Paper Details: Downloads: 783
Serial Number: P1150951943
Title: Content Based Medical Image Retrieval Based on BEMD: use of Generalized Gaussian Density to model BIMFs coefficients
Authors: Said Jai-Andaloussi and Mathieu Lamard and Guy Cazuguel and Hamid Tairi and Mohamed Meknassi and Christian Roux and Béatrice Cochener
Abstract: In this paper, we address the problem of medical diagnosis aid through content based image retrieval methods (CBIR). We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Bidimensional Intrinsic Mode Functions (BIMF) and a residue. The generalized Gaussian density function (GGD) is used for modelling the coefficients derived from each BIMF, and to measure similarity between images we compute the similarity between GGDs by using the Kullback–Leibler Divergence (KLD). Retrieval efficiency is given for different databases including a diabetic retinopathy, mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases
Keywords: Content-Based Image Retrieval, BEMD, Generalized Gaussian density, Kullback–Leibler Divergence.
Journal/Conference: International Journal of Graphics, Vision and Image Processing
Volume: 10
Issue: 2
Submission Date: 12/16/2009 12:00:00 AM
Review Date: 5/4/2010 12:00:00 AM
Publishing Date: 5/20/2010 12:00:00 AM
Article Downloads: 783
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