A review on medical image denoising algorithms

Control Of Spacecraft And Aircraft

Train rail defect classification detection and its parameters learning method

We propose an ultrasound speckle filtering method for not only preserving various edge features but also filtering tissue-dependent complex speckle noises in ultrasound images. The key idea is to detect these various edges using a phase congruence-based edge significance measure called phase asymmetry (PAS), which is invariant to the intensity amplitude of edges and takes 0 in non-edge smooth regions and 1 at the idea step edge, while also taking intermediate values at slowly varying ramp edges. By leveraging the PAS metric in designing weighting coefficients to maintain a balance between fractional-order anisotropic diffusion and total variation (TV) filters in TV cost function, we propose a new fractional TV framework to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters. Then, we exploit the PAS metric in designing a new fractional-order diffusion coefficient to properly preserve low-contrast edges in diffusion filtering. Finally, different from fixed fractional-order diffusion filters, an adaptive fractional order is introduced based on the PAS metric to enhance various weak edges in the spatially transitional areas between objects. The proposed fractional TV model is minimized using the gradient descent method to obtain the final denoised image. The experimental results and real application of ultrasound breast image segmentation show that the proposed method outperforms other state-of-the-art ultrasound despeckling filters for both speckle reduction and feature preservation in terms of visual evaluation and quantitative indices. The best scores on feature similarity indices have achieved 0.867, 0.844 and 0.834 under three different levels of noise, while the best breast ultrasound segmentation accuracy in terms of the mean and median dice similarity coefficient are 96.25% and 96.15%, respectively.

Phase Asymmetry Ultrasound Despeckling With Fractional Anisotropic Diffusion and Total Variation

Ultrasonography is non-invasive and painless. In Ultrasonography the images are often affected with Speckle noise. It is a multiplicative noise. To help the doctors to identify the abnormalities properly there are several methods to diagnose as speckle is a major problem. This paper gives details about popular spatial domain, transform domain, CNN techniques for despeckling in ultrasound images. Transform domain methods like Wavelet methods, Curvelet methods, Bayes Shrink methods are prominent among many researches. Deep learning based methods are evolving like DnCNN, ECNDNet etc. for efficient despeckling. An overview of the methods is given here with certain measurement parameters like PSNR, MSE.

A Review on Speckle Noise Reduction Techniques in Ultrasound Medical images based on Spatial Domain, Transform Domain and CNN Methods

A new wavelet family for speckle noise reduction in medical ultrasound images

This paper presents an analytical redundancy approach for fault detection on a simulated hovering helicopter. For this purpose, a standard state-observer fault-detection technique is extended to adopt a commensurate fractional-order model. The results are favorably compared with those of an integer-order state observer, in terms of after-fault residue amplification and area under the receiver operating characteristic (ROC) curve.

A Fractional-Order Analytical Redundancy Approach for Fault Detection on a Hovering Helicopter

In industry 4.0 there is a growth in the Industrial Internet of Things (iIoT) with a lot of information generation and consequent big data challenges. Thus, it is imperative to have techniques able to process all this data and predict the maintenance of equipment and systems. The development of algorithms for remaining useful life (RUL) estimators is critical for the full functioning of the company’s assets. Especially the aeronautical sector needs to guarantee safety and quality flights. The turbofan, a propulsion engine, is a critical element for an airplane operation. This paper proposes a model to perform prediction of the remaining useful life of an aircraft’s turbo engine. In this work, we focus on the run-to-failure data from an N-CMAPSS turbofan, the data used were provided by NASA in 2021. After training and validating different algorithms such as MLP and CNN, we find CNN as the best approach with an RMSE of 9.11, a score of 5.14, and computed score of 1.17. The results have improved when compared to the literature over 25% in RMSE and 15% in computed score.

Andreia Seixas Leal

Papers

A new wavelet family for speckle noise reduction in medical ultrasound images

A Fractional-Order Analytical Redundancy Approach for Fault Detection on a Hovering Helicopter

Learning methods for remaining useful life prediction in a turbofan engine