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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bell, A.A. Herberich, G. Meyer-Ebrecht, D. Bocking, A. Aach, T. |
| Copyright Year | 2007 |
| Description | Author affiliation: RWTH Aachen Univ., Aachen (Bell, A.A.; Herberich, G.; Meyer-Ebrecht, D.) |
| Abstract | For successful cure, cancer has to be detected as early as possible. Since cancer starts from a single cell, this can best be done using cytopathological methods. One important diagnostically relevant measure is the proliferation rate of the cells, which can be estimated from segmented silver stained nuclei. However, the microscopy images of silver stained specimens vary strongly in intensity and contrast and are furthermore compromised by an overall texture. We show that a precise segmentation of the nuclei is possible using a two-step approach. First, an oversegmentation with the mean shift algorithm is obtained. In a second step, these regions are merged to objects, guided by a suitable shape model, viz an ellipse, but simultaneously allowing deviations from this shape model. The segmentation results are compared to a gold standard of 8617 nuclei from 23 specimens of the thyroid gland, achieving a mean areal segmentation error of DeltaA $macr_{nucleus}$ = $12mum^{2}$ per nucleus. |
| File Size | 255944 |
| File Format | |
| ISBN | 9781424414369 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2007.4379518 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cancer detection Silver Image segmentation Microscopy Shape Clustering algorithms Biomedical measurements Nuclear measurements Glands Area measurement region grouping silver stain microscopy images nuclear segmentation mean shift algorithm |
| Content Type | Text |
| Resource Type | Article |
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