Edge Aware Geometric Filter for Ultrasound Image Enhancement

Ultrasound Image Enhancement Using Structure Oriented Adversarial Network

Abstract: In this letter, we aim to develop a deep adversarial despeckling approach to enhance the quality of ultrasound images. Most of the existing approaches target a complete removal of speckle, which produces oversmooth outputs and results in loss of structural details. In contrast, the proposed approach reduces the speckle extent without altering the structural and qualitative attributes of the ultrasound images. A despeckling residual neural network (DRNN) is trained with an adversarial loss imposed by a discriminator. The discriminator tries to differentiate between the despeckled images generated by the DRNN and the set of high-quality images. Further to prevent the developed network from oversmoothing, a structural loss term is used along with the adversarial loss. Experimental evaluations show that the proposed DRNN outperforms the state-of-the-art despeckling approaches in terms of the structural similarity index measure, peak signal to noise ratio, edge preservation index, and speckle region's signal to noise ratio.

Despeckling CNN with Ensembles of Classical Outputs

Abstract: Ultrasound (US) image despeckling is a problem of high clinical importance. Machine learning solutions to the problem are considered impractical due to the unavailability of speckle-free US image dataset. On the other hand, the classical approaches, which are able to provide the desired outputs, have limitations like input dependent parameter tuning. In this work, a convolutional neural network (CNN) is developed which learns to remove speckle from US images using the outputs of these classical approaches. It is observed that the existing approaches can be combined in a complementary manner to generate an output better than their individual outputs. Thus, the CNN is trained using the individual outputs as well as the output ensembles. It eliminates the cumbersome process of parameter tuning required by the existing approaches for every new input. Further, the proposed CNN is able to outperform the state-of-the-art despeckling approaches and produces the outputs even better than the ensembles for certain images.

Image quality assessment: from error visibility to structural similarity

Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers

Abstract: A method for simulation of pulsed pressure fields from arbitrarily shaped, apodized and excited ultrasound transducers is suggested. It relies on the Tupholme-Stepanishen method for calculating pulsed pressure fields, and can also handle the continuous wave and pulse-echo case. The field is calculated by dividing the surface into small rectangles and then Summing their response. A fast calculation is obtained by using the far-field approximation. Examples of the accuracy of the approach and actual calculation times are given. >

Effect of skin impedance on image quality and variability in electrical impedance tomography: a model study

Abstract: A computer simulation is used to investigate the relationship between skin impedance and image artefacts in electrical impedance tomography. Sets of electrode impedance are generated with a pseudo-random distribution and used to introduce errors in boundary voltage measurements. To simplify the analysis, the non-idealities in the current injection circuit are replaced by a fixed common-mode error term. The boundary voltages are reconstructed into images and inspected. Where the simulated skin impedance remains constant between measurements, large impedances (> 2k omega) do not cause significant degradation of the image. Where the skin impedances 'drift' between measurements, a drift of 5% from a starting impedance of 100 omega is sufficient to cause significant image distortion. If the skin impedances vary randomly between measurements, they have to be less than 10 omega to allow satisfactory images. Skin impedances are typically 100-200 omega at 50 kHz on unprepared skin. These values are sufficient to cause image distortion if they drift over time. It is concluded that the patient's skin should be abraded to reduce impedance, and measurements should be avoided in the first 10 min after electrode placement.

Speckle reducing anisotropic diffusion

Abstract: This paper provides the derivation of speckle reducing anisotropic diffusion (SRAD), a diffusion method tailored to ultrasonic and radar imaging applications. SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. We first show that the Lee and Frost filters can be cast as partial differential equations, and then we derive SRAD by allowing edge-sensitive anisotropic diffusion within this context. Just as the Lee (1980, 1981, 1986) and Frost (1982) filters utilize the coefficient of variation in adaptive filtering, SRAD exploits the instantaneous coefficient of variation, which is shown to be a function of the local gradient magnitude and Laplacian operators. We validate the new algorithm using both synthetic and real linear scan ultrasonic imagery of the carotid artery. We also demonstrate the algorithm performance with real SAR data. The performance measures obtained by means of computer simulation of carotid artery images are compared with three existing speckle reduction schemes. In the presence of speckle noise, speckle reducing anisotropic diffusion excels over the traditional speckle removal filters and over the conventional anisotropic diffusion method in terms of mean preservation, variance reduction, and edge localization.

Speckle in ultrasound B-mode scans

Abstract: Ultrasound images obtained with a simple linear or sector scan show a granular appearance, called "speckle." This speckle is analyzed. The reduction in speckle that can be obtained with a compound scan with maximum amplitude writing is computed. The reduction in speckle is almost as large as can be obtained with averaging. It depends on the number of independent amplitude values that are measured. The condition for the independence of two amplitude values is derived, and thus a limit is given for the possible reduction in speckle.