A HYBRID METHOD FOR MEDICAL IMAGES INDEXING USING PRINCIPAL COMPONENT ANALYSIS AND CDF BIORTHOGONAL WAVELET BASED ON LIFTING SCHEME

Abstract

Hospitals and clinics produce a great number of medical images that are stored in large databases. Content-based medical image retrieval systems (CBMIRs) is one of the solutions to access rapidly and efficiently to these databases using their visual content. In this paper, we propose a new algorithm for medical image indexing. It consists on a combination of bio-orthogonal CDF wavelet transform (WT) based on lifting scheme and principal component analysis (PCA). We use this WT to decompose images, then we apply the PCA method to reduce the number of features and select the pertinent components which represent image signature. Finally, Euclidean distance is used to retrieve most similar image from databases when query image is submitted. We have tested our algorithm on the retinal image database. The results obtained by our algorithm are compared with several published methods cited in the literature and shows an efficiency of 80%, which is significantly higher and much faster than recent methods in CBMIRs domain.