Abstract
According to the speckle feature in Optical coherence tomography (OCT), images with speckle indicate not only noise but also signals, an improved wavelet hierarchical threshold filter (IWHTF) method is proposed. At first, a modified hierarchical threshold-selected algorithm is used to prevent signals from being removed by assessing suitable thresholds for different noise levels. Then, an improved wavelet threshold function based on two traditional threshold functions is proposed to trade-off between speckle removing and sharpness degradation. The de-noising results of an OCT finger skin image shows that the IWHTF method obtains better objective evaluation metrics and visual image quality improvement. When [Formula: see text], [Formula: see text] and [Formula: see text], the improved method can achieve 9.58[Formula: see text]dB improvement in signal-to-noise ratio, with sharpness degraded by 3.81%.
Highlights
Optical coherence tomography (OCT) is a promising optical imaging technique widely used in ophthalmology and cardiology, due to its advantages including non-invasive, high speed, high resolution and three-dimensional imaging.[1,2,3] The quality of images is vital for OCT
We propose a modied hierarchical threshold-selected algorithm and an improved wavelet threshold function based on the wavelet threshold de-noising algorithm proposed by Donoho and Johnstone.[40]
The contrast-to-noise ratio (CNR) and equivalent number of looks (ENL) are calculated on the average of four regions of interest (ROI) from region 2 to 5
Summary
Optical coherence tomography (OCT) is a promising optical imaging technique widely used in ophthalmology and cardiology, due to its advantages including non-invasive, high speed, high resolution and three-dimensional imaging.[1,2,3] The quality of images is vital for OCT. Hardware methods are optical approaches that physically remove speckle noise, including frequency[12,13] and spatial compounding,[14,15,16,17,18] which can signicantly improve signal-to-noise ratio of OCT images. These methods would increase system complexity, decrease imaging speed and spatial resolution.[12,16] Software approaches based on post-processingltering techniques, including adaptive Wienerltering,[6] curvelet domainltering,[19,20] contourlet domainltering,[21] Csiszars I-divergence regularization,[22] interval type II fuzzy system,[23,24] regularized image restoration based on speckle characteristics,[25] compressed sensing (CS) reconstruction,[26,27,28] sparse reconstruction,[29,30,31,32] and wavelet domainltering[33,34,35,36,37] can reduce speckle noise byltering in di®erent transform domain. Wavelet domainltering is widely accepted as a promising method in de-noising for OCT images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
