Visual Quality Of Ultrasound Images Research Articles

Laplacian pyramid based non-linear coherence diffusion for real-time ultrasound image speckle reduction

The inherent speckle noise in medical ultrasound image degrades valuable clinical information and affects the diagnosis quality. Therefore, commercial ultrasound systems always face a big challenge to improve the visual quality of ultrasound images by reducing the speckle noise. A new hybrid algorithm is proposed in the present paper for speckle reduction by combining nonlinear coherence diffusion (NCD) with pyramid-transform-based multi-scale filters (named as Laplacian Pyramid Based Non-linear Coherence Diffusion; viz. LPNCD). The new approach exhibits good capability in preserving tissue features while enhancing the coherence. The theory and implementation of the approach are presented and verified based on phantom and clinical data. By comparing with previous techniques on quantified signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), the results suggest that the LPNCD method may demonstrate superior performance than other commonly used AD-class filters. Meanwhile, the performance of GPU implementations of the current technique is also evaluated, and it is verified that the LPNCD approach may have good performance efficiency and wonderful visual effect as running on mainstream commercial ultrasound systems.

Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images

The speckle noise commonly occurs in ultrasound imaging based applications. Due to the multiplicative nature, speckle noise deteriorates the visual quality of ultrasound images. This affects the performance of radiologists and practitioners for disease diagnosis and/or patient treatment. The current study proposes a bio-inspired multi-gene genetic programming (MGGP) based intelligent estimator to reduce the speckle noise from ultrasound images. The proposed MGGP approach is based on the parallel framework of multiple genes and has effectively utilized the evolutionary learning capabilities to develop an intelligent estimator, by exploiting the useful statistical features extracted from local neighboring pixels. The performance of the proposed novel approach is evaluated on ultrasound images of common carotid artery corrupted with different noise levels. Further, the robust performance was validated on several diverse types of ultrasound images of Breast Cyst, Kidney Cancer, Liver, Liver Cyst, and Fetal Head. The proposed bio-inspired approach showed superior denoising performance over existing approaches. The proposed intelligent estimator is capable of removing speckle noise effectively while preserving the fine lines and edges. During evolution, the MGGP framework automatically selects the useful statistical features and primitive functions from a wider solution space to develop the intelligent estimator. Further, the proposed approach does not require image-dependent optimal threshold values, as conventional speckle denoising approaches required.

A Density Current Modeled Adaptive Weighted Average Despeckling Filter for Ultrasound Thyroid Images

Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, an adaptive weighted average despeckling algorithm is proposed based on density currents equation. An analogy between the gravity current density model for sea breeze and speckle noise affected image is used and a denoising filter is designed. Suitable threshold values to distinguish between different regions in the image are selected for the denoising filter by considering the structural similarity and mean square error parameters of the particular image. Hence the proposed denoising filter adaptively changes the threshold values depending on nature of image and magnitude of noise in the image. For each pixel, weights have been changed adaptively by considering local neighbours in different directions. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method outperforms other existing methods in terms of signal to noise ratio, Structural similarity index and Ultrasound despeckling assessment index. It is also shown that proposed filter is able to improve the visual quality of Ultrasound images and better preserves image structural details compared to existing methods.

A Technique to Suppress Speckle in Ultrasound Images using Nonlocal Mean and Cellular Automata

Kidney ultrasound imaging is an economical, non invasive, real time diagnosis system which is used to measure abnormalities in the shape, size and location of the kidneys in the body. The presence of speckle noise degrades the visual quality of ultrasound images. The primary objective of the research under taken is to detect the renal stone in ultrasound images. The present research paper discusses an algorithm based on cellular automata and nonlocal mean for suppression of the speckle noise. The cellular automatons are dynamic systems to represent the particular problem in terms of specific pattern. This concept is exploited to distinguish the noise from the object being target. The kidney ultrasound images are denoised to detect presence of renal stone in the image. The concept behind the nonlocal mean is an assumption that the pixel being considered for denosing has strong connection with the surrounding area rather than the exactly nearly by pixels. The MLNC (Maximum Likelihood Neighborhood Computation) rules are applied to find the nearest value in the neighbour. The BEP (Border, Edge Preservation) rules are used to safeguard the edges between the regions. The samples of 55 clinical images of ultrasound are used for statistical analysis which is carried out by Signal to Noise Ratio (SNR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR). The result analysis shows MSE = 216.789, SNR = 49.1569 and PSNR = 68.022. The algorithm not only enhances the visual quality but also suppress unwanted speckle noise. The algorithm is dynamic in nature and can be used to de-noise SAR images, MRI and X-rays image. The method is adaptive in nature as cellular modeling can model low noise level.

