Automatic semantic indexing of medical images using a web ontology language for case-based image retrieval

This paper presents a system implemented to evaluate the retrieval efficiency of images when they are semantically indexed using a combination of a web ontology language and the low-level features of the image. Finding a similarity measure algorithm to retrieve images based on the semantic metadata can be very challenging due to diverse image content and inadequate domain-specific ontologies describing the content. The existing methods for indexing images are primarily based on text. While these methods are widely used due to their simplicity, they are not very efficient as they require a domain expert and the textual interpretations of image content vary from person to person. In our approach, we leverage sophisticated image-processing techniques to automatically extract image content information into MPEG-7 format and associate them to the existing domain ontologies developed by experts, thereby, bridging the gap between low-level features and high-level semantics. This implementation and validation experiments show that a high retrieval accuracy rate is obtained when all the image descriptors are combined with an ontology while building the semantic metadata for indexing images.