30 October 2009 An improved algorithm to compute tensor scale and its application to medical image interpolation
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Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74971E (2009) https://doi.org/10.1117/12.851176
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
Tensor scale (t-scale) is a parametric representation of local structure morphology that simultaneously describes its orientation, shape, and isotropic scale. At any image point, t-scale represents the largest ellipse (an ellipsoid in threedimension) centered at that point and contained in the same homogeneous region defined under a given boundary criterion. Here, we present an improved algorithm for t-scale computation and study its application to medical image interpolation. Specifically, the t-scale computation algorithm is improved by: (1) enhancing the accuracy of locating local structure boundary and (2) combining both algebraic and geometric distance errors in ellipse optimization. In the context of interpolation of grey level images, a new deterministic approach is presented that directly determines the interpolation line at each image point using local t-scale information on adjacent slices. At each point on an image slice, the method determines the normal vector derived by its t-scale that yields trans-orientation of the local structure and points to the closest edge on the local structure interface. Local normal vectors at the matching two-dimensional points on two adjacent slices are used to estimate the interpolation line using simple vector algebra. The method has been applied to BrainWeb data sets and also, to several other medical images from different clinical applications and preliminary results are presented.
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Ziyue Xu, Ziyue Xu, Milan Sonka, Milan Sonka, Punam K. Saha, Punam K. Saha, } "An improved algorithm to compute tensor scale and its application to medical image interpolation", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971E (30 October 2009); doi: 10.1117/12.851176; https://doi.org/10.1117/12.851176
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