Segmentation of malaria parasites in peripheral blood smear images

Makkapati, Vishnu Vardhan; Rao, Raghuveer M.

doi:10.1109/icassp.2009.4959845

Cited by 82 publications

(40 citation statements)

References 4 publications

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“…Dealing with these two tasks above, blood smear image analysis has been tackled by using conventional image processing techniques like morphology, edge detection, region growing, thresholding, and pattern recognition techniques etc., which all have shown certain degrees of success with respect to the used sample images [5]. For example, a segmentation scheme for Red Blood Cells (RBCs) and parasites based on HSV color space was presented in [6] via detecting dominant hue range and calculating optimal saturation; in [7], the algorithm is based on edge detection and splitting large clumps made up from erythrocytes edge linking; in [8], it analyzes the infected blood cell images using morphological operators to segment cell images and to classify the parasites; F. B. Tek et al proposed a novel binary parasite detection scheme that is based on a modified K nearest neighbor (KNN) classifier and compared three different classification schemes for the identification of the infecting species and life-cycle stages [9].…”

Section: Introductionmentioning

confidence: 99%

Malaria Cell Counting Diagnosis within Large Field of View

Zou

Chen

Zhang

et al. 2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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Abstract-Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs.

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Section: Introductionmentioning

confidence: 99%

Malaria Cell Counting Diagnosis within Large Field of View

Zou

Chen

Zhang

et al. 2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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“…Most of the research studies surveyed have used Giemsa stained smear slide images though other staining options like Leishman staining (Makkapati et al [5], Das et al [6]) and Romanowsky staining (Suwalka et al [7]) have been used in few studies. The staining of cells are important for parasite detection for both manual and automated systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Toha & Ngah [10], calculated a threshold value to identify parasite region followed by calculating the Euclidean distance to differentiate between each parasite cluster. Makkapati et al [5], segmented chromatin regions by means of Otsu threshold method using HSV colour model and computed distance of red blood cell region and obtained chromatin regions to differentiate from nucleus of white blood cells. Mandal et al [44], used Normalized cuts on the different colour model of the same image.…”

Section: Literature Reviewmentioning

confidence: 99%

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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“…Morphological and novel thresholding selection techniques for identification of erythrocytes were used by [12]. Malaria parasite in HSV (Hue, Saturation, and Value) color space was segmented by [13]. Erythrocytes infected by malaria parasites were detected by using statistical approach [14].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ajala¹,

Funmilola²,

Fenwa³

et al. 2015

IJAIS

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Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Microbiologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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Segmentation of malaria parasites in peripheral blood smear images

Cited by 82 publications

References 4 publications

Malaria Cell Counting Diagnosis within Large Field of View

Malaria Cell Counting Diagnosis within Large Field of View

Malaria Parasitaemia Estimation � A new perspective (Preprint)

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

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