Detection of Cancerous Lesion by Uterine Cervix Image Segmentation

This paper works at segmentation of lesion observed in cervical cancer, which is the second most common cancer among women worldwide. The purpose of segmentation is to determine the location for a biopsy to be taken for diagnosis. Cervix cancer is a disease in which cancer cells are found in the tissues of the cervix. The acetowhite region is a major indicator of abnormality in the cervix image. This project addresses the problem of segmenting uterine cervix image into different regions. We analyze two algorithms namely Watershed, K-means clustering algorithm, Expectation Maximization (EM) Image Segmentation algorithm. These segmentations methods are carried over for the colposcopic uterine cervix image.