UICC-TPCC, Institute of Pathology, Charite, Berlin, Germany.
Diagn Pathol. 2008 Jul 15;3 Suppl 1(Suppl 1):S11. doi: 10.1186/1746-1596-3-S1-S11.
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.
自动化图像分析、虚拟切片的测量以及开放获取的电子测量用户系统需要对基于组织的诊断中的图像质量进行标准化评估。
描述从组织学切片获取的彩色图像的自动化图像质量评估的理论背景和实践经验。
理论、材料和测量：从组织学切片获取的数字图像应呈现出允许自动化图像信息分析的纹理和对象。数字化图像的质量可以通过空间独立和局部滤波操作来估计，这些操作可以研究均匀亮度、低峰值噪声比（可用灰度值的全范围）、最大梯度、均衡灰度值分布以及灰度值阈值的存在。将红-绿-蓝（RGB）空间转换为色调-饱和度-强度（HSI）空间可以检测到颜色和强度的最大值/最小值。原始图像到其标准化对应物的特征距离是量化实际图像质量的适当度量。这些措施已应用于一系列 H&E 染色、荧光（DAPI、Texas Red、FITC）和免疫组织化学染色（PAP、DAB）的切片。已经对超过 5000 张切片进行了测量，并在时间序列中进行了部分分析。
对 H&E 染色的切片进行分析发现，大多数情况下，阴影校正较低（10%），灰度值标准化中等（10-20%）。免疫组织化学染色的切片显示出更大的阴影和灰度值校正。荧光染色的切片通常显示出较高的亮度。仅需要低标准化校正的图像具有至少 5 个不同的统计学显著阈值，这些阈值对于目标分割很有用。质量良好的荧光图像只有一个单一的强度最大值，而质量良好的 H&E 染色图像则有 2-3 个强度最大值。
在对从组织学切片获取的数字图像进行自动分析之前，应先评估图像质量并创建正式标准化的图像。空间依赖和局部滤波操作以及 RGB 和 HSI 空间的分析是再现评估的正式图像质量的适当方法。
