Computer Based Automatic Segmentation of Pap smear Cells for Cervical Cancer Detection

Abstract

Cervical Cancer is the fourth leading cause of death due to cancer among women worldwide. Pap Smear Test is the commonly used method for Cervical Cancer screening. But Pap Smear pathology screening is very time consuming process. Therefore, an automatic detection method of nucleus of cervical cell is proposed in this paper which mainly focuses on time consumption which is an important parameter when it comes the automatic segmentation. The pre-processing is achieved using edge map with double threshold for de-noising of edges, and then segmentation of the nucleus of cervical cancer cell is achieved using Gradient Force Model and Balloon force Model. The two parametric deformable models are used to check the trade-off between the number of iterations and accuracy. Further, geometrical features like perimeter, area, eccentricity, mean intensity etc. are calculated followed by segmentation using both methods to detect whether cell is cancerous or normal. The calculated features are contrasted with each method. The experimental results shows time consumption is reduced using gradient force model in terms of number of iterations used for segmentation with the accuracy of 0.92 which is significant for clinical interpretation.