Abstract
Digital staircase effects and noisy protrusions and dents on object boundaries add major challenges for quantitative structural analysis and visual assessment. In this paper, we present a shape-based smoothing algorithm for binary digital objects to eliminate digital staircase artifacts and remove boundary noise. The method uses a signed distance transform image, where the zero level set defines the object boundary. The key idea of our algorithm is to smooth this zero level set by applying a smoothing filter on the signed distance transform image. The method has been applied on slice-by-slice segmentation results of human proximal femur bone volumes from hip MR imaging. The observed results are encouraging, which suggest that the new method is capable of successfully eliminating digital staircase effects, while preserving the basic geometry of the target object. Quantitative analysis of a phantom experiment results reveals that a notion of “optimum scale” of smoothing exists for the new algorithm, and it is related to the scale of noisy protrusions and dents. The quantitative experiments have shown that, at the optimum smoothing scale, the new method can achieve 98.5% to 99.6% Dice similarity coefficient for noisy protrusions and dents of different sizes.
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This work was supported by the NIH grants NIH R01 AR066008 and NIH R01 AR070131.
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Zhang, X., Chen, C., Chang, G., Saha, P.K. (2018). Shape-Based Smoothing of Binary Digital Objects Using Signed Distance Transform. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_50
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DOI: https://doi.org/10.1007/978-3-030-03801-4_50
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