Evaluation of Pap Smear Nucleus Cell Image Segmentation: The Impact of Enhancement Processes on Segmentation Result - Esron Nainggolan; Nita Merlina; Farisya Setiadi (SAFE-Network, 2026)
Accurate nucleus segmentation is vital for automated cervical cancer diagnosis, yet it remains challenging due to overlapping cells and uneven lighting. This study evaluates Polynomial Contrast Enhancement (PCE) using a second-degree polynomial function to improve segmentation on 100 images from the RepomedUNM dataset. The pipeline integrates grayscale conversion, Gaussian blur, and PCE prior to Canny edge detection. Results demonstrate near-perfect Precision (0.9999–1.0000) across all categories (Normal, H-SIL, L-SIL, and Koilocyt), effectively eliminating false positives. However, Recall and Accuracy remained low (max 0.0634 in H-SIL), a technical consequence of Canny’s limitation in capturing thin boundaries versus solid nuclear areas. The study’s novelty lies in the application of second-degree PCE to stabilize intensity variations across multiple diagnostic categories. While PCE ensures exceptional localization precision, future systems should integrate deep learning to enhance recall in complex overlapping structures.