Segmentation and registration based analysis of microscopy images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automated segmentation of tissue images for computerized IHC analysis
Computer Methods and Programs in Biomedicine
Smoothing of optical flow using robustified diffusion kernels
Image and Vision Computing
Computer-aided techniques for chromogenic immunohistochemistry: Status and directions
Computers in Biology and Medicine
Geometry-driven visualization of microscopic structures in biology
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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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.