Abstract: In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.
Abstract: This paper introduces the effective speckle reduction of
synthetic aperture radar (SAR) images using inner product spaces in
undecimated wavelet domain. There are two major areas in projection
onto span algorithm where improvement can be made. First is the use
of undecimated wavelet transformation instead of discrete wavelet
transformation. And second area is the use of smoothing filter namely
directional smoothing filter which is an additional step. Proposed
method does not need any noise estimation and thresholding
technique. More over proposed method gives good results on both
single polarimetric and fully polarimetric SAR images.
Abstract: This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
Abstract: This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
Abstract: This paper proposes a method for speckle reduction in
medical ultrasound imaging while preserving the edges with the
added advantages of adaptive noise filtering and speed. A nonlinear
image diffusion method that incorporates local image parameter,
namely, scatterer density in addition to gradient, to weight the
nonlinear diffusion process, is proposed. The method was tested for
the isotropic case with a contrast detail phantom and varieties of
clinical ultrasound images, and then compared to linear and some
other diffusion enhancement methods. Different diffusion parameters
were tested and tuned to best reduce speckle noise and preserve
edges. The method showed superior performance measured both
quantitatively and qualitatively when incorporating scatterer density
into the diffusivity function. The proposed filter can be used as a
preprocessing step for ultrasound image enhancement before
applying automatic segmentation, automatic volumetric calculations,
or 3D ultrasound volume rendering.
Abstract: Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound (TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.