A hybrid algorithm for speckle noise reduction of ultrasound images

Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images. Numerous de-speckling schemes were established to date that concern speckle reduction; however, the models suffer from demerits like computational time, computational complexity, etc., that are to be rectified as soon as possible. This compulsion takes to the introduction of a new de-speckling model via an adaptive hybrid filter model that includes four filters like guided filter (GF), speckle-reducing bilateral filter (SRBF), rotation invariant bilateral nonlocal means filter (RIBNLM), and median filter (MF) respectively. Moreover, the novelty goes under the selection of optimal filter coefficients that make the process effective. Bayesian-based neural network is used to predict the appropriate filter coefficients, where the training library is constructed with the optimal coefficients. Along with this, the selection of optimal filter coefficients is done under the defined objective function using a new hybrid algorithm termed as Randomized FireFly (FF) update in Lion Algorithm (RFU-LA) that hybrids the concept of both LA and FF, respectively. Finally, the performance of the proposed de-speckling model is compared over that of other conventional models with respect to different performance measures. Accordingly, from the analysis, the mean MAPE of the proposed method are 39.13% and 49.28% higher than those of the wavelet filtering and hybrid filtering schemes for a noise variance of 0.1.

Methods for image denoising using convolutional neural network: a review

Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

Frequency division denoising algorithm based on VIF adaptive 2D-VMD ultrasound image

Ultrasound imaging has developed into an indispensable imaging technology in medical diagnosis and treatment applications due to its unique advantages, such as safety, affordability, and convenience. With the development of data information acquisition technology, ultrasound imaging is increasingly susceptible to speckle noise, which leads to defects, such as low resolution, poor contrast, spots, and shadows, which affect the accuracy of physician analysis and diagnosis. To solve this problem, we proposed a frequency division denoising algorithm combining transform domain and spatial domain. First, the ultrasound image was decomposed into a series of sub-modal images using 2D variational mode decomposition (2D-VMD), and adaptively determined 2D-VMD parameter K value based on visual information fidelity (VIF) criterion. Then, an anisotropic diffusion filter was used to denoise low-frequency sub-modal images, and a 3D block matching algorithm (BM3D) was used to reduce noise for high-frequency images with high noise. Finally, each sub-modal image was reconstructed after processing to obtain the denoised ultrasound image. In the comparative experiments of synthetic, simulation, and real images, the performance of this method was quantitatively evaluated. Various results show that the ability of this algorithm in denoising and maintaining structural details is significantly better than that of other algorithms.

Improved non-local self-similarity measures for effective speckle noise reduction in ultrasound images

BACKGROUND AND OBJECTIVE

In the observed medical ultrasound image, there is always some speckle noise which suppress the details of images and impairs the value of ultrasonography in diagnosis. This work present a novel despeckling method which effectively exploit non-local self-similarity for restoration of corrupted ultrasound images. The proposed approach consist of three stages. First, an improved optimized Bayesian non-local means (OBNLM) filter in which pixel patch is represented by a new vector form is used to get an preliminary estimation of noise-free image. Then, a new index called redundancy index of each pixel patch is calculated for determining which areas in image have low redundancy. Finally, another new vector form is used to represent pixel patch in areas with low redundancy obtained in second stage to recalculate filtered output, and the recalculated output is superimposed on preliminary estimation to generate final result of proposed method.

METHODS

The performance of proposed approach is evaluated on simulated and real ultrasound images. The experiments conducted on various test image illustrate that our proposed algorithm outperforms the various classic denoising algorithms included block matching 3-D (BM3D) and optimized Bayesian non-local means filter.

RESULTS

The objective evaluations and subjective visual inspection of denoised simulated and real ultrasound images demonstrate that the proposed algorithm can achieve superior performance than previously developed methods for speckle noise suppression.

CONCLUSIONS

The combined use of two new representations improve denoising and edge preserving capability of proposed filter apparently. The success of proposed algorithm would help in building the lay foundation for inventing the despeckling algorithms that can make fuller use of information in images.

Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering

Organ segmentation is an important step in Ultrasound fetal images for early prediction of congenital abnormalities and to estimate delivery date. In many applications of 2D medical imaging, they face problems with speckle noise and object contours. Frequent scanning of fetal leads to clinical disturbances to the fetal growth and the quantitative interpretation of Ultrasonic images also a difficult task compared to other image modalities. In the present work a three-stage hybrid algorithm has been developed to segment the US fetal kidney images for the detection of shape and contour. At the first stage the hybrid Mean Median (Hybrid MM) filter is applied to reduce the speckle noise. Then a kernel based Fuzzy C - means clustering is used to detect the shape and contour. Finally, the texture features are obtained from the segmented images. Based on the obtained texture features, the abnormalities are detected. The Gaussian Radial basis function provides an accuracy of 80% at the second and third trimesters with weighted constant ranging from 4 to 8, compared to other global kernel functions. Similarly the proposed method has an accuracy of 86% with compared to other FCM techniques.