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Diderot: a parallel DSL for image analysis and visualization by Charisee Chiw; Gordon Kindlmann; John Reppy; Lamont Samuels; Nick Seltzer is a scholarly article available to read on EtoBox.

What is Diderot: a parallel DSL for image analysis and visualization about?

Research scientists and medical professionals use imaging technology, such as computed tomography (CT) and magnetic resonance imaging (MRI) to measure a wide variety of biological and physical objects. The increasing sophistication of imaging technology creates demand for equally sophisticated computational techniques to analyze and visualize the image data. Analysis and visualization codes are often crafted for a specific experiment or set of images, thus imaging scientists need support for quickly developing codes that are reliable, robust, and efficient.In this paper, we present the design and implementation of Diderot, which is a parallel domain-specific language for biomedical image analysis and visualization. Diderot supports a high-level model of computation that is based on continuous tensor fields. These tensor fields are reconstructed from discrete image data using separable convolution kernels, but may also be defined by applying higher-order operations, such as differentiation (∇). Early experiments demonstrate that Diderot provides both a high-level concise notation for image analysis and visualization algorithms, as well as high sequential and parallel performance.

Author

Charisee Chiw; Gordon Kindlmann; John Reppy; Lamont Samuels; Nick Seltzer

Publisher

ACM

Published

2012

Language

EN