Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique
Loading...
Date
2023-01-19
Authors
Vimala, Baiju Babu
Srinivasan, Saravanan
Mathivanan, Sandeep Kumar
Muthukumaran, Venkatesan
Babu, Jyothi Chinna
Herencsár, Norbert
Vilcekova, Lucia
ORCID
0000-0002-9504-2275Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Altmetrics
Abstract
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.
Description
Citation
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en