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Escaping path approach with extended neighborhood for speckle noise reduction

  • Silesian University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper presents a new approach to multiplicative noise removal in ultrasound images. The proposed algorithm utilizes concept of digital paths created on the image grid presented in [14] adapted to the needs of multiplicative noise reduction. Digital Paths are used to determine the filter weights taking into account the structures present in the image. Method of creating path is crucial for the efficiency and speed of the filter. The new approach uses special type of digital paths based on so called Escaping Path Model created with extended neighborhood system. The experiments confirmed that the proposed algorithm achieves a comparable results with the existing state of the art denoising schemes in suppressing multiplicative noise in ultrasound images.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings
EditorsJaime S. Cardoso, Roberto Paredes, Xosé M. Pardo
PublisherSpringer Verlag
Pages184-191
Number of pages8
ISBN (Electronic)9783319193892
DOIs
Publication statusPublished - 2015
Event7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015 - Santiago de Compostela, Spain
Duration: 17 Jun 201519 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9117
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015
Country/TerritorySpain
CitySantiago de Compostela
Period17/06/1519/06/15

Keywords

  • Digital paths
  • Extended neighborhood
  • Image filtering
  • Multiplicative noise
  • Ultrasound imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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