Phase‐ and GVF‐Based Level Set Segmentation of Ultrasonic Breast Tumors

Liang Gao, Xiaoyun Liu and Wufan Chen

Journal of Applied Mathematics, 2012, vol. 2012, issue 1

Abstract: Automatically extracting breast tumor boundaries in ultrasound images is a difficult task due to the speckle noise, the low image contrast, the variance in shapes, and the local changes of image intensity. In this paper, an improved edge‐based active contour model in a variational level set formulation is proposed for semi‐automatically capturing ultrasonic breast tumor boundaries. First, we apply the phase asymmetry approach to enhance the edges, and then we define a new edge stopping function, which can increase the robustness to the intensity inhomogeneities. To extend the capture range of the method and provide good convergence to boundary concavities, we use the phase information to obtain an improved edge map, which can be used to calculate the gradient vector flow (GVF). Combining the edge stopping term and the improved GVF in the level set framework, the proposed method can robustly cope with noise, and it can extract the low contrast and/or concave boundaries well. Experiments on breast ultrasound images show that the proposed method outperforms the state‐of‐art methods.

Date: 2012
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https://doi.org/10.1155/2012/810805

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2012:y:2012:i:1:n:810805

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