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Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm
Most of the erythrocyte related diseases are detectable by hematology images analysis. At the first step of this analysis, segmentation and detection of blood cells are inevitable. In this study, a novel method using a line operator and watershed algorithm is rendered for erythrocyte detection and s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3959006/ https://www.ncbi.nlm.nih.gov/pubmed/24672764 |
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author | Khajehpour, Hassan Dehnavi, Alireza Mehri Taghizad, Hossein Khajehpour, Esmat Naeemabadi, Mohammadreza |
author_facet | Khajehpour, Hassan Dehnavi, Alireza Mehri Taghizad, Hossein Khajehpour, Esmat Naeemabadi, Mohammadreza |
author_sort | Khajehpour, Hassan |
collection | PubMed |
description | Most of the erythrocyte related diseases are detectable by hematology images analysis. At the first step of this analysis, segmentation and detection of blood cells are inevitable. In this study, a novel method using a line operator and watershed algorithm is rendered for erythrocyte detection and segmentation in blood smear images, as well as reducing over-segmentation in watershed algorithm that is useful for segmentation of different types of blood cells having partial overlap. This method uses gray scale structure of blood cell, which is obtained by exertion of Euclidian distance transform on binary images. Applying this transform, the gray intensity of cell images gradually reduces from the center of cells to their margins. For detecting this intensity variation structure, a line operator measuring gray level variations along several directional line segments is applied. Line segments have maximum and minimum gray level variations has a special pattern that is applicable for detections of the central regions of cells. Intersection of these regions with the signs which are obtained by calculating of local maxima in the watershed algorithm was applied for cells’ centers detection, as well as a reduction in over-segmentation of watershed algorithm. This method creates 1300 sign in segmentation of 1274 erythrocytes available in 25 blood smear images. Accuracy and sensitivity of the proposed method are equal to 95.9% and 97.99%, respectively. The results show the proposed method's capability in detection of erythrocytes in blood smear images. |
format | Online Article Text |
id | pubmed-3959006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-39590062014-03-26 Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm Khajehpour, Hassan Dehnavi, Alireza Mehri Taghizad, Hossein Khajehpour, Esmat Naeemabadi, Mohammadreza J Med Signals Sens Original Article Most of the erythrocyte related diseases are detectable by hematology images analysis. At the first step of this analysis, segmentation and detection of blood cells are inevitable. In this study, a novel method using a line operator and watershed algorithm is rendered for erythrocyte detection and segmentation in blood smear images, as well as reducing over-segmentation in watershed algorithm that is useful for segmentation of different types of blood cells having partial overlap. This method uses gray scale structure of blood cell, which is obtained by exertion of Euclidian distance transform on binary images. Applying this transform, the gray intensity of cell images gradually reduces from the center of cells to their margins. For detecting this intensity variation structure, a line operator measuring gray level variations along several directional line segments is applied. Line segments have maximum and minimum gray level variations has a special pattern that is applicable for detections of the central regions of cells. Intersection of these regions with the signs which are obtained by calculating of local maxima in the watershed algorithm was applied for cells’ centers detection, as well as a reduction in over-segmentation of watershed algorithm. This method creates 1300 sign in segmentation of 1274 erythrocytes available in 25 blood smear images. Accuracy and sensitivity of the proposed method are equal to 95.9% and 97.99%, respectively. The results show the proposed method's capability in detection of erythrocytes in blood smear images. Medknow Publications & Media Pvt Ltd 2013 /pmc/articles/PMC3959006/ /pubmed/24672764 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Khajehpour, Hassan Dehnavi, Alireza Mehri Taghizad, Hossein Khajehpour, Esmat Naeemabadi, Mohammadreza Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title | Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title_full | Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title_fullStr | Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title_full_unstemmed | Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title_short | Detection and Segmentation of Erythrocytes in Blood Smear Images Using a Line Operator and Watershed Algorithm |
title_sort | detection and segmentation of erythrocytes in blood smear images using a line operator and watershed algorithm |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3959006/ https://www.ncbi.nlm.nih.gov/pubmed/24672764 |
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