Publication detail

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

VIMALA, B. B. SRINIVASAN, S. MATHIVANAN, S. K. MUTHUKUMARAN, V. BABU, J. C. HERENCSÁR, N. VILCEKOVA, L.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

local speckle noise destruction; hybrid deep learning technique; logical-pool recurrent neural network; signal-to-noise ratio; spatial high-pass filter; glandular ultrasound image

Authors

VIMALA, B. B.; SRINIVASAN, S.; MATHIVANAN, S. K.; MUTHUKUMARAN, V.; BABU, J. C.; HERENCSÁR, N.; VILCEKOVA, L.

Released

19. 1. 2023

Publisher

MDPI

Location

Basel

ISBN

1424-8220

Periodical

SENSORS

Year of study

23

Number

3

State

Swiss Confederation

Pages from

1

Pages to

16

Pages count

16

URL

https://www.mdpi.com/1424-8220/23/3/1167

Full text in the Digital Library

http://hdl.handle.net/11012/209274

BibTex

@article{BUT181516,
  author="Baiju Babu {Vimala} and Saravanan {Srinivasan} and Sandeep Kumar {Mathivanan} and Venkatesan {Muthukumaran} and Jyothi Chinna {Babu} and Norbert {Herencsár} and Lucia {Vilcekova}",
  title="Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique",
  journal="SENSORS",
  year="2023",
  volume="23",
  number="3",
  pages="1--16",
  doi="10.3390/s23031167",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/23/3/1167"
}