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A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images

In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimenta...

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Detalles Bibliográficos
Autores principales: Somasekar, J., Ramesh, G., Ramu, Gandikota, Dileep Kumar Reddy, P., Eswara Reddy, B., Lai, Ching-Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820113/
https://www.ncbi.nlm.nih.gov/pubmed/31687444
http://dx.doi.org/10.1016/j.dib.2019.104643
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author Somasekar, J.
Ramesh, G.
Ramu, Gandikota
Dileep Kumar Reddy, P.
Eswara Reddy, B.
Lai, Ching-Hao
author_facet Somasekar, J.
Ramesh, G.
Ramu, Gandikota
Dileep Kumar Reddy, P.
Eswara Reddy, B.
Lai, Ching-Hao
author_sort Somasekar, J.
collection PubMed
description In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.
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spelling pubmed-68201132019-11-04 A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images Somasekar, J. Ramesh, G. Ramu, Gandikota Dileep Kumar Reddy, P. Eswara Reddy, B. Lai, Ching-Hao Data Brief Computer Science In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging. Elsevier 2019-10-12 /pmc/articles/PMC6820113/ /pubmed/31687444 http://dx.doi.org/10.1016/j.dib.2019.104643 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Computer Science
Somasekar, J.
Ramesh, G.
Ramu, Gandikota
Dileep Kumar Reddy, P.
Eswara Reddy, B.
Lai, Ching-Hao
A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title_full A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title_fullStr A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title_full_unstemmed A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title_short A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images
title_sort dataset for automatic contrast enhancement of microscopic malaria infected blood rgb images
topic Computer Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820113/
https://www.ncbi.nlm.nih.gov/pubmed/31687444
http://dx.doi.org/10.1016/j.dib.2019.104643
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