High-Performance Workflow Primitives for Image Registration and Segmentation
Gregory Sharp
James Shackleford
KANDASAMY NAGARAJAN
10.6084/m9.figshare.6171074.v1
https://figshare.com/articles/journal_contribution/High-Performance_Workflow_Primitives_for_Image_Registration_and_Segmentation/6171074
<div>The overall goal of this project is to
develop a high-performance, many-core CPU and GPU accelerated
algorithmic software package for attacking classes of problems that
depend on solutions to data-dense inverse problems such as registration,
segmentation, tomography, and parameter estimation. The specific
technical approach involves developing algorithmic primitives required
by a broad class of inference and analysis based workflows.
Probabilistic primitives for building generative, discriminative, and
conditional random field classification models will be implemented with
emphasis on object segmentation. Specialized registration operators will
be developed for spline and voxel-driven algorithms. These primitives
will be developed within the single instruction multiple data paradigm
which utilizes many-core processing architectures via OpenMP, CUDA, and
OpenCL. The workflow will be supplemented by a graphical user interface
(GUI), providing a feature rich studio of tools that expose
high-performance primitives to scientists visually and intuitively. The
platform architecture will be designed as a distributed system service
targeting locally administered scientific computing clusters where the
number of compute nodes will be able to scale with load requirements.
The GUI and the computational core may either run in a distributed
client-server configuration or together locally on a single high
performance workstation. Emphasis will be placed on documentation and
video/written tutorials necessary for adoption. The project team will
use an open software development model to build a strong user base
comprising both novice users as well as researchers with the need to
implement new algorithms on top of a stable software infrastructure. It
is expected that the availability of this tool and its source code will
catalyze an increase in quantitative image analysis spanning across
research disciplines.</div>
2018-04-23 14:19:21
NSF-SI2-2018
libkaze
plastimatch
registration
segmentation
software library
Computer Engineering
Software Engineering