Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images

Shereena, V. B.; Raju, G.

doi:10.1007/978-981-16-9113-3_51

Cited by 5 publications

(1 citation statement)

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“…The autocoder network is built on a convolutional neural network (CNN) with dilated convolution layers. Researchers have created a hybrid strategy that makes use of Kuan and non-local means filters to reduce noise (as stated in [ 3 ]). They use a Kuan filter to first sharpen the edges, and then non-local techniques to eliminate the speckle noise.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Amarnath,

Manoharan,

Natarajan

et al. 2023

Diagnostics

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Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.

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Section: Related Workmentioning

confidence: 99%