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Feature selection can significantly improve the precision of content-based queries in image databases by removing noisy features or by bursting the most relevant ones. Continuous feature selection techniques assign continuous weights to each feature according to their relevance. In this paper, we propose a supervised method for continuous feature selection. The proposed method applies statistical association rules to find patterns relating low-level image features to high-level knowledge about the images, and it uses the patterns mined to determine the weight of the features. The feature weighting through the statistical association rules reduces the semantic gap that exists between low-level features and the high-level user interpretation of images, improving the precision of the content-based queries. Moreover, the proposed method performs dimensionality reduction of image features avoiding the "dimensionality curse" problem. Experiments show that the proposed method improves the precision of the query results up to 38%, indicating that statistical association rules can be successfully employed to perform continuous feature selection in medical image databases.