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Local maximum likelihood segmentation of echocardiographic images with Rayleigh distribution

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Abstract

In order to interpret ultrasound images, it is important to understand their formation and the properties that affect them, especially speckle noise. This image texture, or speckle, is a correlated and multiplicative noise that inherently occurs in all types of coherent imaging systems. Indeed, its statistics depend on the density and on the type of scatterers in the tissues. This paper presents a new method for echocardiographic images segmentation in a variational level set framework. A partial differential equation-based flow is designed locally in order to achieve a maximum likelihood segmentation of the region of interest. A Rayleigh probability distribution is considered to model the local B-mode ultrasound images intensities. In order to confront more the speckle noise and local changes of intensity, the proposed local region term is combined with a local phase-based geodesic active contours term. Comparison results on natural and simulated images show that the proposed model is robust to attenuations and captures well the low-contrast boundaries.

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Acknowledgements

The authors would like to thank Rabeh Djabri for English proofreading and Dr. Mathiron and Dr. Levy for their help in the clinical evaluation. Part of this work was funded by the Regional Council of Picardie and European Union/FEDER.

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Authors and Affiliations

  1. Medical Computing Laboratory (LIMED), University of Abderrahmane Mira, 06000, Bejaïa, Algeria

    Ahror Belaid

  2. Mirada Medical Ltd, New Road, Oxford, OX1 1BY, UK

    Djamal Boukerroui

  3. Sorbonne universités, Université de Technologie de Compiègne, CNRS 7253 UMR/Heudiasyc, CS 60319, 60203, Compiègne Cedex, France

    Djamal Boukerroui

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  1. Ahror Belaid
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Correspondence to Ahror Belaid.

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Belaid, A., Boukerroui, D. Local maximum likelihood segmentation of echocardiographic images with Rayleigh distribution. SIViP 12, 1087–1096 (2018). https://doi.org/10.1007/s11760-018-1251-7

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  • DOI: https://doi.org/10.1007/s11760-018-1251-7

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