1.
Abhishek Das
– India
2.
Avijit Kar
– India
3.
Debasis Bhattacharyya
– India
Abstract
Uterine Cervical Cancer is one of the prevalent
forms of cancer in women worldwide. Most cases of
cervical cancer can be prevented through screening
programs aimed at detecting precancerous lesions.
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 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 (ROC) curve over all possible decision thresholds. The
automated segmentation yielded satisfactory accuracy
with protean optimal thresholds.
