Details

Title

Denoising methods for improving automatic segmentation in OCT images of human eye

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2017

Numer

No 1

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 0239-7528, eISSN 2300-1917

References

Valverde (2015), Evaluation of speckle reduction with denoising filtering in optical coherence tomography for dermatology th International Symposium on Biomedical Imaging, IEEE, 12. ; Wang (2012), Adaptive speckle reduction in OCT volume data based on block - matching and filtering, IEEE Phot Technol Lett, 24, 3. ; Kudasik (2015), Methods for designing and fabrication large - size medical models for orthopaedics, Bull Pol Tech, 63, 623. ; Nason (1995), The stationary wavelet transform and some statistical applications Notes in, Lecture Statistics, 103. ; Chiu (2010), Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation, Optics express, 18, 19413, doi.org/10.1364/OE.18.019413 ; Puvanathasan (2009), Interval type - II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images, Opt Express, 17, 733, doi.org/10.1364/OE.17.000733 ; Marks (2005), Speckle reduction by I - divergence regularization in optical coherence tomography, Opt Soc Am A, 22, 2366, doi.org/10.1364/JOSAA.22.002366 ; Kraus (2012), Motion correction in optical coherence tomography volumes on a per A - scan basis using orthogonal scan patterns, Biomedical Optics Express, 3, 1182, doi.org/10.1364/BOE.3.001182 ; Mayer (2012), Wavelet denoising of multiframe optical coherence tomography data, Biomedical Optics Express, 3, 572, doi.org/10.1364/BOE.3.000572 ; Rogowska (2002), Image processing techniques for noise removal enhancement and segmentation of cartilage OCT images Physics in Medicine and, Biology, 47, 641. ; Wong (2010), General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery, Opt Express, 18, 8338, doi.org/10.1364/OE.18.008338 ; Abbirame (2014), Speckle noise reduction in spectral domain optical coherence tomography retinal images using fuzzification method on Green Computing Communication and Electrical Engineering, Int Conf, 1. ; Perona (1990), Scale - space and edge detection using anisotropic diffusion Pattern and, IEEE Trans Anal Mach Intell, 12, 629, doi.org/10.1109/34.56205 ; Wojtkowski (2010), High - speed optical coherence tomography : basics and applications, Appl Opt, 49, 61, doi.org/10.1364/AO.49.000D30 ; Abràmoff (2010), Retinal imaging and image analysis IEEE Reviews in, Biomedical Engineering, 169. ; Sonka (2016), Quantitative analysis of retinal OCT, Medical Image Analysis, 33. ; Chitchian (2009), Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual - tree complex wavelet transform, Optics, 14, 14. ; Karamata (2005), Speckle statistics in optical coherence tomography, Opt Soc Am A, 22, 593, doi.org/10.1364/JOSAA.22.000593 ; Bernardes (2010), Improved adaptive complex diffusion despeckling filter, Opt Express, 18, 24048, doi.org/10.1364/OE.18.024048 ; Ozcan (2007), Speckle reduction in optical coherence tomography images using digital filtering, Opt Soc Am A, 24, 1901, doi.org/10.1364/JOSAA.24.001901 ; Jian (2010), Three - dimensional speckle suppression in optical coherence tomography based on the curvelet transform, Optics Express, 18, 1024, doi.org/10.1364/OE.18.001024 ; Kudasik (2016), Methods of reconstructing complex multi - structural anatomical objects with RP techniques, Bull Pol Tech, 64, 315. ; Baghaie (2015), Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT images th International Symposium on Biomedical Imaging, IEEE, 12.

DOI

10.1515/bpasts-2017-0009

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