Automatic liver localization based on classification random forest with KNN for prediction
文献类型：会议论文
| 作者 | Benwei Gong; Baochun He; Qingmao Hu; Fucang Jia |
| 出版日期 | 2015 |
| 会议名称 | 2015 World Congress on Medical Physics and Biomedical Engineering |
| 会议地点 | Toronto;Canada |
| 英文摘要 | Robust localization of liver in 3D-CT images is a prerequisite for automatic liver segmentation. Accurate, ro-bust liver localization is challenging due to the variation in appearance and shape, and the ambiguous boundaries between the liver and its neighbor organs. A fully automatic approach was proposed: in the first stage, the interface between the thoracic cavity and the abdomen was detected with a differen-tial model, and the relative structural prior of liver region was derived; in the second stage, random forest is constructed, each testing sample was predicted with a k nearest neighbor (KNN) model based on the relative structural in the same leaf node of the random forest. Experiment results showed that the proposed method obtained comparable or better performance in liver localization. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/7228]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Benwei Gong,Baochun He,Qingmao Hu,et al. Automatic liver localization based on classification random forest with KNN for prediction[C]. 见:2015 World Congress on Medical Physics and Biomedical Engineering. Toronto;Canada. |
入库方式： OAI收割
来源：深圳先进技术研究院
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