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Pixel intensities of Magnetic Resonance Images (MRI) are known to get corrupted by noise during the acquisition process, resulting in degradation of diagnostic features. Improper estimation of this noise level acts as a performance constraint for noise suppression and subsequent image processing tasks. This paper utilizes a patch based approach where the decomposed patches of MRI with low texture...
This work presents the conclusions of an experimental study that intends to find the best procedure for reducing the noise of medium resolution infrared images. The goal is to find a good scheme for an image database suitable for use in developing a system to aid breast disease diagnostics. In particular, to use infrared images in the screening and postoperative follow-up in the UFF university hospital,...
Segmentation plays a vital role in extracting information from medical images. Segmentation is the process of partitioning the image into distinct regions. Magnetic resonance imaging is used to extract images of soft tissues of human body. It is used to analyze the human organs without the need for surgery. Generally MRI images contain a significant amount of noise caused by operator performance,...
In image processing noise removal is the strenuous tasks. Noise removal forms one of the applications of segmentation. It is also the basic tool for the medical diagnosis. It helps the medical practitioner to extract the defected organ easily and give a proper diagnosis. The present scenario is to concentrate on extracting the desired tissue from the noisy image obtained through ultrasound scanning...
Medical image processing is considered as an important topic in the domain of image processing. It is used to help the doctors to improve and speed up the diagnosis process. In particular, computed tomography scanners (CT-Scanner) are used to create cross-sectional medical 3D images of bones. In this paper, we propose a method for CT-Scanner identification based on the sensor noise analysis. We built...
The two-dimensional images are often insufficient to achieve a perfect diagnosis in the medical area; on the other hand the three-dimensional images allow having an interesting deductive vision and there denoising has become a necessity and an essential need. Many methods have been proposed to reduce noise and to preserve edge, are usually used for 2D images and they have been extended to 3D data...
We propose in this paper a novel example-based method for Gaussian denoising of CT images. In the proposed method, denoising is performed with the help of a set of example CT images. We construct, from the example images, a database consisting of high and low-frequency patch pairs and then use the Markov random field to denoise. The proposed denoising method can restore the high-frequency band that...
Median filter as a widespread noise reduction technique is here adapted for images taken with computed tomography (CT) scanner. In order to remove noise and thereby preserve edges and clear contours of objects we propose adapted method which uses partial variable median filtering. Except method explanation, we propose suitable platform for implementation, considering size of data set and time requirements...
Radiation dose of X-ray Computed Tomography (CT) imaging has raised a worldwide health concern. Therefore, low-dose CT imaging has been of a huge interest in the last decade. However, lowering the radiation dose degrades the image quality by increasing the noise level, which may reduce the diagnostic performance of the images. As a result, image denoising is one of the fundamental tasks in low-dose...
In this paper, we present an image segmentation technique based on fuzzy c-means (FCM) incorporated with wavelet domain noise filtration. With the use of image noise feature estimation composed of preliminary coefficient classification and wavelet domain indicator, a filter for balancing the preservation of relevant details against the degree of noise reduction can be created. The filter is further...
An effective image denoising algorithm based on fuzzy logic is proposed. This algorithm combines the fuzzy logic with the Perona-Malik method. This algorithm builds a new diffusion coefficient in partial derivative equation with the fuzzy membership between the image gradient and the corresponding smooth regions. By defining reasonable fuzzy membership function, the algorithm based on a selective...
This paper describes a novel method for preprocessing of microscopy images by means of denoising and contrast enhancement in the wavelet domain. A non-linear enhancement function has been designed based on the local dispersion of the wavelet coefficients modelled as a bivariate Cauchy distribution. Within the same statistical framework, a simultaneous noise reduction in the image is performed by means...
3D Denoising as one of the most significant tools in medical imaging was studied in the literature. However, most existing 3D medical data denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose an efficient 3D medical data denoising method that can handle a noise mixture of various types. Our method is based on modified 2D Adaptive Rood Pattern Search (ARPS)...
A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of the new method. Results demonstrate that compared...
In the present days, for the human body anatomical study and for the treatment planning medical science very much depend on the medical imaging technology and medical images. Specifically for the human brain, MRI widely prefers and using for the imaging. But by nature medical images are complex and noisy. This leads to the necessity of processes that reduces difficulties in analysis and improves quality...
Ultrasound imaging has been considered as the most powerful techniques for imaging organs and soft tissue structures in the human body. However its main limitation is its poor quality of images which are degraded by speckle noise. Speckle is a multiplicative form of noise which is inherent in ultrasound imaging but carries some useful information which should be filtered out without losing the features...
Due to abrupt increase in interacting strength of probed molecules and the AFM tip, the dramatic change in tip vibrations leads to a loss or inadequate acquisition of height information during the scanning across the sample surfaces. Consequently, stripe noises occur and immediately become the first encountered obstacle for characterizing objects in AFM images. The un-supervised DeStripe has been...
In this article we develop Stein-type results for unbiased estimation of the risk associated with parametric estimators of the noncentrality parameter of chi-squared random variables on two degrees of freedom. These results allow for estimator adaptivity, and thus can be used to optimize the parameters of a broad class of typical denoising functions, subject only to weak smoothness assumptions. We...
Magnetic Resonance (MR) imaging is useful for medical diagnosis. However, MR images are often corrupted by Rician noise, leading to undesirable visual quality. Based on the fact that many images can be acquired at nearly the same location, this paper proposes a novel learning method for the reduction of Rician noise using nonlinear ridge regression with a training set established from a set of given...
In this paper, an adaptive approach of bilateral filtering is introduced for the despeckling of medical ultrasound images. The range parameter is estimated from intensity homogeneity measurements. For each pixel, the measurements are carried out utilizing its local neighbors considering different directions. The range parameter is then estimated from the variance of the most homogeneous blocks and...
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