Adaptive Despeckling and Heart Disease Diagnosis by Echocardiogram using Optimized Deep Learning Model

Chamundeshwari,; Biradar, Nagashattappa; Udaykumar,

doi:10.1080/21681163.2022.2032361

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…The pictures and video frames from echocardiography are combined in Ref. 44 to provide a new method of diagnosing heart disease. Noise reduction is the first step in the procedure since speckle noise is the primary issue with echocardiography imaging.…”

Section: Related Workmentioning

confidence: 99%

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Medical diagnostics rely heavily on ultrasound imaging since it is non-invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal-dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non-homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non-homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two-step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non-subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

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Section: Related Workmentioning

confidence: 99%

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Singh

Diwakar

2023

Int J Imaging Syst Tech

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Lung-UNet: A Modified UNet-Based DNN for COVID Lung Segmentation from Chest X-Ray and CT-Scan Images

Saha

2024

Communications in Computer and Information Science

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Optimized Pyramidal Convolution Shuffle attention neural network based fetal cardiac cycle detection from echocardiograms

Balaji,

2024

Biomedical Signal Processing and Control

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Adaptive Despeckling and Heart Disease Diagnosis by Echocardiogram using Optimized Deep Learning Model

Cited by 6 publications

References 35 publications

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

Lung-UNet: A Modified UNet-Based DNN for COVID Lung Segmentation from Chest X-Ray and CT-Scan Images

Optimized Pyramidal Convolution Shuffle attention neural network based fetal cardiac cycle detection from echocardiograms

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