Breast cancer is the most common diagnosed cancer in female over the world. It is the most important cause of 'cancer death' among female. For diagnosis of breast masses the Fine needle aspiration cytology (FNAC) is applied as the primary tool. Though it is an easy, rapid and comparatively minimally invasive and inexpensive procedure for quick diagnosis, it is largely a subjective tool. Computer-assisted image morphometry provides a new influential method for high-precision measurement of nuclear features and can help to differentiate between benign and malignant breast aspirates. Therefore, the aim of study was to evaluate computer assisted semi-automatic nuclear morphometry on cytology of breast lesions to differentiate benign and malignant lesions of breast. Total 70 cases were included in this cross sectional study, performed in the department of Pathology, Mymensingh Medical College, Mymensingh, Bangladesh from March 2019 to February 2021. Morphometric analysis was done on images captured from FNAC slides of the selected cases. Image J Morphometric Software was used for image analysis. Statistical analysis was done by using SPSS 26.0 version. On analysis of morphometry, four nuclear size parameters, two nuclear shape parameters and one nuclear chromasia parameter were measured. The cut-off values with sensitivity and specificity between benign and malignant category for mean nuclear area were 61.54μm², 91.0%, 97.0%, for mean maximum feret diameter 10.89μm, 94.0%, 95.0%, for mean minimum feret diameter 7.71μm, 94.0%, 97.0% and for mean nuclear perimeter 33.32μm, 94.0%, 95.0% respectively. So, in this observation, morphometry is adjunctive tools which may overcome the limitations of inter observer agreement, improve diagnostic accuracy and avoid unnecessary repeat Fine Needle Aspiration (FNA) in breast aspirates.