Abstract
We propose a segmentation method for blurred and low-resolution CT images focusing physical properties. The basic idea of our research is simple: two objects can be easily separated in areas of structural weakness. Given CT images of an object, we assign a physical property such as Young’s modulus to each voxel and create functional images (e.g., von Mises strain at the voxel). We then remove the voxel with the largest value in the functional image, and these steps are reiterated until the input model is decomposed into multiple parts. This simple and unique approach provides various advantages over conventional segmentation methods, including preciousness and noise robustness. This paper also demonstrates the efficiency of our approach using the results of various types of CT images, including biological representations and those of engineering objects.
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Hishida, H., Michikawa, T., Ohtake, Y., Suzuki, H., Oota, S. (2010). CT Image Segmentation Using Structural Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_5
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DOI: https://doi.org/10.1007/978-3-642-17277-9_5
Publisher Name: Springer, Berlin, Heidelberg
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