Multimode Retrieval of Mammography Based on Association Rules

Abstract

The mammogram case has images of low level features and semantic features. In order to achieve efficient retrieval of breast imaging cases，and enhance the certainty of computer aided diagnosis，a multi-mode retrieval method based on association rules is proposed in this paper. First of all，feature selection algorithm based on the association rules can be used to select the low level features associated with image semantic features，to achieve the dimension reduction. The associative rules which between the selected features and the semantic features can be excavated by using the Apriori algorithm .And then，the associative classifier engine will be used to build the associative classification model depend on the associative rules to capture the visual semantic features. Finally，take obtained semantic from the association classification as input semantic，combining with the low level features of image，to implement the mammogram case multi-mode retrieval. We conducted experiments comparing by precision and recall rate and relevance ranking average value and so nn as the results show，multi一mode retrieval method proposed by this paper and provide visual semantic features of can effectively improve the performance of breast imaging case retrieval image by its low-level features. Multi-mode retrieval reduced the semantic gap between image low level features and visual semantic features，improved the accuracy of image retrieval and provided more meaningful decision support for doctors.

Keywords

• mammogram; association rules ; feature selection; associative classification; multi-mode retrieval