“…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].…”
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.
“…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].…”
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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