Optimized Optical Coherence Tomography Imaging With Hough Transform-Based Fixed-Pattern Noise Reduction

Abstract

Fixed-pattern noise seriously affects the clinical application of optical coherence tomography (OCT), especially, in the imaging of tumorous tissue. We propose a Hough transform-based fixed-pattern noise reduction (HTFPNR) method to reduce the fixed-pattern noise for optimizing imaging of tumorous tissue with OCT system. Using by the HTFPNR method, we detect and map the outline of fixed-pattern noise in the OCT images, and finally efficiently reduce the fixed-pattern noise by the longitudinal and horizontal intelligent processing procedure. We adopt the image-to-noise ratio with full information (INRfi) and the noise reduction ratio (NRR) to evaluate the outcome of fixed-pattern noise reduction ratio, respectively. The INRfi of OCT image’s noise reduction of ex vivo brainstem tumor is approximate 21.92 dB. Six groups of OCT images, including three types of fixed-pattern noises, have been validated via experimental evaluation of the ex vivo gastric tumor. In the different types of fixed-pattern noise, the mean INRfis are 25.24, 23.04, and 19.35 dB, respectively. This result demonstrates that it is highly efficient and useful in fixed-pattern noise reduction. The fluctuating range of the NRR is 0.84–0.88 for three types of added noise in the OCT images. This result demonstrates that the HTFPNR method can as possible as save useful information by comparing to previous research. This proposed HTFPNR method can be used into the fixed-pattern noise reduction of OCT images in other soft biological tissue in the future.