Statistical Association Rules and Relevance Feedback: Powerful Allies to Improve the Retrieval of Medical Images

Abstract

This work aims at developing an efficient support to improve the precision of medical image retrieval by content, introducing an approach that combines techniques of statistical association rule mining and relevance feedback. Low level features of shape and texture are extracted from images. Statistical association rules are used to select the most relevant features to discriminate the images, reducing the size of the feature vectors and eliminating noisy features that influence negatively the query results, making the whole process more efficient. Additionally, our approach uses a new relevance feedback technique to overcome the semantic gap that exists between low level features and the high level user interpretation of images. Experiments show that the combination of statistical association rule mining and the relevance feedback technique proposed here improve the precision of the query results up to 100%.