Improving medical image retrieval through multi-descriptor similarity functions and association rules

Content-based image retrieval (CBIR) systems still face the problem of low precision of system results. To improve the precision of such systems, many image visual extractors have been developed and employed to represent the images. However, the usage of a large number of extractors and consequently, a large number of features, leads to the “dimensionality curse”, where the retrieval performance and the query accuracy diminish. In this paper, we propose a new method, called Statistical Fractal-scaled Product Metric (SFPM), to maximize the accuracy of CBIR systems and speedup similarity queries. The SFPM method combines association rule mining and the Fractal-scaled Product Metric (FPM) [4], to determine a reduced set of features and appropriate scale factors in multi-descriptor image similarity assessment. The FPM is an unsupervised method to determine a scale factor among features in multi-descriptor image similarity assessment based on the Fractal Theory. Experiments have shown that SFPM reduced the feature vector size in up to 65% and improved in up to 27% the query precision when comparing with the use of the FPM technique. The results show that the proposed method SFPM is effective in determining a reduced set of features and a near-optimal set of scale factors for the descriptors involved, and it is well-suited to improve the quality of content-based query in CBIR systems.