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Suppression of Speckle Noise in Medical Images via Segmentation-Grouping of 3D Objects Using Sparse Contourlet Representation

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Abstract

A novel method for filtering ultrasonic and magnetic resonance images contaminated by multiplicative (speckle) noise is justified and implemented. The method consists of several stages: segmentation of image areas, grouping of similar structures in 3D, homomorphic transformation, a 3D filtering approach based on a sparse representation in the contourlet transform (CLT) space with posterior filtering according to MI weights of similar 2D structures, and the final inverse homomorphic transformation. A physical interpretation of the filtering procedure in the case of speckle noise is given, and a structural scheme for noise suppression is developed. Simulation based on the new filtering procedure has confirmed its superiority in terms of generally accepted criteria, such as the structural similarity index measure, peak signal-to-noise ratio, edge preservation index, and the resolution index alpha, as well as in a visual comparison of filtered images.

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Authors and Affiliations

  1. Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences, Moscow, Russia

    V. F. Kravchenko &  Yu. V. Guliaev

  2. Bauman Moscow State Technical University, Moscow, Russia

    V. F. Kravchenko

  3. Instituto Politecnico Nacional de Mexico, Mexico City, Mexico

    V. I. Ponomaryov & G. Aranda Bojorges

Authors
  1. V. F. Kravchenko
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  2. Yu. V. Guliaev
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  3. V. I. Ponomaryov
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  4. G. Aranda Bojorges
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Corresponding authors

Correspondence to V. F. Kravchenko, Yu. V. Guliaev, V. I. Ponomaryov or G. Aranda Bojorges.

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The authors declare that they have no conflicts of interest.

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Translated by I. Ruzanova

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Kravchenko, V.F., Guliaev, Y.V., Ponomaryov, V.I. et al. Suppression of Speckle Noise in Medical Images via Segmentation-Grouping of 3D Objects Using Sparse Contourlet Representation. Dokl. Math. 107, 77–82 (2023). https://doi.org/10.1134/S1064562423700461

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  • DOI: https://doi.org/10.1134/S1064562423700461

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