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Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium.

Konsti J, Lundin M, Linder N, Haglund C, Blomqvist C, Nevanlinna H, Aaltonen K, Nordling S, Lundin J - Diagn Pathol (2012)

Bottom Line: To reduce the need of extensive data storage systems image files can be compressed and scaled down.Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.Both of the studied image analysis methods showed good agreement between visual and automated results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. juho.konsti@helsinki.fi

ABSTRACT

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.

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Histograms of automated tumor segmentation scores (blue denotes stromal and red colorectal cancer epithelial images as determined by visual assessment; negative x-axis values correspond to stroma and positive values to epithelium by decision of automated method).
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Figure 5: Histograms of automated tumor segmentation scores (blue denotes stromal and red colorectal cancer epithelial images as determined by visual assessment; negative x-axis values correspond to stroma and positive values to epithelium by decision of automated method).

Mentions: The histogram of automated tumor segmentation scores is shown in Figure 5. The stromal and epithelial images form two distinct peaks in the histogram. When compression level increases, two separate peaks remain but the epithelial peak moves towards the stromal side leading to an increasing number of misclassified images. The effect of this can be observed as a sharp decrease in percentage agreement and kappa values between compression ratios 1:25 and 1:50 (Table 6). By lowering the decision threshold from zero, the discrimination accuracy of the current algorithm can be retained, but would require a re-calibration procedure to be integrated with the algorithm.


Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium.

Konsti J, Lundin M, Linder N, Haglund C, Blomqvist C, Nevanlinna H, Aaltonen K, Nordling S, Lundin J - Diagn Pathol (2012)

Histograms of automated tumor segmentation scores (blue denotes stromal and red colorectal cancer epithelial images as determined by visual assessment; negative x-axis values correspond to stroma and positive values to epithelium by decision of automated method).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3375185&req=5

Figure 5: Histograms of automated tumor segmentation scores (blue denotes stromal and red colorectal cancer epithelial images as determined by visual assessment; negative x-axis values correspond to stroma and positive values to epithelium by decision of automated method).
Mentions: The histogram of automated tumor segmentation scores is shown in Figure 5. The stromal and epithelial images form two distinct peaks in the histogram. When compression level increases, two separate peaks remain but the epithelial peak moves towards the stromal side leading to an increasing number of misclassified images. The effect of this can be observed as a sharp decrease in percentage agreement and kappa values between compression ratios 1:25 and 1:50 (Table 6). By lowering the decision threshold from zero, the discrimination accuracy of the current algorithm can be retained, but would require a re-calibration procedure to be integrated with the algorithm.

Bottom Line: To reduce the need of extensive data storage systems image files can be compressed and scaled down.Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.Both of the studied image analysis methods showed good agreement between visual and automated results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. juho.konsti@helsinki.fi

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus