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Proceedings Paper

Segmentation-based L-filtering of speckle noise in ultrasonic images
Author(s): Eleftherios Kofidis; Sergios Theodoridis; Constantine L. Kotropoulos; Ioannis Pitas
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Paper 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.

Paper Details

Date Published: 1 May 1994
PDF: 10 pages
Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); doi: 10.1117/12.172565
Show Author Affiliations
Eleftherios Kofidis, Univ. of Patras (Greece)
Sergios Theodoridis, Univ. of Patras (Greece)
Constantine L. Kotropoulos, Univ. of Thessaloniki (Greece)
Ioannis Pitas, Univ. of Thessaloniki (Greece)


Published in SPIE Proceedings Vol. 2180:
Nonlinear Image Processing V
Edward R. Dougherty; Jaakko Astola; Harold G. Longbotham, Editor(s)

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