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Scale is a widely used notion in medical image analysis that evolved in the form of scale-space theory where the key idea is to represent and analyze an image at various resolutions. Recently, a notion of local morphometric scale referred to as "tensor scale" was introduced using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. In the previous work, tensor scale was described using a 2-D algorithmic approach and a precise analytic definition was missing. Also, with previous framework, 3-D application is not practical due to computational…mehr

Produktbeschreibung
Scale is a widely used notion in medical image analysis that evolved in the form of scale-space theory where the key idea is to represent and analyze an image at various resolutions. Recently, a notion of local morphometric scale referred to as "tensor scale" was introduced using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. In the previous work, tensor scale was described using a 2-D algorithmic approach and a precise analytic definition was missing. Also, with previous framework, 3-D application is not practical due to computational complexity. This book, therefore, establishes an analytic definition of tensor scale in n-dimensional (n-D) images, provides an efficient computational solution for 2- and 3-D images and investigates its role in various medical imaging applications including image interpolation, filtering, and segmentation.
Autorenporträt
Ziyue Xu, Ph.D. from the University of Iowa in 2012, has been worked extensively in medical image processing and analysis with focus on image structural feature extraction and its applications. Xu is currently a fellow in National Institutes of Health.