Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen

SUMMARY: Image processing techniques are being widely developed for helping specialists in analysis of histological images and its application is especially useful in obtaining numerical data for the realization of the subsequent statistical analysis. The use of these methods makes the histological...

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Autores principales:	Salinas,Paulo, Sanhueza,Jorge, Sandoval,Carlos
Lenguaje:	English
Publicado:	Sociedad Chilena de Anatomía 2018
Materias:	Collagen Stereology Segmentation Image Analysis Histology
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-95022018000301118
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spelling	oai:scielo:S0717-950220180003011182019-09-16Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of CollagenSalinas,PauloSanhueza,JorgeSandoval,Carlos Collagen Stereology Segmentation Image Analysis Histology SUMMARY: Image processing techniques are being widely developed for helping specialists in analysis of histological images and its application is especially useful in obtaining numerical data for the realization of the subsequent statistical analysis. The use of these methods makes the histological analysis of experts more objective and less time-consuming. In this paper we evaluate how well the quantitative methods - color-based image segmentation and stereology - agree on average, and how well they agree for the individuals when they are used to quantify type I and III collagen. Digital images of sections of Salmo salar jaws (5 µm, SiriusRed staining) were analyzed. Collagen quantification was performed by two methods in the same group of images: i) Color Based-Segmentation (K-means algorithm; pixel cluster; ImageJ32 v1.51p) and ii) Stereology (VV; M36; STEPanizer Stereological Tools). They were evaluated 200 images per group. The difference between groups and concordance was analyzed using t-Student (p<0.05) and Blant Altman Comparison Method, respectively. The data analysis of average and individual assessments showed that there is concordance between two methods. In conclusion, stereology and color-based image segmentation are powerful tools which quantify collagen in histological sections.info:eu-repo/semantics/openAccessSociedad Chilena de AnatomíaInternational Journal of Morphology v.36 n.3 20182018-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-95022018000301118en10.4067/S0717-95022018000301118
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Collagen Stereology Segmentation Image Analysis Histology
spellingShingle	Collagen Stereology Segmentation Image Analysis Histology Salinas,Paulo Sanhueza,Jorge Sandoval,Carlos Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
description	SUMMARY: Image processing techniques are being widely developed for helping specialists in analysis of histological images and its application is especially useful in obtaining numerical data for the realization of the subsequent statistical analysis. The use of these methods makes the histological analysis of experts more objective and less time-consuming. In this paper we evaluate how well the quantitative methods - color-based image segmentation and stereology - agree on average, and how well they agree for the individuals when they are used to quantify type I and III collagen. Digital images of sections of Salmo salar jaws (5 µm, SiriusRed staining) were analyzed. Collagen quantification was performed by two methods in the same group of images: i) Color Based-Segmentation (K-means algorithm; pixel cluster; ImageJ32 v1.51p) and ii) Stereology (VV; M36; STEPanizer Stereological Tools). They were evaluated 200 images per group. The difference between groups and concordance was analyzed using t-Student (p<0.05) and Blant Altman Comparison Method, respectively. The data analysis of average and individual assessments showed that there is concordance between two methods. In conclusion, stereology and color-based image segmentation are powerful tools which quantify collagen in histological sections.
author	Salinas,Paulo Sanhueza,Jorge Sandoval,Carlos
author_facet	Salinas,Paulo Sanhueza,Jorge Sandoval,Carlos
author_sort	Salinas,Paulo
title	Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
title_short	Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
title_full	Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
title_fullStr	Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
title_full_unstemmed	Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen
title_sort	color-based segmentation vs. stereology: a simple comparison between two semi-automated methods of image analysis for the quantification of collagen
publisher	Sociedad Chilena de Anatomía
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-95022018000301118
work_keys_str_mv	AT salinaspaulo colorbasedsegmentationvsstereologyasimplecomparisonbetweentwosemiautomatedmethodsofimageanalysisforthequantificationofcollagen AT sanhuezajorge colorbasedsegmentationvsstereologyasimplecomparisonbetweentwosemiautomatedmethodsofimageanalysisforthequantificationofcollagen AT sandovalcarlos colorbasedsegmentationvsstereologyasimplecomparisonbetweentwosemiautomatedmethodsofimageanalysisforthequantificationofcollagen
_version_	1718445062651641856

Color-Based Segmentation vs. Stereology: A Simple Comparison Between Two Semi-Automated Methods of Image Analysis for the Quantification of Collagen

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