Segmentation of Stretched Pap Smear Cytology Images using Clustering Algorithm

Papanicolaou test or better known as Pap test is the most popular and effective screening test for cervical cancer. At time, however, the detection of abnormal or cancerous cervical cells can be missed due to technical and human errors. In certain cases, the Pap smear images are blurred and highly affected by unwanted noises. These factors can hide and obscure the important cervical cells morphologies thus increasing the rate of false diagnosis rate. In this study a segmentation technique for Pap smear cytology images is proposed. The proposed technique begins by applying stretching process to enhance the contrast of Pap smear cytology images. The stretched Pap smear cytology images will then be segmented into three cervical cell’s structures; nucleus, cytoplasm and background. The results show that the proposed segmentation technique produced better segmentation performance compared to the conventional clustering algorithms. The size and shape of cervical cells are also maintained through the proposed technique. This effort will assist pathologists for easier and better cervical cells morphological extraction.