Adaptive Speckle Reduction in OCT Volume Data Based on Block-Matching and 3-D Filtering

Wang, Longzhi; Meng, Zhuo; Yao, Xiang‐Yu; Liu, Tiegen; Su, Ya; Qin, Mingliang

doi:10.1109/lpt.2012.2211582

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…Recent literature includes also papers describing application of the wavelet thresholding method for a 3D set of human skin images [20,21]. This method utilizes the information from neighboring frames to minimize the effect of blurring and emphasize the details in the image.…”

Section: Denoising Methods For Improving Automatic Segmentation In Ocmentioning

confidence: 99%

“…The results of research conducted by Ozcan et al [19] suggest superiority of methods based on wavelet transformations in comparison to other techniques. The published findings were derived from data gathered mainly using images of animal tissues (pigs, rats, mice) [8,15], human skin [20,21], and human healthy retina [9]. In many cases the noise reduction methods were analyzed on nonmedical and synthetic images [11,13,15].…”

Section: Introductionmentioning

confidence: 99%

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

Stankiewicz¹,

Marciniak²,

Dąbrowski³

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This paper presents analysis of selected noise reduction methods used in optical coherence tomography (OCT) retina images (the socalled B-scans). The tested algorithms include median and averaging filtering, anisotropic diffusion, soft wavelet thresholding, and multiframe wavelet thresholding. Precision of the denoising process was evaluated based on the results of automated retina layers segmentation, since this stage (vital for ophthalmic diagnosis) is strongly dependent on the image quality. Experiments were conducted with a set of 3D low quality scans obtained from 10 healthy patients and 10 patients with vitreoretinal pathologies. Influence of each method on the automatic image segmentation for both groups of patients is thoroughly described. Manual annotations of investigated retina layers provided by ophthalmology experts served as reference data for evaluation of the segmentation algorithm.

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Section: Denoising Methods For Improving Automatic Segmentation In Ocmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Stankiewicz¹,

Marciniak²,

Dąbrowski³

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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show abstract

“…It controls the diffusion rate of the image. The locations of boundaries in the regions are absolute at the time t, for the scale space variables 19 . The divergence operator simplifies as, The smoothing is done within a region, the interior of each region is set as one and the boundary of each region is set as zero.…”

Section: (5) Anisotropic Diffusion Filtermentioning

confidence: 99%

Reduction of Speckle Noise in SDOCT Retinal Images by Fuzzification and Anisotropic Diffusion Filtering

Padmasini¹,

Abbirame²,

Umamaheswari³

et al. 2015

Biosci., Biotechnol. Res. Asia

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Spectral Domain Optical Coherence Tomography (SDOCT) is a well accepted clinical standard for diagnosing and monitoring the pathological changes in optic disc and retinal layers. In recent times, even though spectral domain detection technique has proved higher potential in terms of extremely high sensitivity and image acquisition speed, the major shortcomings is existence of speckle and its effect on interpretation and diagnosis. In this paper, image enhancement is performed using fuzzification method followed by anisotropic diffusion filtering for effective reduction of speckle noise. Fuzzification technique uses the maximum fuzzy entropy principle. For the removal of speckles of size 2-3µm, anisotropic diffusion filtering is performed. On an average Structural Similarity Index Measure (SSIM) is calculated to be 0.9997 which proves that the image quality has been significantly improved. Moreover edges are perfectly preserved in this filtering technique.

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“…It controls the diffusion rate of the image. The locations of boundaries in the regions are absoluteat the time t, for the scale space variables [19]. The divergence operator simplifies as, The smoothing is done within a region, the interior of each region is set as one and the boundary of each region is set as zero.…”

Section: ) Anisotropic Diffusion Filtermentioning

confidence: 99%

Speckle noise reduction in spectral domain optical coherence tomography retinal images using anisotropic diffusion filtering

Padmasini¹,

Abbirame

Yacin³

et al. 2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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Adaptive Speckle Reduction in OCT Volume Data Based on Block-Matching and 3-D Filtering

Cited by 17 publications

References 9 publications

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

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

Reduction of Speckle Noise in SDOCT Retinal Images by Fuzzification and Anisotropic Diffusion Filtering

Speckle noise reduction in spectral domain optical coherence tomography retinal images using anisotropic diffusion filtering

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