Denoising Ultrasound Medical Images

Sastry, Apurba; Miee, P.V.V.Kishore; Prasad, Ch. Raghava; Prasad, M. V. D.

doi:10.4018/ijmtie.2015010101

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Kishore et al, 29 have endeavored to retrieve the image quality of medical ultrasound pictures through a difficult and smooth wavelet thresholding block. Using the mother wavelet of debauchee, medical ultrasound image is converted to the wavelet domain.…”

Section: Literature Surveymentioning

confidence: 99%

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A new image restoration approach by combining empirical wavelet transform and total variation using chaotic squirrel search optimization

Kumar

Narasimhulu²,

Prasad

2020

Int J Numerical Modelling

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Image noise is the random variation of brightness or color information in the images. Noise can enter the picture during capturing or transmitting. Different linear and nonlinear methods have been implemented to remove the noise from an image, but transformation of wavelets is becoming increasingly important. We propose a fundamental concept for disintegrating the input bubble, sound, and movement signal of the object/camera into appropriate coefficients by transforming the empirical wavelet and making full changes. A new messy screen search strategy is used to assess the variability at each pixel of the decomposed picture. Empirical wavelet is a kind of wavelet tailored to the data being handled. This is an adaptive technique for decomposing the signal or picture to a set of sections recognized as phases. This wavelet transform is relevant for de‐noising, decompression, etc. Chaotic squirrel search optimizers imitate the vibrant feeding behavior and efficiency of northern flying squirrels, called gliding. Gliding is a powerful system used for lengthy ranges by tiny mammals. Compared to other wavelet transformations, the median peak signal for noise ratio (PSNR) enhancement obtained by EWT is 73%. Numerical tests indicate that our proposed method could be able to substantially enhance restored pictures' efficiency and achieve greater SNR and structural similarity index scores likened to the present state of the art methodologies.

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Section: Literature Surveymentioning

confidence: 99%

“…The SSO standard needs the entire population to be updated during the summer, at the start of the iteration. The change in climate is assessed in Equations (28) and (29) after all the people have been updated:…”

Section: Judging and Random Updating The Seasonal Transformationmentioning

confidence: 99%

A new image restoration approach by combining empirical wavelet transform and total variation using chaotic squirrel search optimization

Kumar

Narasimhulu²,

Prasad

2020

Int J Numerical Modelling

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Using LoRa technology for agricultural monitoring system

Prasad,

Daniel,

Rupa

et al. 2024

INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING &Amp; COMMUNICATION ENGINEERING SYSTEMS: SPACES-2021

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Speech Emotion Recognition Using Deep Learning LSTM for Tamil Language

Fernandes¹,

Mannepalli²

2021

JST

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Deep Neural Networks (DNN) are more than just neural networks with several hidden units that gives better results with classification algorithm in automated voice recognition activities. Then spatial correlation was considered in traditional feedforward neural networks and which do not manage speech signal properly to it extend, so recurrent neural networks (RNNs) were implemented. Long Short-Term Memory (LSTM) systems is a unique case of RNNs for speech processing, thus considering long-term dependencies Deep Hierarchical LSTM and BiLSTM is designed with dropout layers to reduce the gradient and long-term learning error in emotional speech analysis. Thus, four different combinations of deep hierarchical learning architecture Deep Hierarchical LSTM and LSTM (DHLL), Deep Hierarchical LSTM and BiLSTM (DHLB), Deep Hierarchical BiLSTM and LSTM (DHBL) and Deep Hierarchical dual BiLSTM (DHBB) is designed with dropout layers to improve the networks. The performance test of all four model were compared in this paper and better efficiency of classification is attained with minimal dataset of Tamil Language. The experimental results show that DHLB reaches the best precision of about 84% in recognition of emotions for Tamil database, however, the DHBL gives 83% of efficiency. Other design layers also show equal performance but less than the above models DHLL & DHBB shows 81% of efficiency for lesser dataset and minimal execution and training time.

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Denoising Ultrasound Medical Images

Cited by 4 publications

References 30 publications

A new image restoration approach by combining empirical wavelet transform and total variation using chaotic squirrel search optimization

A new image restoration approach by combining empirical wavelet transform and total variation using chaotic squirrel search optimization

Using LoRa technology for agricultural monitoring system

Speech Emotion Recognition Using Deep Learning LSTM for Tamil Language

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