- Title
- Computer aided abnormality detection for microscopy images of cervical tissue
- Creator
- Cui, Yue; Jin, Jesse S.; Park, Mira; Luo, Suhuai; Xu, Min; Peng, Yu; Wong, W. S. Felix; Santos, Leonardo D.
- Relation
- 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME2010). Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (Gold Coast, QLD 13-15 July, 2010) p. 63-68
- Publisher Link
- http://dx.doi.org/10.1109/ICCME.2010.5558872
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2010
- Description
- Cervical cancer is the second most common malignancy among women worldwide, if it is detected in early stage, cure rate is relatively high. Computer aided abnormality detection for cervical smear is developed to assist medical experts to handle the microscopy images, examine cell abnormalities and diagnose dyskaryosis. The microscopy images of cells in cervix uteri are stained by the tumor marker Ki-67, so that the abnormal nuclei present brown while normal ones are bluish. Segmentation is the most important and difficult task to calculate the ratio of abnormal nuclei to all nuclei. In order to achieve accurate segmentation of nuclei, we propose a multi-level segmentation approach for abnormality identification in microscopy images. First level segmentation aims to partition abnormal (stained) nuclei regions and all nuclei regions. Because of under-segmentation after first level segmentation, second level segmentation is applied to further partition the clustered nuclei. In order to classify touching regions of clustered nuclei and separate regions of single nucleus, relevant meaningful features are extracted from regions of interest. Consequently all the nuclei regions are separated and in conjunction with the abnormal nuclei regions in the first level segmentation, the abnormality i.e. ratio of abnormal nuclei to all nuclei is obtained. Experimental results indicate that our method achieved an accuracy of 93.55% and 95.8% in term of abnormal nuclei and all nuclei respectively for identification of abnormalities. Our proposed method produces a satisfactory segmentation.
- Subject
- abnormality detection; cervical cancers; cervical tissues; microscopy images; biomedical engineering; medical imaging
- Identifier
- http://hdl.handle.net/1959.13/933184
- Identifier
- uon:11563
- Identifier
- ISBN:9781424468416
- Rights
- Copyright © 2010 IEEE. Reprinted from the Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
- Reviewed
- Hits: 2354
- Visitors: 3142
- Downloads: 288
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT03 | Publisher version (open access) | 1 MB | Adobe Acrobat PDF | View Details Download |