Speckle reduction and deblurring of ultrasound images using artificial neural network

Uddin, Muhammad Shahin; Halder, Kalyan Kumar; Tahtalı, Murat; Lambert, Andrew; Pickering, Mark

doi:10.1109/pcs.2015.7170056

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…SN reduction technique has been developed for 2-D US imaging based on an artificial neural network. This method can suppress the blur and speckle noise correctly [14]. Fast removal of SN in real US images has been described by combining Adaptive filtering and Bergman iterative technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

Karthiha¹,

Allwin²

2023

Intelligent Automation &Amp; Soft Computing

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Section: Literature Reviewmentioning

confidence: 99%

Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

Karthiha¹,

Allwin²

2023

Intelligent Automation &Amp; Soft Computing

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“…Fatty liver disease relates to the category of diffused liver diseases that is caused due to the enormous deposit of triglycerides and other fat types in the liver cells. Inspite of the potential characteristics of ultrasonic images, the activity involved in classifying the normal cells from infected cells of the liver is influenced by minimum contrast, close appearances and hazy nature of images [5][6][7]. Inherently, the ultrasonic image of one liver disease may closely resemble the image of other liver disease or the similar ultrasonic images of the same liver disorder may exhibit different textures.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

Rajesh¹

2018

CVRJST

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In the treatment of abdominal diseases like fatty liver disorder, ultrasonic images-based investigation is considered as the primary step of diagnosis. But, the noisy feature of ultrasonic images combined with the least contrasting features introduces maximum complexity during the process of automated classification. This paper contributes an Improved Active Contour Segmentation scheme for effective segmentation. Then Gray Level Co-occurrence Matrix (GLCM) and fractal features are extracted from the segmented ultrasonic images and the classification is achieved using an enhanced forest classification incorporated in the artificial neural networks (ANN) for accurate detection. The results of the proposed automated approach are investigated using classification accuracy, mean classification accuracy, true positive rate, false positive rate and true negative rate. The results of the proposed scheme also infers a classification accuracy rate of 98.73% and a mean classification accuracy rate of 97.65% compared to the baseline automated liver disorder classification techniques.

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An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems

Al‐Tahhan,

Fares

2024

Microscopy Res & Technique

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Ultrasound images are susceptible to various forms of quality degradation that negatively impact diagnosis. Common degradations include speckle noise, Gaussian noise, salt and pepper noise, and blurring. This research proposes an accurate ultrasound image denoising strategy based on firstly detecting the noise type, then, suitable denoising methods can be applied for each corruption. The technique depends on convolutional neural networks to categorize the type of noise affecting an input ultrasound image. Pre‐trained convolutional neural network models including GoogleNet, VGG‐19, AlexNet and AlexNet‐support vector machine (SVM) are developed and trained to perform this classification. A dataset of 782 numerically generated ultrasound images across different diseases and noise types is utilized for model training and evaluation. Results show AlexNet‐SVM achieves the highest accuracy of 99.2% in classifying noise types. The results indicate that, the present technique is considered one of the top‐performing models is then applied to real ultrasound images with different noise corruptions to demonstrate efficacy of the proposed detect‐then‐denoise system.Research Highlights Proposes an accurate ultrasound image denoising strategy based on detecting noise type first. Uses pre‐trained convolutional neural networks to categorize noise type in input images. Evaluates GoogleNet, VGG‐19, AlexNet, and AlexNet‐support vector machine (SVM) models on a dataset of 782 synthetic ultrasound images. AlexNet‐SVM achieves highest accuracy of 99.2% in classifying noise types. Demonstrates efficacy of the proposed detect‐then‐denoise system on real ultrasound images.

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Speckle reduction and deblurring of ultrasound images using artificial neural network

Cited by 10 publications

References 16 publications

Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

An Efficient and Automated Classification Scheme for Diagnosing Fatty Liver Disorder using Ultrasonic Images

An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems

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