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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yang Song Weidong Cai Eberl, S. Fulham, M.J. Dagan Feng |
| Copyright Year | 2010 |
| Abstract | Content-based image retrieval (CBIR) has been an active research area since mid 90’s with major focus on feature extraction, due to its significant impact on image retrieval performance. When applying CBIR in the medical domain, different imaging modalities and anatomical regions require different feature extraction methods that integrate some domain-specific knowledge for effective image retrieval. This paper presents some new CBIR techniques for positron emission tomography - computed tomography (PET-CT) lung images, which exhibit special characteristics such as similar image intensities of lung tumors and soft tissues. Adaptive texture feature extraction and structural signature representation are proposed, and implemented based on our recently developed CBIR framework. Evaluation of the method on clinical data from lung cancer patients with various disease stages demonstrates its benefits. |
| Starting Page | 152 |
| Ending Page | 157 |
| File Size | 1718072 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424488162 |
| DOI | 10.1109/DICTA.2010.37 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-01 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Lungs Computed tomography Image retrieval Feature extraction Positron emission tomography Pixel Tumors |
| Content Type | Text |
| Resource Type | Article |
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