Adaptive image inversion of contrast 3D echocardiography for enabling automated analysis

IntroductionContrast 3D echocardiography (C3DE) is commonly used to enhance the visual quality of ultrasound images in comparison with non-contrast 3D echocardiography (3DE). Although the image quality in C3DE is perceived to be improved for visual analysis, however it actually deteriorates for the purpose of automatic or semi-automatic analysis due to higher speckle noise and intensity inhomogeneity. Therefore, the LV endocardial feature extraction and segmentation from the C3DE images remains a challenging problem. MethodsTo address this challenge, this work proposes an adaptive pre-processing method to invert the appearance of C3DE image. The image inversion is based on an image intensity threshold value which is automatically estimated through image histogram analysis. ResultsIn the inverted appearance, the LV cavity appears dark while the myocardium appears bright thus making it similar in appearance to a 3DE image. Moreover, the resulting inverted image has high contrast and low noise appearance, yielding strong LV endocardium boundary and facilitating feature extraction for segmentation. ConclusionsOur results demonstrate that the inverse appearance of contrast image enables the subsequent LV segmentation.

SPECKLE REDUCTION IN ULTRASOUND IMAGES USING NEIGHSHRINK AND BILATERAL FILTERING

Speckle is a random multiplicative noise which obscures the perception and extraction of fine details in ultrasound images and speckle reduction is necessary to improve the visual quality of ultrasound images for better diagnosis. This study aims at introducing an algorithm by hybridizing bilateral filter with NeighShrink. The bilateral filter is applied before decomposition and after reconstruction of the image using discrete wavelet transform to improve the denoising efficiency and preserve the edge features effectively. The wavelet thresholding scheme NeighShrink is used for thresholding of wavelet coefficients. The algorithm is tested with synthetically speckled and real ultrasound images. Quality evaluation metrics such as Peak Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Correlation Coefficient (CoC) are used to assess the performance of the proposed method. Experimental results show that the proposed scheme improves the visual quality of ultrasound images by suppressing the speckle noise while retaining edges.

Wavelet based Despeckling of Medical Ultrasound Images using Speckle Reducing Anisotropic Diffusion and Local Wiener Filtering

Multiplicative speckle noise which is inherently present in medical ultrasound images degrades the important clinical informations and badly affects the quality of the diagnosis. It is necessary to reduce the speckle noise to improve the visual quality of ultrasound images for better diagnoses. In this paper, a wavelet based method for despeckling of the ultrasound images is introduced where a local Wiener filter along with speckle reducing anisotropic diffusion (SRAD) filter are employed in a homomorphic framework. The signal variance in the local wiener filter is estimated from the output image of the SRAD filter. Since the size and shape of the locally adaptive window is an important issue in estimating the signal variance, nearly arbitrarily shaped windows are used for better performance. The experimental results using synthetically speckled ultrasound images show that the speckle noise is reduced to a great extent while preserving the important clinical information. In order to demonstrate the effectiveness of the proposed method, the method is compared with several other existing methods in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), edge preservation index ( ), and standard deviation to mean (S/M) ratio.

Enhancement of the ultrasound images by modified anisotropic diffusion method

Speckle is a primary factor which degrades the contrast resolution and masks the meaningful texture information present in an ultrasound image. Its presence severely hampers the interpretation and analysis of ultrasound images. When speckle reduction technique is applied for visual enhancement of ultrasound images, it is to be kept in mind that blurring associated with speckle reduction should be less and fine details are properly enhanced. With these points in consideration, the modified speckle reduction anisotropic diffusion (MSRAD) method is proposed in the present study to improve the visual quality of the ultrasound images. In the proposed MSRAD method, the four neighboring pixel template in speckle reduction anisotropic diffusion (SRAD) method of Yu and Acton (IEEE Trans Image Process 11:1260-1270, 2002) have been replaced by a new template of larger number of neighboring pixels to calculate the diffusion term. To enhance visual quality of ultrasound images, nonquadratic regularization (Yu and Yadegar, Proceedings of the IEEE international conference on image processing, 2006) is incorporated with MSRAD method and accordingly changes in parameter settings have been made. The performance of MSRAD method was evaluated using clinical ultrasound images, interpretation by the medical experts and results of MSRAD method by subjective and objective criteria.

Intelligence techniques for prostate ultrasound image analysis

In this paper we present an intelligent scheme, employing a combination of fuzzy logic, pulse coupled neural networks (PCNNs), wavelets and rough sets, for analysing prostrate ultrasound images in order diagnose prostate cancer. Image noise is a principal factor which hampers the visual quality of ultrasound images and can therefore lead to misdiagnosis. To address this issue we first utilise an algorithm based on type-II fuzzy sets to enhance the contrast of the image. This is followed by performing PCNN-based segmentation in order to identify the region of interest and to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalised, followed by application of a rough set analysis to discover the dependency between the attributes, and to generate a set of reducts consisting of a minimal number of attributes. Finally, a rough set classifier is designed for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of our approach, we present tests on different prostate ultrasound images. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other intelligent techniques including decision trees, discriminant analysis, rough neural networks, fuzzy ARTMAP, and neural networks.

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.