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Mentions: The results are partly accumulated in serial order, as demonstrated in figure 1 and figure 2. All these images have been acquired and submitted by one institute of pathology only. Extremes in image quality can clearly be seen in both images. A striking example of shading correction of hue-intensity grey value in a fluorescent stained image is shown in figure 3. Thus, even images obtained in the same institution are subject to quite large differences in formal image quality. In images acquired from H&E stained slides, the average number of statistically significant thresholds amounts to 4 – 7 covering an image area of about 60% at the beginning (table 2). Images with a low number of potential segmentation thresholds display a high number of potential, however are not always correct to identify objects (not shown). The common relationship between the different colour spaces in images obtained from H&E stain was similar in those obtained from fluorescent and immunohistochemically stained slides (DAB, PAP). A statistically significant relationship (0.05%) between the colour spaces (hue and saturation) with the intensity space could be obtained in nearly all cases, indicating a reliable staining.
How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)
Bottom Line: Immunohistochemically stained slides displayed greater shading and grey value correction.Images requiring only low standardization corrections possess at least 5 different statistically significant thresholds, which are useful for object segmentation.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.
Affiliation: UICC-TPCC, Institute of Pathology, Charite, Berlin, Germany. firstname.lastname@example.org
Abstract: Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis.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.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 possess one singular intensity maximum in contrast to good images obtained from H&E stained slides that present with 2 - 3 intensity maxima.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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