Supervised segmentation of overlapping cervical pap smear images

Abstract

Overlapping of cervical cancerous cells and presence of debris, mucus and blood play a major issue in accurate segmentation of cells. Manual screening of overlapped cells in Pap smear slides is prone to error due to the complexity, high variation in shape and size and poor contrast of images. The automated system must be able to detect the nucleus and cytoplasm of clumped cells accurately as merging of cells is a characteristic of high stages of cervical cancer. In this paper, we propose a novel method to accurately segment the overlapping cells by dividing the whole image into many small non-overlapping pixel blocks, then extracting the texture features from Gray level co-occurrence matrix GLCM. The overlapped parts have a noticeable change in certain features which help us in selecting the area of interest which is marked explicitly and further the contours are marked using Independent level set method, accurately segmenting the cell nucleus and cytoplasm.