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How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)

BACKGROUND: Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis. AIMS: To describe the theoretical background and the practical experiences in automated image quality est...

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Autores principales: Kayser, Klaus, Görtler, Jürgen, Metze, Konradin, Goldmann, Torsten, Vollmer, Ekkehard, Mireskandari, Masoud, Kosjerina, Zdravko, Kayser, Gian
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2500119/
https://www.ncbi.nlm.nih.gov/pubmed/18673499
http://dx.doi.org/10.1186/1746-1596-3-S1-S11
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author Kayser, Klaus
Görtler, Jürgen
Metze, Konradin
Goldmann, Torsten
Vollmer, Ekkehard
Mireskandari, Masoud
Kosjerina, Zdravko
Kayser, Gian
author_facet Kayser, Klaus
Görtler, Jürgen
Metze, Konradin
Goldmann, Torsten
Vollmer, Ekkehard
Mireskandari, Masoud
Kosjerina, Zdravko
Kayser, Gian
author_sort Kayser, Klaus
collection PubMed
description BACKGROUND: Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis. AIMS: To describe the theoretical background and the practical experiences in automated image quality estimation of colour images acquired from histological slides. THEORY, MATERIAL AND MEASUREMENTS: Digital images acquired from histological slides should present with textures and objects that permit automated image information analysis. The quality of digitized images can be estimated by spatial independent and local filter operations that investigate in homogenous brightness, low peak to noise ratio (full range of available grey values), maximum gradients, equalized grey value distribution, and existence of grey value thresholds. Transformation of the red-green-blue (RGB) space into the hue-saturation-intensity (HSI) space permits the detection of colour and intensity maxima/minima. The feature distance of the original image to its standardized counterpart is an appropriate measure to quantify the actual image quality. These measures have been applied to a series of H&E stained, fluorescent (DAPI, Texas Red, FITC), and immunohistochemically stained (PAP, DAB) slides. More than 5,000 slides have been measured and partly analyzed in a time series. RESULTS: Analysis of H&E stained slides revealed low shading corrections (10%) and moderate grey value standardization (10 – 20%) in the majority of cases. Immunohistochemically stained slides displayed greater shading and grey value correction. Fluorescent stained slides are often revealed to high brightness. Images requiring only low standardization corrections possess at least 5 different statistically significant thresholds, which are useful for object segmentation. Fluorescent images of good quality only posses one singular intensity maximum in contrast to good images obtained from H&E stained slides that present with 2 – 3 intensity maxima. CONCLUSION: Evaluation of image quality and creation of formally standardized images should be performed prior to automatic analysis of digital images acquired from histological slides. Spatial dependent and local filter operations as well as analysis of the RGB and HSI spaces are appropriate methods to reproduce evaluated formal image quality.
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spelling pubmed-25001192008-08-08 How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology) Kayser, Klaus Görtler, Jürgen Metze, Konradin Goldmann, Torsten Vollmer, Ekkehard Mireskandari, Masoud Kosjerina, Zdravko Kayser, Gian Diagn Pathol Proceedings BACKGROUND: Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis. AIMS: To describe the theoretical background and the practical experiences in automated image quality estimation of colour images acquired from histological slides. THEORY, MATERIAL AND MEASUREMENTS: Digital images acquired from histological slides should present with textures and objects that permit automated image information analysis. The quality of digitized images can be estimated by spatial independent and local filter operations that investigate in homogenous brightness, low peak to noise ratio (full range of available grey values), maximum gradients, equalized grey value distribution, and existence of grey value thresholds. Transformation of the red-green-blue (RGB) space into the hue-saturation-intensity (HSI) space permits the detection of colour and intensity maxima/minima. The feature distance of the original image to its standardized counterpart is an appropriate measure to quantify the actual image quality. These measures have been applied to a series of H&E stained, fluorescent (DAPI, Texas Red, FITC), and immunohistochemically stained (PAP, DAB) slides. More than 5,000 slides have been measured and partly analyzed in a time series. RESULTS: Analysis of H&E stained slides revealed low shading corrections (10%) and moderate grey value standardization (10 – 20%) in the majority of cases. Immunohistochemically stained slides displayed greater shading and grey value correction. Fluorescent stained slides are often revealed to high brightness. Images requiring only low standardization corrections possess at least 5 different statistically significant thresholds, which are useful for object segmentation. Fluorescent images of good quality only posses one singular intensity maximum in contrast to good images obtained from H&E stained slides that present with 2 – 3 intensity maxima. CONCLUSION: Evaluation of image quality and creation of formally standardized images should be performed prior to automatic analysis of digital images acquired from histological slides. Spatial dependent and local filter operations as well as analysis of the RGB and HSI spaces are appropriate methods to reproduce evaluated formal image quality. BioMed Central 2008-07-15 /pmc/articles/PMC2500119/ /pubmed/18673499 http://dx.doi.org/10.1186/1746-1596-3-S1-S11 Text en Copyright © 2008 Kayser et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Kayser, Klaus
Görtler, Jürgen
Metze, Konradin
Goldmann, Torsten
Vollmer, Ekkehard
Mireskandari, Masoud
Kosjerina, Zdravko
Kayser, Gian
How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title_full How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title_fullStr How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title_full_unstemmed How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title_short How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
title_sort how to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2500119/
https://www.ncbi.nlm.nih.gov/pubmed/18673499
http://dx.doi.org/10.1186/1746-1596-3-S1-S11
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