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New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Kather JN, Weis CA, Marx A, Schuster AK, Schad LR, Zöllner FG - PLoS ONE (2015)

Bottom Line: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors.We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.

ABSTRACT

Background: Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and results: In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context: Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

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Related in: MedlinePlus

Foreground-background contrast in phantom images can be markedly increased by applying a new color map.A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.
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pone.0145572.g004: Foreground-background contrast in phantom images can be markedly increased by applying a new color map.A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.

Mentions: In this figure, bivariate color maps are compared to the full sRGB gamut in CIELAB color space. A) Original color map extracted from the sample image. B) Set of 5 optimized color maps in the same view (color maps from Fig 4A–4E). It can be seen that the improved color maps occupy a larger part of the perceivable color space, maximizing visually transmittable information.


New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Kather JN, Weis CA, Marx A, Schuster AK, Schad LR, Zöllner FG - PLoS ONE (2015)

Foreground-background contrast in phantom images can be markedly increased by applying a new color map.A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696851&req=5

pone.0145572.g004: Foreground-background contrast in phantom images can be markedly increased by applying a new color map.A-F) A phantom image was colored in one of six bivariate color maps, the panel title indicate the hexadecimal codes of the foreground and background color (F represents a typical blue—brown standard color map while A-E represent new color combinations). G) 225 combinations of 15 distinct colors were pairwisely compared. The grayscale intensity and overlayed number indicate the perceptual contrast of phantom images that were digitally stained with the respective color map (numbers: mean ± standard deviation). All measurements were normalized to the maximum contrast. Reading example: The standard brown (#B58C70) on blue (#5C5FA1) color map resulted in a perceptual contrast that was 39% ± 7% of the maximally achieved contrast.
Mentions: In this figure, bivariate color maps are compared to the full sRGB gamut in CIELAB color space. A) Original color map extracted from the sample image. B) Set of 5 optimized color maps in the same view (color maps from Fig 4A–4E). It can be seen that the improved color maps occupy a larger part of the perceivable color space, maximizing visually transmittable information.

Bottom Line: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors.We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.

ABSTRACT

Background: Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and results: In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context: Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Show MeSH
Related in: MedlinePlus