Recent improvements in tensor scale computation and its applications to medical imaging

Ziyue Xu; Milan Sonka M.D.; Punam K. Saha

doi:10.1117/12.811360

27 March 2009 Recent improvements in tensor scale computation and its applications to medical imaging

Ziyue Xu, Milan Sonka M.D., Punam K. Saha

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725939 (2009) https://doi.org/10.1117/12.811360
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Tensor scale (t-scale) is a local morphometric parameter describing local structure shape, orientation and scale. At any image location, t-scale is the parametric representation of the largest ellipse (an ellipsoid in 3D) centered at that location and contained in the same homogeneous region. Recently, we have improved the t-scale computation algorithm by: (1) optimizing digital representations for LoG and DoG kernels for edge detection and (2) ellipse fitting by using minimization of both algebraic and geometric distance errors. Also, t-scale has been applied to computing the deformation vector field with applications to medical image registration. Currently, the method is implemented in twodimension (2D) and the deformation vector field is directly computed from t-scale-derived normal vectors at matching locations in two images to be registered. Also, the method has been used to develop a simple algorithm for computing 2D warping from one shape onto another. Normal vector yields local structure orientation pointing to the closest edge. However, this information is less reliable along the medial axis of a shape as it may be associated with either of the two opposite edges of the local shape. This problem is overcome using a shape-linearity measure estimating relative changes in scale along the orthogonal direction. Preliminary results demonstrate the method's potential in estimating deformation between two images.

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Ziyue Xu, Milan Sonka M.D., and Punam K. Saha "Recent improvements in tensor scale computation and its applications to medical imaging", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725939 (27 March 2009); https://doi.org/10.1117/12.811360

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