An Efficient Multiscale Wavelet Local Binary Pattern for Biomedical Image Retrieval

Abstract

A method for biomedical image retrieval using multiscale wavelet local binary pattern (LBP) is presented in this paper. The method first decomposes a biomedical image into approximation and oriented detail subbands using discrete wavelet transform (DWT). Since the oriented detail subbands at each scale exhibit distinct directional features the proposed method employ a new 4-point LBP with selected non-diagonal neighbors in horizontal and vertical subbands, and a 4-point LBP with selected diagonal neighbors in diagonal subband to extract the LBP histogram. An 8-point LBP is employed in approximation subband to extract the LBP histogram. The biomedical image is finally represented by a single feature histogram that is formed by concatenation of all the LBP histograms. The proposed method provides significantly reduced feature vector size while maintaining same or most of the times better retrieval efficiency compared to original LBP and other relevant wavelet-based LBP schemes. The Euclidean distance measure is used for query matching and retrieval is performed based on the least matching distance. The method is tested using OSIRIX image data sets and experimental results validating the efficiency of the proposed method over other relevant schemes, are presented.