A RELEVANCE FEEDBACK APPROACH BASED ON MODIFICATION OF SIMILARITY MEASURE USING PARTICLE SWARM OPTIMIZATION IN A MEDICAL X-RAY IMAGE RETRIEVAL SYSTEM

Abstract

Relevance feedback (RF) approaches are use to improve the performance of content-based image retrieval (CBIR) systems. In this paper, a RF approach based on modification of similarity measure using particle swarm optimization (PSO) in a medical X-ray image retrieval system is proposed. In this algorithm, using PSO, the significance of each feature in the similarity measure is modified to image retrieval. This modification causes that good features have major effect in relevant image retrieval. The defined fitness function in PSO uses relevant and irrelevant retrieved images with different strategies, simultaneously. The relevant and irrelevant images are used to exhort and penalize similarity measure, respectively. To evaluate, the proposed RF is integrated to a CBIR system based on semantic classification. In this system, using merging scheme in a hierarchical structure, the overlapped classes are merged together and determined search space for each query image. The proposed RF evaluated on a database consisting of 10000 medical X-ray images of 57 classes. The proposed algorithm provides the improvement, effectiveness more than the literature.