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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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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. |
format | Online Article Text |
id | pubmed-6820113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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