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A curve evolution approach to object-based tomographic reconstruction
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DOI：10.1109/TIP.2002.806253.png)
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摘要
En 中文
In this paper, we develop a new approach to tomographic reconstruction problems based on geometric curve-evolution techniques. We use a small set of texture coefficients to represent the object and background inhomogeneities and a contour to represent the boundary of multiple connected or unconnected objects. Instead of reconstructing pixel values on a fixed rectangular grid, we then find a reconstruction by jointly estimating these unknown contours and texture coefficients of the object and background. By designing a new tomographic flow, the resulting problem is recast into a curve-evolution problem and an efficient algorithm based on level set techniques is developed. The performance of the curve evolution method is demonstrated using examples with noisy limited-view Radon transformed data and noisy ground-penetrating radar data. The reconstruction results and computational cost are compared with those of conventional, pixel-based regularization methods. The results indicate that the curve evolution methods achieve improved shape reconstruction and have potential computation and memory advantages over conventional regularized inversion methods.
Keyword:
curve evolution
image reconstruction
level set
parametric model
tomography
underground radar imaging
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13.7
论文数:
1.0W
被引数:
8.4W
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