Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU147003" target="_blank" >RIV/00216305:26220/23:PU147003 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/23/3/1167" target="_blank" >https://www.mdpi.com/1424-8220/23/3/1167</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23031167" target="_blank" >10.3390/s23031167</a>

Result language

angličtina

Original language name

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Original language description

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SENSORS

ISSN

1424-8220

e-ISSN

1424-3210

Volume of the periodical

23

Issue of the periodical within the volume

3

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

1-16

UT code for WoS article

000930307600001

EID of the result in the Scopus database

2-s2.0-85147895425