success rate of 75% while the accuracy of Pf classification was 90%.ConclusionsThis work presents an automatic device for both detection and classification of malaria parasite species on thick blood film. The system is based on digital image analysis and featured with motorized stage units, designed to easily be mounted on most conventional light microscopes used in the endemic areas. The constructed motorized module could control the movements of objective lens and microscope stage at high precision for effective acquisition of quality images for analysis. The analysis program could accurately classify parasite species, into Pf or Pv, based on distribution of chromatin size.

Detection of malaria parasites in thick blood smear: A review

“…To enhance the contrast of the image, Kaewkamnerd et al [25] controlled vertical movement of the motorized unit so that the system is able to capture images in different depths of field. The quality of the image is improved because of the margin in-focus information over a range of images to generate a single entirely in-focus image.…”

“…The last step is filtering malaria parasites from other objects according to their difference in sizes. The second and third steps are repeated until all the parasites are discovered and labelled [25]. The threshold value is determined based on image calculation, such as histogram, mean intensity value.…”

“…Kaewkamnerd et al [25] classify the malaria parasites into two species, P. falciparum (Pf) and P. vivax (Pv) based on chromatin size feature. The observation of the chromatin size shows that Pf parasites have smaller size of chromatin than those of Pv.…”

“…After pre-processing step is done, Kaewkamnerd et al [25] convert the whole in-focus image into HSV (Hue-SaturationValue) color format. For segmentation purposes, the value component from HSV color channel is employed.…”

Detection of Malarial and Babesiosis Parasite in RBC using Combination of Annular Ring Ratio and Marker Controlled Watershed Segmentation

“…The technique in [18] uses SUSAN edge based algorithm to segment the RBCs and Probabilistic Neural Network (PNN) is used to classify the infected cells from normal cells. The technique in [10] uses histogram based thresholding to identify the infected cells. The scheme in [4-6] uses colour and morphology based algorithm to identify the infected cells.…”