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Using Relevance Feedback with Short-term Memory for Content-based Spine X-ray Image Retrieval.

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Xu X, Lee DJ, Antani SK, Long LR, Archiband JK
J Neurocomputing. June 2009;72(10-12):2259-69.
Abstract: 

Managing large medical image databases has become a challenging task as more medical images are produced and stored in digital format. Computer-aided decision support for content-based image retrieval (CBIR) is an essential tool for medical image management. This paper presents a novel hybrid relevance feedback (RF) system for shape-based retrieval of spine X-ray images. A new shape similarity measure that considers both whole shape and partial shape matching is presented. The proposed RF architecture includes separate retrieval and feedback modes to solicit user's opinion for refining retrieval results. A unique short-term memory approach is implemented to avoid repeated request for user's feedback on the same, already approved, and retrieved relevant images. An automatic weight updating scheme is developed to present the images on which it is best for the user to provide feedback. Incorporating all these unique features, the proposed RF retrieval system is able to reduce the gap between high-level human visual perception and low-level computerized features. Experimental results show overall retrieval accuracy improvement of 22.0% and 17.5% after the second feedback iteration for retrieving spine X-ray images with similar osteophytes severity and type, respectively.

Xu X, Lee DJ, Antani SK, Long LR, Archiband JK. Using Relevance Feedback with Short-term Memory for Content-based Spine X-ray Image Retrieval. J Neurocomputing. June 2009;72(10-12):2259-69.