A Novel Hybrid Framework for Medical Image Retrieval

Abstract

A new hybrid framework for Content-Based Medical Image Retrieval (MCBIR) is proposed in this paper to deals with the accuracy issues related with the existing MCBIR. The proposed hybrid framework initially divides the images into number of non-overlapping rectangular regions. Subsequently, statistical based color autocorrelogram (CA) and texture autocorrelogram (TA) is extracted for each region respectively. Then the geometric based chordiogram descriptor (CD) is extracted for each region. Both the statistical based and geometric based descriptors are combined to create a feature vector. The corresponding image regionsor patches in the query and target medical images are compared using the Canberra distance measure. The proposed hybrid framework is evaluated using the benchmark database and it is confirmed that it significantly outperforms the state-of-the-art system in terms precision, recall and G-measure.