Medical image denoising using multi-resolution transforms

In Medical Image Processing (MIP), the images are influenced by various types of noises. Hence, reducing such noises becomes a crucial issue. The main objective is to denoise the noisy images effectively with reduced computational cost. This paper proposes a denoising technique using Discrete Wavelet Transform (DWT) and Social Spider Optimization (SSO) algorithm. Initially, DWT is applied over various noises over the input medical images. Then, the wavelet coefficients are optimized by applying the SSO algorithm. Finally, the inverse of DWT (IDWT) is applied over the optimized coefficients. The denoised image is obtained, and then, the PSNR evaluation is utilized for finding the superior performance. Experimental results using MATLAB show that the proposed algorithm has better performance when compared with the existing approaches.

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“…Further medical denoising approaches using Autoencoders 21 and multi-resolution transforms 22 were also proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

Aravindan

Seshasayanan

2019

Concurrency and Computation

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“…The modern digital era opens up new opportunities for identifying depression through medical imaging [3], social media forums, etc. Social Media enables the people to express their thoughts of depression in public or only to their interested circle.…”

Section: Introductionmentioning

confidence: 99%

EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

Priya¹,

Karthika²

2023

Intelligent Automation &Amp; Soft Computing

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Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network (LSTM) model. The feature extraction process combines a novel feature selection method called Elite Term Score (ETS) and Word2Vec to extract the syntactic and semantic information respectively. First, the ETS method leverages the document level, class level, and corpus level probabilities for computing the weightage/score of the terms. Then, the ideal and pertinent set of features with a high ETS score is selected, and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms. Finally, the resultant word vector obtained is called EliteVec, which is fed to the hybrid LSTM model based on Honey Badger optimizer with population reduction technique (PHB) which predicts whether the input textual content is depressive or not. The PHB algorithm is integrated to explore and exploit the optimal hyperparameters for strengthening the performance of the LSTM network. The comprehensive experiments are carried out with two different Twitter depression corpus based on accuracy and Root Mean Square Error (RMSE) metrics. The results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1% accuracy and 0.0559 RMSE.

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“…A threshold-based technique is employed to reduce noise, and a locally adaptive threshold-based edge preservation denoising scheme is used [21]. Digital image transformation [22] techniques are applied to remove noise from medical images. A hybrid technique of a modified median filter and a Wiener filter [23] was used to restore the noise from images using morphological image processing operations.…”

Section: Introductionmentioning

confidence: 99%

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Thomas

Krishna

2022

IJEECS

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Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio and structural similarity index metrics are determined to prove the performance of the proposed method. The evaluated results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the DICOM image for diagnosis purposes.

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Medical image denoising using multi-resolution transforms

Cited by 19 publications

References 16 publications

Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

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