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Abstract: Aims: Quantitative image analysis of histopathology slides is becoming an important technology in diagnostic pathology. To this end, it is essential to combine a robust image analysis software with the most commonly used immunohistochemical staining methods. In this investigation, we describe a practical application of NIH ImageJ software for quantitative vascular image analysis for diaminobenzene chromogen-based CD34 immunostain in breast cancer. CD34 immunostain is in a unique position to identify lymphangiogenesis and angiogenesis simultaneously in a given tumor tissue. This investigation aims at establishing a practical quantitative vascular image analysis solution for diagnostic pathologists by using ImageJ, and CD34 immunostain. Methods and results: Tissue microarray slides containing breast cancer tissue were immunostained for CD34 for simultaneous identification of lymphatic endothelial cells (LEC) and blood vessel endothelial cells (BEC). Digital images were analyzed using NIH ImageJ software. A CD34 score was quantified for each tissue core as a percentage (CD34-positive area/area of tissue core). The mean CD34 scores were 0.24%, 0.40%, 1.30%, 2.33%, 2.64%, and 3.44% for normal breast tissue, in stage IIA, IIB, IIIA, IIIB, and IIIC breast cancer tissue cores, respectively (p <0.0001). The mean CD34 scores were 0.70% and 2.21% for lymph node-negative and lymph node-positive breast cancer patients, respectively (p <0.0001). Conclusions: ImageJ software seems to be an attractive quantitative image analysis tool for diagnostic pathology for immunohistochemistry-based applications because of its capabilities, availability, and ease of use with most image formats. Our results show the feasibility, versatility, and ease of use of ImageJ and CD34 immunohistochemistry for vascular image analysis in breast pathology. Given the prospects of novel lymphatic and vascular endothelium-targeting therapeutics in breast oncology, the practical analysis of combined LEC and BEC density described in this report could enable diagnostic pathologists to apply quantitative vascular image analysis easily in their pathology practice and translational research. [Copyright &y& Elsevier]