Robust and fast-converging level set method for side-scan sonar image segmentation

Yan Liu; Qingwu Li; Guanying Huo

doi:10.1117/1.JEI.26.6.063021

30 November 2017 Robust and fast-converging level set method for side-scan sonar image segmentation

Yan Liu, Qingwu Li, Guanying Huo

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063021 (November 2017). https://doi.org/10.1117/1.JEI.26.6.063021

Abstract

A robust and fast-converging level set method is proposed for side-scan sonar (SSS) image segmentation. First, the noise in each sonar image is removed using the adaptive nonlinear complex diffusion filter. Second, k-means clustering is used to obtain the initial presegmentation image from the denoised image, and then the distance maps of the initial contours are reinitialized to guarantee the accuracy of the numerical calculation used in the level set evolution. Finally, the satisfactory segmentation is achieved using a robust variational level set model, where the evolution control parameters are generated by the presegmentation. The proposed method is successfully applied to both synthetic image with speckle noise and real SSS images. Experimental results show that the proposed method needs much less iteration and therefore is much faster than the fuzzy local information c-means clustering method, the level set method using a gamma observation model, and the enhanced region-scalable fitting method. Moreover, the proposed method can usually obtain more accurate segmentation results compared with other methods.

Citation Download Citation

Yan Liu, Qingwu Li, and Guanying Huo "Robust and fast-converging level set method for side-scan sonar image segmentation," Journal of Electronic Imaging 26(6), 063021 (30 November 2017). https://doi.org/10.1117/1.JEI.26.6.063021

Received: 23 July 2017; Accepted: 8 November 2017; Published: 30 November 2017

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