Computer aided detection and classification of Pap smear cell images using principal component analysis

Pap smear is a screening methodology employed in cervix cancer detection and diagnosis. The Pap smear images of cervical region are used to detect the abnormality of the cervical cells. In this paper, the computer aided automatic detection and classification method for Pap smear cell images are proposed. The cell nucleus is segmented using watershed segmentation methodology and features are extracted from segmented cell nucleus Pap smear image. The extracted features are classified using principal component analysis method. The proposed system classifies the test Pap smear cell image into dysplastic (D), parabasal (P) and superficial (S) cell images for cervical cancer diagnosis.