A Semantic Fusion Approach Between Medical Images and Reports Using UMLS

One of the main challenges in content-based image retrieval still remains to bridge the gap between low-level features and semantic information. In this paper, we present our first results concerning a medical image retrieval approach using a semantic medical image and report indexing within a fusion framework, based on the Unified Medical Language System (UMLS) metathesaurus. We propose a structured learning framework based on Support Vector Machines to facilitate modular design and extract medical semantics from images. We developed two complementary visual indexing approaches within this framework: a global indexing to access image modality, and a local indexing to access semantic local features. Visual indexes and textual indexes - extracted from medical reports using MetaMap software application - constitute the input of the late fusion module. A weighted vectorial norm fusion algorithm allows the retrieval system to increase its meaningfulness, efficiency and robustness. First re...

• Type: Conference
• Year: 2006
• Where: AIRS
• Authors: Daniel Racoceanu, Caroline Lacoste, Roxana Teodorescu, Nicolas Vuillemenot
• Keywords: AIRS 2006, Image Retrieval, Information Management, Semantic, Semantic Medical Image