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New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.
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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.
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PubMed Central - PubMed
Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.
ABSTRACT
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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. Related in: MedlinePlus |
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pone.0145572.g003: New color maps contain more colors than the original color map.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. Mentions: First, we examined the standard blue—brown color map in CIELAB space as shown in Fig 3A. It can be seen that compared to the full RGB gamut, this color map is small, i.e. contains only a small subset of colors. We strived to create an improved color map. We created alternative color maps as contiguous 2D planes in the 3D CIELAB space (Fig 3B). Our aim was to create a color map that was larger than the original color map, i.e. contained more visually distinct colors. Apart from the color map size, we defined the following criteria for an optimized color map: |
View Article: PubMed Central - PubMed
Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.
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.
