期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 44, 期 4, 页码 519-528出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2011.01.011
关键词
Auto-annotation; Biomedical images; Histology; Histopathology; Image retrieval; Kernels; Kernel alignment
资金
- Ministerio de Educacion Nacional de Colombia by Convocatoria COLCIENCIAS [1101-487-25779]
- Colciencias by Convocatoria COLCIENCIAS [1101-489-25577]
Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image as query. The proposed approach automatically annotates the images in the collection, as well as the query images, with high-level semantic concepts. This semantic representation delivers an improved retrieval performance providing more meaningful results. We model the problem of automatic image annotation using kernel methods, resulting in a unified framework that includes: (1) multiple features for image representation, (2) a feature integration and selection mechanism (3) and an automatic semantic image annotation strategy. An extensive experimental evaluation demonstrated the effectiveness of the proposed framework to build meaningful image representations for learning and useful semantic annotations for image retrieval. (C) 2011 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据