Segmenting cervical epithelial nuclei from confocal images Gaussian Markov random fields

Cervical cancer is always preceded by epithelial lesions which have larger and more densely spaced nuclei than normal tissue. Detecting and removing these lesions prevents the development of cervical cancer. A proposed method to detect precancerous lesion in vivo is to use the nuclear size and density information from fiber optic confocal images of the cervical epithelial tissue to classify the tissue as normal or precancerous. Automatically segmenting nuclei is challenging because they are hard to decipher from the noise in the confocal images. This paper outlines an algorithm to automatically segment cervical epithelial nuclei from fiber optic confocal videos using Gaussian Markov random fields. Gaussian Markov random fields segment images with additive Gaussian noise by modeling the underlying structure of the image. The algorithm described in this paper detects 90% of the nuclei in each frame with a 14% error rate.