Capsule endoscopy images classification by random forests and ferns
文献类型：会议论文
| 作者 | Baopu Li; Ran Zhou; Can Yang; Max Q.-H. Meng; Guoqing Xu; Chao Hu |
| 出版日期 | 2014 |
| 会议名称 | 2014 4th IEEE International Conference on Information Science and Technology, ICIST 2014 |
| 会议地点 | Shenzhen, China |
| 英文摘要 | Capsule endoscopy (CE) is a rather new imaging technique designed specially for small intestine that is untouchable for traditional endoscopy such as gastroscope and colonoscopy. At present, reviewing a whole CE video for each patient is an intensive task for physicians. Hence, computerized methods for a CE video is desired to reduce the review time for clinicians. In this paper, we utilize color textural features and random forests and ferns to classify CE images. A novel color uniform local binary pattern (CULBP) algorithm is first proposed, which integrates color norm patterns and color angle patterns. The CULBP feature is robust to variation of illumination and discriminative for classification. Furthermore, in order to obtain a high classification performance and efficiency, two recent machine learning approaches, i.e., random forests and ferns, are used for classification. The experiments demonstrate a very encouraging detection accuracy of the scheme. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5579]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Baopu Li,Ran Zhou,Can Yang,et al. Capsule endoscopy images classification by random forests and ferns[C]. 见:2014 4th IEEE International Conference on Information Science and Technology, ICIST 2014. Shenzhen, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
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