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	Automatic liver localization based on classification random forest with KNN for prediction
	Benwei Gong; Baochun He; Qingmao Hu; Fucang Jia
	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
内容类型	会议论文
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.

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