Semantics modeling in diagnostic medical image databases using customized fuzzy membership functions

It is widely recognized that fuzzy methods play an important role in image database retrieval, especially in the context of semantic queries. Known approaches that use crisp hierarchical semantic networks have been studied and applied to content-based image retrieval (CBIR) to narrow the gap between semantics and image features. Unfortunately, most of the studies lack the flexibility to adapt to an individual's preferences and/or to establish a general-purpose semantic network for sharing the perceptual understanding. In this paper, we propose a semantic query system for diagnostic image database retrieval that uses physician-defined linguistic variables. Users can obtain more desirable retrieval results by creating new, customized semantic terms, and by modeling a suite of membership functions to reflect their preferences. The system brings an increased versatility for image retrieval, and a great amount of possibilities for customizing the semantic terms using customized fuzzy mappings. Our unique approach provides various query methods that use the semantic terms within the domain of HRCT images of the lung and allows individual users to bring the contribution to the common knowledge base.