Detection of abnormal regions of precancerous lesions in digitised uterine Cervix images

Invasive uterine cervical cancer is a prevalent cause of cancer-related mortality and morbidity among women in the developing world. In this paper, a survey of current image segmentation methods and their possible applications to identify Cervical Intraepithelial Neoplasia (CIN) are discussed. Approaches to Cervix image segmentation and analysis are discussed. A very efficient algorithm for segmentation of abnormal regions of cancerous cervical lesions is developed, verified and compared with other existing methodologies. Several image processing methodologies and mathematical operations are exploited and applied to this research work. Although the success of the applied algorithms is highly dependent on the quality of the images used, statistical results regarding the feature extraction, running time and pattern classification are obtained and found to be quite satisfactory. This efficient automatic segmentation and pattern classification methodology will highly append content based image retrieval from database of cervix image and has the potential of playing a significant role to the development of an image-based screening tool for Cervical Cancer.