Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging

Somasekar, J.; Reddy, B. Eswara

doi:10.1016/j.compeleceng.2015.04.009

Cited by 46 publications

(22 citation statements)

References 17 publications

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“…To distinguish between true and false positives, Back propagation Feed forward neural network based to state the classifier have been used, which later diagnoses the species of the disease. In [6], RBCs infected with malaria parasites found in the microscopic images is found using an edge-based segmentation that has been developed to improve the diagnostic process. Illuminance correction and color effect reduction is done with color space transformation and Gamma equalization.…”

Section: Literature Surveymentioning

confidence: 99%

Diagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks

Mohana*,

Vani,

K.N

et al. 2019

IJRTE

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Malaria is a deadly disease brought about by Plasmodium parasites which affects the general population through the bites of female mosquitoes, called "malaria vectors." There are about five parasites species that cause malaria in human body, and two of the species namely P. falciparum , P.vivax pose the greatest threat. The most prominent technique to detect malaria is by taking blood smear samples to check if the RBC is affected by parasite under the microscope by qualified experts. It is a complex technique and the diagnosis depends on the experience and inside of the person who performs the examination. Malaria blood smear have been diagnosed earlier using image processing methods based on machine learning. This was not effective so far. Convolutional Neural Network (CNN) is use in this system which helps in classifying the cells present in the blood smear images as infected or uninfected

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Section: Literature Surveymentioning

confidence: 99%

Diagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks

Mohana*,

Vani,

K.N

et al. 2019

IJRTE

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show abstract

“…There are several studies suggesting a computer vision approach for the diagnosis of malaria through microscopic blood smear images. A detailed survey for segmentation of malaria parasites through microscopic imaging found in [2] [3]. Otsu or histogram thresholding has been attempted for malaria infected erythrocyte segmentation and It has limitation where textural variation is high [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Two-Stage Thresholding Method for Segmentation of Malaria Parasites in Microscopic Blood Images

Jalari¹,

Reddy

2017

JBEMI

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Developing computerized diagnostic tool for the detection of malaria infected cells in microscopic blood images can help to reduce malaria-induced mortality. Segmentation of malaria infected cells is a key step in the automated malaria diagnosis pipeline. In this paper, a novel two-stage thresholding method for segmentation of malaria parasites in microscopic blood images for diagnosis is presented. The RGB microscopic image is converted into YUV color space and luminance component is considered for single channel processing. The infected parasites are segmented by the proposed threshold method, which is carried out in two stages by maximizing between-class variance of an original image and consequently by an iterative threshold selection from a stage-one threshold image with suitable stopping criteria. The experimental results on benchmark dataset that comprise more than 300 images show that the proposed method successfully detects malaria parasites with no prior knowledge of the contents of the image without parameter tuning.

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“…Digital image processing becomes one of the most common computer-based methods which often used in computer aided diagnosis (CAD) [5]. By utilizing a computer with special software which developed to support the malaria diagnosis, it can be categorized as an easy tool that can be easily distributed in rural areas.…”

Section: Introductionmentioning

confidence: 99%

Detection of malaria parasites in thick blood smear: A review

Azif

Nugroho²,

Wibirama³

2018

CST

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Based on data from World Health Organization, in 2015, there are 90% of deaths caused by malaria disease in Africa, Southeast Asia and countries of eastern Mediterranean. It makes the malaria become one of the most dangerous diseases that often leads to death. To support the diagnosis of malaria, early detection of plasmodium parasite is needed. Recently, malaria diagnosis process can be done with the help of computer, or often referred to as Computer Aided Diagnosis (CAD). By utilizing the digital image from the blood staining process, digital image processing can be performed to detect the presence of malaria parasite. There are 2 types of blood smear images that can be used in the malaria diagnosis process, namely, thin blood smear images and thick blood smear images. This paper provides a review of the techniques and methods used in the diagnosis of computer-assisted malaria using thick blood smear images as a diagnostic material.

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Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging

Cited by 46 publications

References 17 publications

Diagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks

Diagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks

A Novel Two-Stage Thresholding Method for Segmentation of Malaria Parasites in Microscopic Blood Images

Detection of malaria parasites in thick blood smear: A review

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