Segmentation-based L-filtering of speckle noise in ultrasonic images

Eleftherios Kofidis; Sergios Theodoridis; Constantine L. Kotropoulos; Ioannis Pitas

doi:10.1117/12.172565

1 May 1994 Segmentation-based L-filtering of speckle noise in ultrasonic images

Eleftherios Kofidis, Sergios Theodoridis, Constantine L. Kotropoulos, Ioannis Pitas

Proceedings Volume 2180, Nonlinear Image Processing V; (1994) https://doi.org/10.1117/12.172565
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

Abstract

We introduce segmentation-based L-filters, that is, filtering processes combining segmentation and (nonadaptive) optimum L-filtering, and use them for the suppression of speckle noise in ultrasonic (US) images. With the aid of a suitable modification of the learning vector quantizer self-organizing neural network, the image is segmented in regions of approximately homogeneous first-order statistics. For each such region a minimum mean-squared error L- filter is designed on the basis of a multiplicative noise model by using the histogram of grey values as an estimate of the parent distribution of the noisy observations and a suitable estimate of the original signal in the corresponding region. Thus, we obtain a bank of L-filters that are corresponding to and are operating on different image regions. Simulation results on a simulated US B-mode image of a tissue mimicking phantom are presented which verify the superiority of the proposed method as compared to a number of conventional filtering strategies in terms of a suitably defined signal-to-noise ratio measure and detection theoretic performance measures.

Citation Download Citation

Eleftherios Kofidis, Sergios Theodoridis, Constantine L. Kotropoulos, and Ioannis Pitas "Segmentation-based L-filtering of speckle noise in ultrasonic images", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); https://doi.org/10.1117/12.172565

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