An efficient content-based medical image indexing and retrieval using local texture feature descriptors

Abstract

This paper presents an efficient medical image indexing and retrieval method using two new proposed feature descriptors named as threshold local binary AND pattern (TLBAP) and local adjacent neighborhood average difference pattern (LANADP). In basic local binary pattern (LBP), every center pixel is considered as a threshold to generate the binary pattern, whereas in the proposed method a threshold value is calculated using the highest pixel intensity of the neighboring pixels to construct the threshold local binary pattern (TLBP). Thereafter, logical AND operation is performed between LBP and TLBP pattern to produce TLBAP pattern. The objective of the other feature descriptor named here as LANADP is to explore the relationship of neighboring pixels with its adjacent neighbors in vertical, horizontal and diagonal directions. In the proposed work, both TLBAP and LANADP features are concatenated in the form of the histograms to generate the final features vector and the performance of the system is evaluated. To test the effectiveness of the proposed method, three publicly available medical image databases, namely OASIS-MRI brain images, NEMA-CT images and VIA/ELCAP-CT images, are used. Two measures, viz. average retrieval precision and average retrieval rate, have been used to evaluate the performance of the method proposed which is further compared with some existing local pattern-based methods. The experimental results show that the proposed methods give better results as compared to the other existing methods considered in this study.