Digital pathology and image analysis have developed extensively during the last couple of years. Especially the advance in whole-slide scanning, software, and computer processing makes it possible to apply these methods in tissue-based research. Today this task is dominated by tedious manual assessments by pathologists with the interobserver and intraobserver variation this includes. Automated quantitative assessment of immunohistochemical staining has the potential to objectively extract numerical measures from cell and tissue structures, and allows efficient high throughput analysis in clinical research. Published data of manual cell counts in psoriatic skin samples were in this study reevaluated using the digital image analysis (DIA) software. Whole slides immunohistochemically stained for CD3, CD4, CD8, CD45R0, and Ki-67 were scanned and quantitatively evaluated using simple threshold analysis. Regression analysis with R values in the range of 0.85 to 0.95 indicates a good correlation between the manual count of cell numbers and the staining density obtained by automated DIA. Moreover, we show that the automated image analysis is reliable over a broad range of thresholds and that it is robust to differences in staining intensities and hence useful for high throughput analysis. DIA is a viable technical approach for automated cell quantification. Its output highly correlates to the conventional manual cell counting and hence allows for increasing the throughput and reducing the analysis time significantly.