Automated status identification of microscopic images obtained from malaria thin blood smears

Abstract

-Development of an accurate laboratory diagnostic tool, as recommended by WHO, is the key step to overcome the serious global health burden caused by malaria. This study aims to explore the possibility of computerized diagnosis of malaria and to develop a novel image processing algorithm to reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. The algorithm was designed as an expert system based on the method used by medical practitioner performing microscopy diagnosis of malaria. Digital images were acquired using a digital camera connected to a light microscope. Prior to processing, the images were subjected to gray-scale conversion to decrease image variability. Global thresholding were implemented to obtain erythrocyte and other blood cell components in each image. The segmented images were further processed to obtain possibly infected erythrocyte and the components of parasite inside the corresponding erythrocyte using multiple threshold. These parasite's constituents (nucleus and cytoplasm) were used as the preliminary basis for parasite/non parasite classification. Malaria samples prepared and provided by Eijkman Institute of Molecular Biology Indonesia were used to test the proposed algorithm.

Two-stage feature extraction to identify Plasmodium ovale from thin blood smear microphotograph

“…2. Preprocessing comprises of noise filtering, hole filling as developed in the previous work [4]. The next step is to extract erythrocyte from the input image for being further analyzed in the subsequent stages.…”

“…Unfortunately, the morphological change of the parasites are less considered in those studies. In our initial study, we have developed a Computer Aided Diagnosis system to assist the microscopist in identifying the Malaria status of the patient [4]. The feature extraction was developed to identify the presence of Plasmodium falciparum by calculating the ratio between pixels which is assumed as the part of parasite nucleus and cytoplasm.…”

Malaria Parasitaemia Estimation � A new perspective (Preprint)

“…Tek et al [14] [15], used image subtraction from a known image whereas Das et al [6] adapted the 'Gray World assumption' for illumination correction. To achieve noise elimination, Median filtering has been adapted by most authors like, Ruberto et al [16], Ross et al [17], Anggraini et al [18], Das et al [6], Sutkar et al [19], Chandra et al [20], Rosado et al [21], Predanan et al [22], Bahendwar et al [23] and Nugroho et al [24]. Authors Dave et al [25] and Savkare et al [26] have used a combination of Median filtering with Laplacian filter for noise removal along with enhancement of the edge region.…”

“…Khan et al [38], used different textural features with Feed-forward Back Propagation Neural Network for parasite identification. Anggraini et al [18], used Multilayer perceptron model for classification. The authors Das et al [6], Ghosh et al [45], used Bayesian and SVM classifier for detecting parasite region.…”

“…The authors Reni et al [29] utilized a modified 'Morphological Closing' operation for artefact removal and preservation of maximum foreground information. Image binarization using Otsu thresholding for image segmentation was performed by authors Das et al [6], Savkare et al [34], Ahirwar et al [28], Anggraini et al [18]. Mehrjou [35], Malihi et al [36] Rosado et al [21] and Savkare et al [26].…”