Medical Image Semantic Annotation Based on MIL

摘要：

　　A method of medical image semantic annotation based on multi-instance learning is presented in this paper,as a result,the semantic gap problem which existence between the low-level image visual features and the high-level semantics is decreased to some extent.The lung CT images are used an example in study.A lung CT image is a bag of multi-instance learning in this method,the two-dimensional convex hull algorithms is used on the extraction of pulmonary parenchyma of the lung CT image,the gray and texture feature of the double lobes are calculated individually as the instance in the bag.The image semantic annotation is achieved by using the interactive mode which generated the positive and negative bags,and adopting multiple instance learning algorithms which combined expected maximum and diversified density.A fairly good semantic annotation ability of the method is indicated according to the experiment result.

关键词： Multi-instance learning Semantic annotation Diversity density

作者: Jia Gang Feng Yuan Zheng Bing

作者单位: College of Automation Harbin Engineering University Harbin, HeiLongjiang Province, 150000, China

会议类型: 国际会议

会议名称: 2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 85-90

在线出版日期: 2013-05-25（万方平台首次上网日期，不代表论文的发表时间）