NCTU_DBLAB@ImageCLEFmed 2005: Medical Image Retrieval Task

The number of visual runs in 2008 was much lower than in previous years, and the evolution is not as fast as with textual retrieval techniques.. Five groups submitted a total of

For the medical image retrieval task, we distinguished gray images from color images and used different kinds of visual features to describe them. Our submission ranked second among

The results ob- tained with UMLS-based approaches show the potential of this conceptual indexing, especially when using a semantic dimension filtering, and the benefit of working

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF). Proceedings of SPIE,

The corresponding English and French UMLS terms associated with the concepts identified by MetaMap were added to the original English Queries.. The text was processed by SMART and

Our experiments showed that it is possible to achieve good retrieval performance on a medical image collection using a CBIR approach.. We used global features, which is in contrast

We propose several image features, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar images..

In this paper, we present the results of an experimental investigation studying the impact of this process on the accuracy of research results and thus will determine