Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A vector space model for automatic indexing
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Using human perceptual categories for content-based retrieval from a medical image database
Computer Vision and Image Understanding
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisMed: a visual vocabulary approach for medical image indexing and retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
| Hi-index | 0.00 |
To facilitate the automatic indexing and retrieval of large medical image databases, images and associated texts are indexed using concepts from the Unified Medical Language System (UMLS) metathesaurus. We propose a structured learning framework for learning medical semantics from images. Two complementary global and local visual indexing approaches are presented. Two fusion approaches are also used to improve textual retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. Using the ImageCLEFmed database, we demonstrate that our framework is superior when compared to the automatic runs evaluated in 2005 on the same Medical Image Retrieval task.