Abstract
Virtual anatomy models show in detail characteristics of the human body systems. These models are based in surface representation of the structures and lack information from the interior of the object. Creating models that represent the surface, the interior of the object and are able to provide pathological information is the current challenge of research in life sciences. We present a method to synthesize realistic three-dimensional organic tissues starting from bidimensional textured multi-channel samples. The method relies on an energy function that measures the difference between the reference texture and the synthesized object, through a distance metric that compares perpendicular neighborhoods in the object to neighborhoods in the sample. When this function is minimized by IRLS, the result is a solid object that resembles the sample at every slice. In some cases, the optimization might be aided by adding the feature distance transform, calculated from a given binary mask. This allows to code large textured areas. Multiple textures can also be provided to the optimization in order to create anistropic textures. We apply our method starting from various micrometric images such as histology images or slices of Synchrotron Radiation Computed Micro-Tomography (SRμCT) images. A major advantage of our method is to extend 2D histological information to a 3D representation. We demonstrate the accuracy of the generated texture by comparing statistical and morphological parameters computed from the synthetic object with those obtained from the real object underlying the reference images.