Content Based Medical Image Retrieval using Binary Association Rules

In this study, we propose a content-based medical image retrieval framework based on binary association rules to augment the results of medical image diagnosis, for supporting clinical decision making. Specifically, this work is employed on scanned Magnetic Resonance brain Images (MRI) and the proposed Content Based Image Retrieval (CBIR) process is for enhancing relevancy rate of retrieved images. The pertinent features of a query brain image are extracted by applying third order moment invariant functions, which are then examined with the selected feature indexes of large medical image database for appropriate image retrieval. Binary association rules are incorporated here for organizing and marking the significant features of database images, regarding a specific criterion. Trigonometric function distance similarity measurement algorithm is applied to improve the accuracy rate of results. Moreover, the performances of classification and retrieval methods are determined in terms of precision and recall rates. Experimental results reveal the efficacy of the adduced methodology as compared to the related works.