Purpose <p>Early identification of pre-cancerous cervical epithelial changes is clinically critical yet often relies on subjective visual assessment. Quantitative, reproducible imaging biomarkers are therefore required to provide an objective assessment and facilitate artificial intelligence (AI) based automated diagnosis in precancerous cervical screening.</p> Methods <p>This study attempts to identify imaging biomarkers to objectively characterize and differentiate structural changes associated with pre-cancerous cells. The optical microscopic images of superficial and precancerous koilocytotic cells are obtained from a publicly available Pap smear image database with pre-annotated polygon boundaries. Initially, the boundary inconsistencies and minor boundary intersections are corrected in the images. Features such as nuclear area, total cell area, cytoplasm area, nucleus-cytoplasm (NC) ratio, and bending energy are computed from the identified boundaries. To further evaluate the potential for automated differentiation, a logistic regression classification model is employed, and its performance is assessed.</p> Results <p>Results confirm nuclear enlargement, variation in cytoplasmic space, elevated NC ratios in koilocytes compared with superficial cells, and alterations in nuclear bending energy, attributed to nuclear irregularity, cellular enlargement, and a perinuclear halo. These characteristic features arising due to the effects of human papillomavirus are found to be significant in discriminating koilocytotic cells from superficial cells. The logistic regression model achieved an average five-fold cross-validation weighted F1-score of 85.59% and balanced accuracy of 86.01%.</p> Conclusion <p>This preliminary study proposes morphometric biomarkers that appear to provide size and magnification-invariant descriptors for early detection of precancerous cellular changes and could enable AI based automated diagnosis without reliance on subjective assessment.</p>