The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame.

We describe a comprehensive strategy to develop inexpensive, rugged and portable optical imaging systems for molecular imaging of cancer, which couples the development of optically active contrast agents with advances in functional genomics of cancer.

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.

Confocal microscopy provides images of tissue architecture and cellular morphology in living tissue in real time. Pilot clinical trials show confocal images can diagnose dysplasia with unprecedented specificity and sensitivity. Flexible fiber optic instrumentation permits in vivo imaging. This talk will overview confocal imaging for detection and management of cancer.