Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval

Abstract

This paper presents a novel feature extraction algorithm called local ternary co-occurrence patterns (LTCoP) for biomedical image retrieval. The LTCoP encodes the co-occurrence of similar ternary edges which are calculated based on the gray values of center pixel and its surrounding neighbors. Whereas the standard local derivative pattern (LDP) encodes the co-occurrence between the first-order derivatives in a specific direction. The existing LDP is a specific direction rotational variant feature where as our method is rotational invariant. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS−MRI database, NEMA−CT database and VIA/I–ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LTP, local tetra patterns (LTrP) and LDP with and without Gabor transform.