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Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium
BACKGROUND: Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image co...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375185/ https://www.ncbi.nlm.nih.gov/pubmed/22436596 http://dx.doi.org/10.1186/1746-1596-7-29 |
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author | Konsti, Juho Lundin, Mikael Linder, Nina Haglund, Caj Blomqvist, Carl Nevanlinna, Heli Aaltonen, Kirsimari Nordling, Stig Lundin, Johan |
author_facet | Konsti, Juho Lundin, Mikael Linder, Nina Haglund, Caj Blomqvist, Carl Nevanlinna, Heli Aaltonen, Kirsimari Nordling, Stig Lundin, Johan |
author_sort | Konsti, Juho |
collection | PubMed |
description | BACKGROUND: Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation. METHODS: Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images. RESULTS: Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25. CONCLUSIONS: The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995 |
format | Online Article Text |
id | pubmed-3375185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-33751852012-06-18 Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium Konsti, Juho Lundin, Mikael Linder, Nina Haglund, Caj Blomqvist, Carl Nevanlinna, Heli Aaltonen, Kirsimari Nordling, Stig Lundin, Johan Diagn Pathol Research BACKGROUND: Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation. METHODS: Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images. RESULTS: Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25. CONCLUSIONS: The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995 BioMed Central 2012-03-21 /pmc/articles/PMC3375185/ /pubmed/22436596 http://dx.doi.org/10.1186/1746-1596-7-29 Text en Copyright ©2012 Konsti 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 | Research Konsti, Juho Lundin, Mikael Linder, Nina Haglund, Caj Blomqvist, Carl Nevanlinna, Heli Aaltonen, Kirsimari Nordling, Stig Lundin, Johan Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title | Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title_full | Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title_fullStr | Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title_full_unstemmed | Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title_short | Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
title_sort | effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375185/ https://www.ncbi.nlm.nih.gov/pubmed/22436596 http://dx.doi.org/10.1186/1746-1596-7-29 |
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