This article is part of the supplement: New trends in digital pathology: Proceedings of the 9th European Congress on Telepathology and 3rd International Congress on Virtual Microscopy

Open Access Proceedings

How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)

Klaus Kayser1*, Jürgen Görtler2, Konradin Metze3, Torsten Goldmann4, Ekkehard Vollmer4, Masoud Mireskandari5, Zdravko Kosjerina6 and Gian Kayser7

Author Affiliations

1 UICC-TPCC, Institute of Pathology, Charite, Berlin, Germany

2 IBM DeepComputing, Brussels, Belgium

3 Institute of Pathology, University of Campinas, Campinas, Brasil

4 Institute of Pathology, Research Center Borstel, Borstel, Germany

5 Department of Pathology, Iran University of Medical Sciences, Tehran, Iran

6 Institute of Pathology, University Novi Sad, Novi Sad, Serbia

7 Institute of Pathology, University Freiburg, Freiburg, Germany

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Diagnostic Pathology 2008, 3(Suppl 1):S11  doi:10.1186/1746-1596-3-S1-S11

Published: 15 July 2008

Abstract

Background

Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissue-based diagnosis.

Aims

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.

Results

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 posses one singular intensity maximum in contrast to good images obtained from H&E stained slides that present with 2 – 3 intensity maxima.

Conclusion

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.