Automated P.falciparum Detection System for Post-Treatment Malaria Diagnosis Using Modified Annular Ring Ratio Method

Kareem, S.; Kale, I.; Morling, R.C.S.

doi:10.1109/uksim.2012.108

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…Parasite type Diaz et al (2009), Anggraini et al (2011a, Hanif et al (2011), Purwar et al (2011, Sio et al (2007), Elter et al (2011), Mavandadi et al (2012), Kareem et al (2012aKareem et al ( , 2012b, Vink et al (2013), Premaratne et al, (2003), Lee et al (2011), Luengo-Oroz et al (2012, Anggraini et al (2011b), Linder et al (2014) P. falciparum Ghosh et al (2011a) Das et al (2011), Abdul-Nasir et al (2012, von Mühlen (2004) P. vivax Das et al (2012Das et al ( , 2013, Prasad et al (2012), Makkapati & Rao, (2011), Ross et al (2006, Kaewkamnerd et al (2012), Yunda et al (2011), Maity et al (2012, Khan et al (2014) P. falciparum and P. vivax Suwalka et al (2012), Khan et al (2011), Soni (2011, Soni et al (2011), Komagal et al (2013), Ahirwar et al (2012), Tek et al (2006Tek et al ( , 2009Tek et al ( , 2010, Malihi et al (2013), Gitonga et al (2014), Thung & Suwardi (2011) P. falciparum, P. vivax, P. malariae and P. ovale Ma et al (2010), Proudfoot et al (2008) P. yoelii,…”

Section: Authorsmentioning

confidence: 99%

“…Toha & Ngah (2007) used histogram thresholding-based approach for segmentation of chromatin dots from thick smear images. Kareem et al (2012aKareem et al ( , 2012b introduced angular ring ratio method towards identification of P. falciparum gametocyte. Moving K-means clustering has also been used for extracting parasite infected regions (Abdul Nasir et al, 2012).…”

Section: Authorsmentioning

confidence: 99%

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Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

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Section: Authorsmentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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“…R. C. S. Morling, et.al [13] [14] detected parasites on the basis of size and morphology and has proposed an annular ring ratio method for segmentation of blood cells. Iis Hamsir Ayub Wahab, et.al [15] used different second order statistical measurement combined with the principal component analysis method for detection of malaria parasite.…”

Section: Fig 1 Different Life Stages Of Malaria Parasitementioning

confidence: 99%

“…The count of RBC is also affected if the parasite multiplication starts. Hence, while working on the detection of malaria parasite, there is a basic requirement of separation of RBC from the background and from the artifacts (WBC, platelets, dirt, dye crystals etc) [13].…”

Section: Separation and Counting Of Rbcmentioning

confidence: 99%

A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

Charpe¹,

Bairagi²,

Desarda³

et al. 2015

IJCA

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Malaria is caused due to the mosquito bite hence the parasite enter into blood through the saliva of the mosquito. The malaria parasite directly infects the red blood cells, therefore to design an automatic detection system, the red blood cells should be segmented from the artifacts and background in a microscopic image. Here in this paper watershed transform is used with the distance transform which separates even the overlapped red blood cells more efficiently, which are useful for the diagnosis of parasite and for the parasitemia too. The result shows improvement in diagnostic accuracy of detection of the parasite in Red Blood Cells and also describing the life cycle stage of the parasite. The accuracy, sensitivity and specificity achieved were as 97.7%, 97.4% and 97.7% respectively.

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“…The next task is to differentiate the WBC and RBCs. A WBC detection algorithm has been explained in [7] which estimate the presence of the WBC if the mean pixel intensity and variance falls within a threshold range. This method is effective for post treatment malaria diagnosis, where the parasitemia which is a measure of blood infection is comparatively low.…”

Section: B Wbc and Rbc Detection Algorithmmentioning

confidence: 99%

Automated malaria parasite detection in thin blood films:- A hybrid illumination and color constancy insensitive, morphological approach

Kareem

Kale

Morling

2012

2012 IEEE Asia Pacific Conference on Circuits and Systems

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obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The WestminsterResearch online digital archive at the University of Westminster aims to make the research output of the University available to a wider audience. Copyright and Moral Rights remain with the authors and/or copyright owners.Whilst further distribution of specific materials from within this archive is forbidden, you may freely distribute the URL of WestminsterResearch: ((http://westminsterresearch.wmin.ac.uk/). Abstract-This paper illustrates the automated diagnosis of malaria parasite (Plasmodium species) in microscopic images of Giemsa stained thin blood films. The procedure adapts a morphological approach for blood cell identification and uses the image features such as intensity, histogram, relative size and geometry for further analysis. Two methods of object classification have been described for parasite detection; one based on relative size and morphology and the other based on intensity variation. An analytical study on both methods has been performed further to validate the accuracy of the methods.

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Automated P.falciparum Detection System for Post-Treatment Malaria Diagnosis Using Modified Annular Ring Ratio Method

Cited by 11 publications

References 5 publications

Computational microscopic imaging for malaria parasite detection: a systematic review

Computational microscopic imaging for malaria parasite detection: a systematic review

A Novel Method for Automatic Detection of Malaria Parasite Stage in Microscopic Blood Image

Automated malaria parasite detection in thin blood films:- A hybrid illumination and color constancy insensitive, morphological approach

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