Automated and unsupervised detection of malarial parasites in microscopic images
Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the nu...
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ftdoajarticles:oai:doaj.org/article:ecd1c7bc3ac5483cb23dcc97894741e3 2023-05-15T15:16:10+02:00 Automated and unsupervised detection of malarial parasites in microscopic images Purwar Yashasvi Shah Sirish L Clarke Gwen Almugairi Areej Muehlenbachs Atis 2011-12-01T00:00:00Z https://doi.org/10.1186/1475-2875-10-364 https://doaj.org/article/ecd1c7bc3ac5483cb23dcc97894741e3 EN eng BMC http://www.malariajournal.com/content/10/1/364 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-10-364 1475-2875 https://doaj.org/article/ecd1c7bc3ac5483cb23dcc97894741e3 Malaria Journal, Vol 10, Iss 1, p 364 (2011) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2011 ftdoajarticles https://doi.org/10.1186/1475-2875-10-364 2022-12-31T08:31:47Z Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 10 1 |
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Directory of Open Access Journals: DOAJ Articles |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Purwar Yashasvi Shah Sirish L Clarke Gwen Almugairi Areej Muehlenbachs Atis Automated and unsupervised detection of malarial parasites in microscopic images |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria. Method A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives. Results The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites. Conclusion Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite ... |
format |
Article in Journal/Newspaper |
author |
Purwar Yashasvi Shah Sirish L Clarke Gwen Almugairi Areej Muehlenbachs Atis |
author_facet |
Purwar Yashasvi Shah Sirish L Clarke Gwen Almugairi Areej Muehlenbachs Atis |
author_sort |
Purwar Yashasvi |
title |
Automated and unsupervised detection of malarial parasites in microscopic images |
title_short |
Automated and unsupervised detection of malarial parasites in microscopic images |
title_full |
Automated and unsupervised detection of malarial parasites in microscopic images |
title_fullStr |
Automated and unsupervised detection of malarial parasites in microscopic images |
title_full_unstemmed |
Automated and unsupervised detection of malarial parasites in microscopic images |
title_sort |
automated and unsupervised detection of malarial parasites in microscopic images |
publisher |
BMC |
publishDate |
2011 |
url |
https://doi.org/10.1186/1475-2875-10-364 https://doaj.org/article/ecd1c7bc3ac5483cb23dcc97894741e3 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 10, Iss 1, p 364 (2011) |
op_relation |
http://www.malariajournal.com/content/10/1/364 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-10-364 1475-2875 https://doaj.org/article/ecd1c7bc3ac5483cb23dcc97894741e3 |
op_doi |
https://doi.org/10.1186/1475-2875-10-364 |
container_title |
Malaria Journal |
container_volume |
10 |
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1 |
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1766346469072175104 |