Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples.

Many simulation studies in biomedicine are based on a similar sequence of processing steps, starting from images and running through geometric model generation, assignment of tissue properties, numerical simulation and visualization of the results--a process known as image-based geometric modelling and simulation. We present an overview of software systems for implementing such a sequence both within highly integrated problem-solving environments and in the form of loosely integrated pipelines. Loose integration in this case indicates that individual programs function largely independently but communicate through files of a common format and support simple scripting, so as to automate multiple executions wherever possible. We then describe three specific applications of such pipelines to translational biomedical research in electrophysiology.

A level-set approach to image blending.

This paper presents a novel method for blending images. Image blending refers to the process of creating a set of discrete samples of a continuous, one-parameter family of images that connects a pair of input images. Image blending has uses in a variety of computer graphics and image processing applications. In particular, it ran be used for image morphing, which is a method for creating video streams that depict transformations of objects in scenes based solely on pairs of images and sets of user-defined fiducial points. Image blending also has applications for video compression and image-based rendering. The proposed method for image blending relies on the progressive minimization of a difference metric which compares the level sets between two images. This strategy results in an image blend which is the solution of a pair of coupled, nonlinear, first-order partial differential equations that model multidimensional level-set propagations. When compared to interpolation this method produces more natural appearances of motion because it manipulates the shapes of image contours rather than simply interpolating intensity values. This strategy results in a process that has the qualitative property of deforming greyscale objects in images rather than producing a simple fade from one object to another. This paper presents the mathematics that underlie this new method, a numerical implementation, and results on real images that demonstrate its effectiveness.