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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wei Wang Yaoyao Zhu Xiaolei Huang Lopresti, D. Zhiyun Xue Long, R. Antani, S. Thoma, G. |
| Copyright Year | 2009 |
| Description | Author affiliation: Communications Engineering Branch, National Library of Medicine, MD 20894, USA (Zhiyun Xue; Long, R.; Antani, S.; Thoma, G.) || Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA (Wei Wang; Yaoyao Zhu; Xiaolei Huang; Lopresti, D.) |
| Abstract | In this paper, we introduce a new classifier ensemble approach, applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level—namely, the sensitivity p and specificity q, which also produces an optimal estimate of the true segmentation. In comparison with previous work [1] that utilizes the STAPLE algorithm [2] for performance level based combination, this work achieves multiple-observer segmentation in a Bayesian decision framework using the maximum a posterior (MAP) principle, considering each classifier as an observer. In our experiments, we applied our method and several other popular ensemble methods to the problem of detecting Acetowhite regions in cervical images. On 100 images, the overall performance of the proposed method is better than: (i) an overall classifier learned using the entire training set, (ii) average voting ensemble, (iii) ensemble based on the STAPLE algorithm; it is comparable to that of majority voting and that of the (manually picked) best-performing individual classifier in the ensemble set. |
| Starting Page | 342 |
| Ending Page | 345 |
| File Size | 175493 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193054 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Voting Classification tree analysis Data engineering Biomedical imaging Support vector machines Support vector machine classification Shape Bayesian methods Computer science specificity classifier ensemble segmentation cervigram multiple classifier system sensitivity |
| Content Type | Text |
| Resource Type | Article |
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