Analysis of Speckle Diminution in Ultrasound Images—A Review

Tilakraj, N.; Ravikumar, K. M.

doi:10.1007/978-981-13-5802-9_82

Cited by 2 publications

(2 citation statements)

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“…If , then the median value is . If , then is the noise and is the original image, where the median is given as 7The noise and image are uncorrelated and that optimizes the filter so that MSE is minimized (8) The degradation model of wiener filter is represented as (9) 10Where, is the Additive Noise, is the degradation function, is the degraded image.…”

Section: Neural System-based Hybrid Filter Modelmentioning

confidence: 99%

“…The image of ultrasound can be adjusted, moved, and is moderately safe, these types of images comprise of speckle noise and artifacts leading to degradation of the organ reducing the contrast. Human perception is affected for recognizing pathological tissue [8]. High frequency waves of sound are utilised for viewing the internal body parts using Ultrasound Imaging.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Neural Filter (HNF) Based on Adaptive Median and Weiner Techniques for Reducing Speckle Noise of Ultrasound Liver Tumor Images

Uplaonkar*¹,

Amarapur²

2019

IJRTE

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Pre-processing of image is considered to be an important aspect medical image analysis in order to enhance its quality. Noise reduction is performed for improvement in the visual quality and removing redundant image values. Ways of pre-processing of image are a necessity, for removing the noise and for quality enhancement of the image. Before applying any approach on medical images, measures used to pre-process seem to be vital for limiting the abnormalities’ findings with no influence from background of the medical image. In this work, a filter is proposed based on Adaptive Median and Weiner Hybrid Neural Filter for noise reduction. The review of filtering techniques is implemented using MATLAB platform, followed by comparison of results with different filtering techniques to show the system effectiveness.

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Section: Neural System-based Hybrid Filter Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%