A multistep image analysis method to increase automated identification efficiency in immunohistochemical nuclear markers with a high background level

Background In anatomical and surgical pathology, the customary method of manual observation and measurement of immunohistochemically stained markers from microscopic images is tedious, expensive and time consuming. There is great demand for automated procedures for analyzing digital images (DIs) of these markers [1] given that they reduce human variability in the evaluation of stained markers [2,3] and increase the speed and efficiency of the analysis [4]. Computerized DI analysis software generally involves a stained objects/nuclei segmentation method to detect and quantify the number of positively stained markers in combination with the standard evaluation of their morphometric and/or densitometric features [5,6]. However, automatic segmentation often fails due to the presence of spurious stain deposits in tissue sections (background). The “removal” of the background from noisy DIs, so that only the objects of interest are identified, is difficult due to the color values of pixels in the nuclei and background overlapping during the color segmentation processes. We previously developed an automated macro that allows quantification of several nuclear markers in various neoplasic tissues [7]. In an attempt to standardize the immunohistochemical analysis and to improve cell detection, we propose a new procedure that quantifies only positively stained nuclei even though they have a similar color to that of the surrounding tissue. The aim of this work was to develop a single automated procedure that allows images to be analyzed irrespective of whether the spurious stain deposit in background is present or absent. The multistep process includes algorithms that permit this discrimination so that the appropriate procedure for optimal quantification can then be applied.