By Christos P. Loizou, Constantinos S. Pattichis
It truly is recognized that speckle is a multiplicative noise that degrades picture caliber and the visible overview in ultrasound imaging. This necessitates the necessity for powerful despeckling options for either regimen medical perform and teleconsultation. The target for this booklet is to introduce the theoretical heritage (equations), the algorithmic steps, and the MATLAB™ code for the subsequent workforce of despeckle filters: linear filtering, nonlinear filtering, anisotropic diffusion filtering and wavelet filtering. The booklet proposes a comparative evaluate framework of those despeckle filters according to texture research, picture caliber overview metrics, and visible overview through medical examiners, within the review of cardiovascular ultrasound pictures recorded from the carotid artery. the result of our paintings provided during this e-book, recommend that the linear neighborhood facts filter out DsFlsmv, gave the easiest functionality, by means of the nonlinear geometric clear out DsFgf4d, and the linear homogeneous masks zone filter out DsFlsminsc. those filters superior the category separation among the asymptomatic and the symptomatic periods (of ultrasound photos recorded from the carotid artery for the evaluation of stroke) in accordance with the facts of the extracted texture good points, gave just a marginal development within the category luck price, and stronger the visible review performed via health workers. A despeckle filtering research and evaluate framework is proposed for choosing the main acceptable clear out or filters for the pictures lower than research. those filters could be additional constructed and evaluated at a bigger scale and in medical perform within the computerized photograph and video segmentation, texture research, and type not just for scientific ultrasound yet for different modalities in addition, similar to man made aperture radar (SAR) photos. desk of Contents: advent to Ultrasound Imaging / Despeckle Filtering Algorithms / assessment technique / purposes of Despeckle Filtering in Ultrasound Imaging / comparability and dialogue of Despeckle Filtering Algorithms / precis and destiny instructions
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Extra info for Despeckle filtering algorithms and software for ultrasound imaging
3 Compute the noise variance s 2n with Eq. 5) for the whole image. 4 Starting from the left upper corner of the image, compute for each sliding moving window the coefficient ki, j in Eq. 4). 5 Compute fi, j in Eq. 1) and replace the noisy middle point in each moving window gi, j with the new computed value fi, j . 6 Repeat steps 4 and 5 for all the pixels in the image by sliding the moving window from left to right. 7 Repeat steps 3–6 for a second iteration of despeckle filtering. 8 Compute the image quality evaluation metrics and the texture features for the original and despeckled images.
7f ). Asymptomatic images were recorded from patients at risk of atherosclerosis in the absence of clinical symptoms, whereas symptomatic images were recorded from patients at risk of atherosclerosis, which have already developed clinical symptoms, such as a stroke episode. 7f ). 4 LIMITATIONS OF DESPECKLE FILTERING TECHNIQUES Despeckling is always a tradeoff between noise suppression and loss of information, which is something that experts are very concerned about. It is, therefore, desirable to keep as much important information as possible.
Both filters form the output image by computing the central pixel intensity inside a filter-moving window, which is calculated from the average intensity values of the pixels and a coefficient of variation inside the moving window. Kuan considered a multiplicative speckle model and designed a linear filter based on the minimum-mean-square error criterion that has optimal performance when the histogram of the image intensity is Gaussian distributed. The Lee filter  is a particular case of the Kuan filter based on a linear approximation made for the multiplicative noise model.