Early Detection of Cervical Cancer Using Novel Segmentation Algorithms

Technology in healthcare is one of the emerging applications areas. Women in remote areas of developing nations are subject to lack of adequate healthcare facilities and specialist doctors. One of the commonest forms of cancer in women worldwide is Uterine Cervical Cancer. Most cases of cervical cancer can be prevented through screening programs aimed at detecting precancerous lesions. Due to lack of the gynecooncologists(specialist doctors) in those resource less regions, the women are not treated early which often leads to Cervical Interepithelial Neoplasia(CIN) or cancerous lesions and proves fatal. In this paper, novel methods have been proposed for automated probabilistic image segmentation of cervical cancer. The detection of cervical lesions is an important issue in image processing because it has a direct impact on surgical planning. We also examined the segmentation accuracy based on a validation metric against the estimated composite latent gold standard, which was derived from several experts' manual segmentations. The distribution functions of the lesion and control pixel data were parametrically assumed to be a mixture of probability distributions with different shape parameters. We also estimated the corresponding receiver operating characteristic curve over all possible decision thresholds. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds.