Name
Title | ||
|---|---|---|
An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TIP.2005.852460 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
gynaecology,statistical image modeling,reflectance confocal images,bayesian framework,statistical analysis,confocal microscopy,image segmentation,automatic segmentation,nuclei segmentation,gaussian markov random fields (gmrfs),confocal reflectance microscopy,cervical tissue,in vivo cervical tissue,cancer,optical microscopy,bayes methods,image model,cervical precancer detection method,image segmentation algorithm,anisotropic diffusion,biological tissues,image enhancement,medical image processing | Anisotropic diffusion,Image quality,Image processing,Image segmentation,Artificial intelligence,Microscopy,Computer vision,Pattern recognition,Segmentation,Algorithm,Confocal,Confocal microscopy,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 9 | 1057-7149 |
Citations | PageRank | References |
15 | 1.29 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brette L Luck | 1 | 15 | 1.29 |
| Kristen D Carlson | 2 | 15 | 1.29 |
| Alan C. Bovik | 3 | 3341 | 274.64 |
| Rebecca Richards-Kortum | 4 | 22 | 5.12 |