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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tatanun, C. Ritthipravat, P. Bhongmakapat, T. Tuntiyatorn, L. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Otolaryngology, Faculty of Medicine, Ramathibodi Hospital, Bangkok, Thailand (Bhongmakapat, T.) || Biomedical Engineering Programme, Faculty of Engineering, Mahidol University, 25/25 Puttamolthon 4, Salaya, Nakornpathom, Thailand (Tatanun, C.; Ritthipravat, P.) || Department of Radiology, Faculty of Medicine, Ramathibodi Hospital, Bangkok, Thailand (Tuntiyatorn, L.) |
| Abstract | This paper describes a framework for automatic nasopharyngeal carcinoma segmentation from CT images. The proposed technique is based on the Region Growing Method. It is automatic segmentation in which an initial seed is generated without human intervention. The seed is generated from a probabilistic map representing the chances of it being tumor. This map is created from three probabilistic functions based on location of the tumor, intensities, and non-tumor region respectively. The pixel in which the probability is the highest will be selected as potential seeds. Only one representative of these seeds will be selected as an initial seed. Then the seed will be used for region growing subsequently. The experimental results showed that the potential seeds and initial seed were correctly determined with a percentage accuracy of 81.60% and 95.10%. The seed was grown in preprocessed CT images for identifying the nasopharyngeal carcinoma region. The results showed that, perfect match and corresponding ratio were 71.31% and 53.00% respectively |
| File Size | 724784 |
| File Format | |
| ISBN | 9781424468928 |
| e-ISBN | 9781424468935 |
| DOI | 10.1109/ICSPS.2010.5555663 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-05 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Nasopharyngeal Carcinoma Computed tomography Region Growing Technique Liver Automatic Segmentation Probabilistic logic Pixel Tumors Biomedical imaging |
| Content Type | Text |
| Resource Type | Article |
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