In the field of dental biometrics, textural information plays a significant role very often in tissue characterization and gum diseases diagnosis, in addition to morphology and intensity. Failure to diagnose gum diseases in its early stages may leads to oral cancer. Dental biometrics has emerged as vital biometric information of human being due to its stability, invariant nature and uniqueness. The objective of this paper is to improve the classification accuracy based on fused LBP and SIFT textural features for the development of a computer assisted screening system. The swift expansion of dental images has enforced the requirement of efficient dental image retrieval system for retrieving images that are visually similar to query image. This paper implements a dental image retrieval system using fused LBP & SIFT features. The fused LBP & SIFT features identify the gum diseases from the epithelial layer in classifying normal dental images about 91.6% more accurately compared to other features. © Springer International Publishing Switzerland 2016.
R. Suganya, Rajaram, S., Vishalini, S., Meena, R., and Dr. Senthil Kumar T., “Dental image retrieval using fused local binary pattern & scale invariant feature transform”, Advances in Intelligent Systems and Computing, vol. 425. Springer, pp. 215-224, 2016.