A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images

Abstract

Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-consuming task. This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects, from which erythrocytes are found. Finally, a two step classification process identifies infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and average specificity of 91.2%.

Malaria Parasitaemia Estimation � A new perspective (Preprint)

“…The red blood cell de-clumping is performed by a rule based binary splitting algorithm as proposed by Kumar et al [30] is also employed by Sio et al [27] to de-clump red blood cells for accurate enumeration result. Diaz et al [31] used a template matching method by sliding a template over the clump region to separate the cells. Watershed transform method for clump splitting has been used by Preedanan et al [22] and Bairagi et al [32] (with Euclidian distance transform).…”

“…Authors Diaz et al [31], used different histogram features with Support Vector Machine (SVM) and multilayer perceptron for classification. Khan et al [38], used different textural features with Feed-forward Back Propagation Neural Network for parasite identification.…”

Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

“…proposed a semi-automatic method for quantification and classification of malaria infected erythrocytes. Here, erythrocyte feature is described by a set of histogram features such as color histogram, saturation level histogram, gray scale histogram, tamura texture histogram, and sobel histogram [9]. Springl et.…”

“…The morphological feature of different malaria parasites is shown in Table 1. Several researchers have used different feature set to classify the infected and non-infected erythrocytes [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The microscopic image of the thin blood smear is shown in Figure 1.…”

Comparative Analysis of different types of Malaria Diseases using First Order Features

“…Color histogram based malaria parasite detection has been carried out [10]. Further, [11] showed quantification and classification of P. falciparum infected erythrocytes. Morphological and novel thresholding selection techniques for identification of erythrocytes were used by [12].…”