Biomedical Image Indexing and Retrieval Descriptors: A Comparative Study

• Published in: Procedia computer science Vol. 85; pp. 954 - 961
• Main Authors: Deep, Gagan, Kaur, Lakhwinder, Gupta, Savita
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2016
• Subjects: directional local ternary quantized extrema pattern; Image retrieval; Local binary pattern (LBP); local mesh ternary pattern; Local ternary pattern (LTP); Medical imaging; Texture; Local ternary pattern (LTP); Medical imaging; Image retrieval; Local binary pattern (LBP); local mesh ternary pattern; directional local ternary quantized extrema pattern; Texture
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2016.05.287

This paper focuses on the comparison of two new proposed pattern descriptors i.e., local mesh ternary pattern (LMeTerP) and directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the former descriptor encodes the gray scale relationship among the neighbors for a given center pixel with three selected directions of mess patterns which is generated from 2D image and later descriptor encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0̊, 45̊, 90̊ and 135̊) for a given center pixel in an image. The novelty of the proposed descriptors is that they use ternary patterns from images to encode more spatial structure information which lead to better retrieval. The experimental results demonstrate the superiority of the new techniques in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques (like LBP, LTP, LQEP, LMeP etc.) on three different types of benchmark biomedical databases.