In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

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.

The use of computational resources can improve the diagnosis of medical diseases as a second opinion. Due to the large amount of data obtained daily, incremental techniques have been proposed to process medical data stream. In this paper we present an incremental decision tree classifier called StARMiner Tree (ST), which is based on Very Fast Decision Tree (VFDT) technique, to mine medical data. Different from VFDT, our proposed method ST does not depend on the number of reading samples to split a node. Because of it, ST is less conservative and describes the data since their first samples, being appropriate to be employed in medical environment, where not always a large number of data samples are available. We applied ST to four medical datasets, comparing the ST performance to the VFDT. The results indicated that ST is well-suited to deal with medical data streams, presenting high accuracy and low execution time.

In this paper we present a framework based on association-rules to help diagnosis of mammogram abnormalities. Our framework - SuGAR - combines low-level features automatically extracted from images with high-level knowledge gotten from specialists to mine association rules, suggesting possible diagnoses. Our framework is optimized, in the sense that it combines, in a single step, feature selection and discretization, reducing the mining complexity. The framework was applied to real datasets and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that association rules can effectively aid in the diagnosing task.