Boundary Enhancement and Speckle Reduction for Ultrasound Images via Salient Structure Extraction

Investigation on ultrasound images for detection of fetal congenital heart defects

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding

Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained. The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate. The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.

Carotid artery ultrasound image analysis: A review of the literature

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging

BACKGROUND AND OBJECTIVE

Low-dose X-ray fluoroscopy has continually evolved to reduce radiation risk to patients during clinical diagnosis and surgery. However, the reduction in dose exposure causes quality degradation of the acquired images. In general, an X-ray device has a time-average pre-processor to remove the generated quantum noise. However, this pre-processor causes blurring and artifacts within the moving edge regions, and noise remains in the image. During high-pass filtering (HPF) to enhance edge detail, this noise in the image is amplified.

METHODS

In this study, a 2D edge enhancement algorithm comprising region adaptive HPF with the transient improvement (TI) method, as well as artifacts and noise reduction (ANR), was developed for degraded X-ray fluoroscopic images. The proposed method was applied in a static scene pre-processed by a low-dose X-ray fluoroscopy device. First, the sharpness of the X-ray image was improved using region adaptive HPF with the TI method, which facilitates sharpening of edge details without overshoot problems. Then, an ANR filter that uses an edge directional kernel was developed to remove the artifacts and noise that can occur during sharpening, while preserving edge details.

RESULTS

The quantitative and qualitative results obtained by applying the developed method to low-dose X-ray fluoroscopic images and visually and numerically comparing the final images with images improved using conventional edge enhancement techniques indicate that the proposed method outperforms existing edge enhancement methods in terms of objective criteria and subjective visual perception of the actual X-ray fluoroscopic image.

CONCLUSIONS

The developed edge enhancement algorithm performed well when applied to actual low-dose X-ray fluoroscopic images, not only by improving the sharpness, but also by removing artifacts and noise, including overshoot.

OCT skin image enhancement through attenuation compensation

The enhancement of optical coherence tomography (OCT) skin images can help dermatologists investigate the morphologic information of the images more effectively. In this paper, we propose an enhancement algorithm with the stages that includes speckle reduction, skin layer detection, and attenuation compensation. A weighted median filter is designed to reduce the level of speckle while preserving the contrast. A novel skin layer detection technique is then applied to outline the main skin layers: stratum corneum, epidermis, and dermis. The skin layer detection algorithm does not make any assumption about the structure of the skin. A model of the light attenuation is then used to estimate the attenuation coefficient of the stratum corneum, epidermis, and dermis layers. The performance of the algorithm has been evaluated qualitatively based on visual evaluation and quantitatively using two no-reference quality metrics: signal-to-noise ratio and contrast-to-noise ratio. The enhancement algorithm is tested on 35 different skin OCT images, which show significant improvements in the quality of the images, especially in the structures at deeper levels.

Symmetry and appearance based automated detection of salient anatomical regions in ultrasound

In this paper we present a method for automated detection of enclosed anatomical regions in ultrasound images by utilizing the coarse shape symmetry as well as relative homogeneity of their sonographic appearance. The proposed method comprises of two steps: First, local phase based filtering [2] is used to detect points in the image which are roughly positioned along the axes of spatial symmetry with respect to structures around them. Secondly, the sonographic 'appearance' and location of these points is used to define a distance-map on the image, which is supplied to a simple fast-marching algorithm in order to provide the final feature detections. The method is robust to ultrasound speckle and works well with or without specialized pre-processing (e.g. speckle-reduction filtering). We illustrate the proposed method with qualitative results on in-vivo Ultrasound images.

Ultrasound despeckling for contrast enhancement

Images produced by ultrasound systems are adversely hampered by a stochastic process known as speckle. A despeckling method based upon removing outlier is proposed. The method is developed to contrast enhance B-mode ultrasound images. The contrast enhancement is with respect to decreasing pixel variations in homogeneous regions while maintaining or improving differences in mean values of distinct regions. A comparison of the proposed despeckling filter is compared with the other well known despeckling filters. The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo.

Denoising and 4D visualization of OCT images

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of th new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications.