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Systematic segmentation method based on PCA of image hue features for white blood cell counting
Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719728/ https://www.ncbi.nlm.nih.gov/pubmed/34972155 http://dx.doi.org/10.1371/journal.pone.0261857 |
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author | Garcia-Lamont, Farid Alvarado, Matias Cervantes, Jair |
author_facet | Garcia-Lamont, Farid Alvarado, Matias Cervantes, Jair |
author_sort | Garcia-Lamont, Farid |
collection | PubMed |
description | Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works. |
format | Online Article Text |
id | pubmed-8719728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87197282022-01-01 Systematic segmentation method based on PCA of image hue features for white blood cell counting Garcia-Lamont, Farid Alvarado, Matias Cervantes, Jair PLoS One Research Article Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works. Public Library of Science 2021-12-31 /pmc/articles/PMC8719728/ /pubmed/34972155 http://dx.doi.org/10.1371/journal.pone.0261857 Text en © 2021 Garcia-Lamont et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Garcia-Lamont, Farid Alvarado, Matias Cervantes, Jair Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title | Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title_full | Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title_fullStr | Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title_full_unstemmed | Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title_short | Systematic segmentation method based on PCA of image hue features for white blood cell counting |
title_sort | systematic segmentation method based on pca of image hue features for white blood cell counting |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719728/ https://www.ncbi.nlm.nih.gov/pubmed/34972155 http://dx.doi.org/10.1371/journal.pone.0261857 |
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