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Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does(More)
Red lesions in the form of Microaneurysms (MAs) and Hemorrhages (HMs) are among the first explicit signs of diabetic retinopathy (DR). Hence robust detection of these lesions is an important diagnostic task in computer assistance systems. In this paper we present a new curvelet based algorithm to separate these red lesions from the rest of the color retinal(More)
In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific(More)
In this paper, we proposed novel noise reduction algorithms that can be used to enhance image quality in various medical imaging modalities such as magnetic resonance and multidetector computed tomography. The noisy captured 3-D data are first transformed by discrete complex wavelet transform. Using a nonlinear function, we model the data as the sum of the(More)
Speckle noise is an inherent nature of ultrasound images, which may have negative effect on image interpretation and diagnostic tasks. In this paper, we propose several multiscale nonlinear thresholding methods for ultrasound speckle suppression. The wavelet coefficients of the logarithm of image are modeled as the sum of a noise-free component plus an(More)
BACKGROUND Nowadays, medical imaging equipments produce digital form of medical images. In a modern health care environment, new systems such as PACS (picture archiving and communication systems), use the digital form of medical image too. The digital form of medical images has lots of advantages over its analog form such as ease in storage and(More)
In this paper, we present a new image denoising algorithm. We assume a mixture of bivariate circular symmetric Laplacian probability density functions (pdfs) where for each wavelet coefficients may have different local parameter. This pdf characterizes simultaneously 1) the heavy-tailed nature, 2) the interscale dependencies of the wavelet coefficients and(More)
The performance of various estimators, such as maximum a posteriori (MAP) is strongly dependent on correctness of the proposed model for noise-free data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is very important in the wavelet based image denoising. This paper presents a new image denoising algorithm(More)