Anisotropic Geodesic Filter for Speckle Noise Reduction and Edge Preservation in 2D and 3D Echocardiography

  • Author / Creator
    Nehan Khan
  • It is a challenge today for medical practitioners and manufacturers to improve ultrasound image quality as the technology has reached its physical limits. Ultrasound images are a great help for non-invasive diagnostics but suffer from a wide variety of artifacts such as shadowing, limited field of view, and speckle noise. The main focus of this thesis is on the application of a new non-linear image processing technique in cardiac ultrasound imaging and more specifically on various methods to reduce multiplicative speckle noise. Various filtering techniques for speckle noise reduction have been proposed in the past; however, their performances are still limited as a compromise between speckle noise reduction and image features preservation is difficult to reach.

    In this thesis, an anisotropic geodesic filtering algorithm is proposed to reduce the multiplicative noise in cardiac ultrasound images. The algorithm is based on a scale-space filtering technique comparable to Gaussian filtering but with the difference that the Gaussian weights are automatically modified using a non-linear geodesic distance calculation between the pixels which is capable of automatically preserving edges. In the thesis, the proposed anisotropic geodesic filter is compared to various existing filters such as Gaussian filter, median filter, and other types of non-linear filters based on gradient-based anisotropic diffusion. We demonstrate that the proposed anisotropic geodesic filter performs best in terms of preserving features and at the same time can provide improvements to the signal-to-noise-ratio of real-time 2D and 3D echo-cardiographs. The proposed algorithm is validated on real-world ultrasound images comparing signal-to-noise-ratio (SNR), root-mean-square-error (RMSE), peak-signal-to-noise-ratio (PSNR), contrast and contrast-to-noise-ratio (CNR).

  • Subjects / Keywords
  • Graduation date
    Fall 2018
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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