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, Volume 77, Issue 12, pp 15657–15675 | Cite as

Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images

  • Syed Gibran Javed
  • Abdul MajidEmail author
  • Yeon Soo Lee
Article
  • 72 Downloads

Abstract

The speckle noise commonly occurs in ultrasound imaging based applications. Due to the multiplicative nature, speckle noise deteriorates the visual quality of ultrasound images. This affects the performance of radiologists and practitioners for disease diagnosis and/or patient treatment. The current study proposes a bio-inspired multi-gene genetic programming (MGGP) based intelligent estimator to reduce the speckle noise from ultrasound images. The proposed MGGP approach is based on the parallel framework of multiple genes and has effectively utilized the evolutionary learning capabilities to develop an intelligent estimator, by exploiting the useful statistical features extracted from local neighboring pixels. The performance of the proposed novel approach is evaluated on ultrasound images of common carotid artery corrupted with different noise levels. Further, the robust performance was validated on several diverse types of ultrasound images of Breast Cyst, Kidney Cancer, Liver, Liver Cyst, and Fetal Head. The proposed bio-inspired approach showed superior denoising performance over existing approaches. The proposed intelligent estimator is capable of removing speckle noise effectively while preserving the fine lines and edges. During evolution, the MGGP framework automatically selects the useful statistical features and primitive functions from a wider solution space to develop the intelligent estimator. Further, the proposed approach does not require image-dependent optimal threshold values, as conventional speckle denoising approaches required.

Keywords

Ultrasound images Speckle noise Multi-gene Genetic programming Parallel-framework Denoising 

Notes

Acknowledgements

This work is supported by Higher Education Commission, Government of Pakistan under Indigenous PhD Fellowship Program-Batch VII, PIN No. 117-3250-EG7-012. The authors are also grateful to Dr. Dominic Searson for providing help regarding GPTIPS Toolbox.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Syed Gibran Javed
    • 1
  • Abdul Majid
    • 1
    Email author
  • Yeon Soo Lee
    • 2
  1. 1.Biomedical Informatics Research Lab, Department of Computer and Information SciencesPakistan Institute of Engineering and Applied SciencesIslamabadPakistan
  2. 2.Department of Biomedical Engineering, College of Medical ScienceCatholic University of Daegu HayangroGyeongsanRepublic of Korea

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