A robust medical image retrieval system based on wavelet optimization and adaptive block truncation coding

Abstract

This paper presents a proposed method for medical image retrieval in order to search in a database for an image that is similar to a query image. The proposed Medical Image Retrieval System (MIRS) consists of two phases; enrollment phase and querying phase. In enrollment phase, the Discrete Wavelet Transform (DWT) coefficients are computed from every incoming image. Four wavelet types; Haar, Daubechies, Coiflet, and Symlet wavelets with different decomposition levels have been tested and compared in order to determine the most suitable wavelet type for the retrieval approach. Then, the Block Truncation Codes (BTCs) are extracted from the wavelet coefficients. To make the proposed image retrieval system robust, the BTC is adaptive by dividing the image into sub-blocks using one of four different scanning methods; raster, zigzag, Morton or Hilbert scanning. Finally, the extracted codes are stored as features vectors database. In querying phase, the BTCs are extracted from the wavelet coefficients of the query image. The similarity measurement between the features vector of the query images and the features vectors stored in the features vectors database is carried out using 8 different distance metrics to select the most suitable one. The proposed MIRS has been tested with a medical image database consists of 7500 CT brain images collected from a teaching hospital in Egypt. The results demonstrated that the proposed approach gives good results with extracting the BTCs with Morton scanning from the DB2 DWT. Moreover, Manhattan distance achieved the best similarity measurement results. The performance of the proposed MIRS has been compared with the published medical image retrieval approaches for VIA-ELCAP and Kvasir databases. The results indicated that, the proposed MIRS is robust and efficient for different medical image databases due to the advantages of dividing the image into blocks and each block can be retrieved separately according to its variance.