Automated Algorithm for Segmentation of Pap Smear Image for Cervical Cancer Detection

Cervical cancer is the second most common gynecologic cancer worldwide. Unlike the other cancers it does not show any symptoms in its earlier stage which causes mortality among women. It takes 8 to 10 years to develop from precancerous to severe stage. The important reasons for higher cervical cancer in developing countries are lack of resources, lack of effectual screening programs and inadequately organized health system aimed for detecting precancerous condition before they progress to persistent cancer. So, there is a need of low cost approach for effective cervical cancer screening programs .Cervical cancer is a disease that can be prevented through both primary prevention and early detection. So in developed countries the incidence of cervical cancer has decreased due to screening, early detection and treatment. However in developing countries, 80% of cervical cancers are incurable at the time of detection due to their advanced stage. Several screening modalities are now available for early detection of cervical cancer and its precursor lesions. When cervical cancer occurs, cells in the cervix region cannot function normally and form tumours which destroys the normal cells. It can be easily detected and preventable in its earlier stage with pap smear test. Many screening techniques are introduced for finding the abnormal cells . Pap smear test is also a screening test from which smear is taken from cervix and examined under the microscope. In automatic cervical cancer cell segmentation, a single cell image is segmented into nucleus and non nucleus region. In this paper Fuzzy C-means (FCM) clustering technique is proposed for single cell segmentation.