Speeding up the Analysis of Neuron Morphology using Parallel Processing
Wang DD*, Bourke D, Domanski L and Vallotton P
Quantitative Imaging, CSIRO Mathematics, Informatics and Statistics, Locked Bag 17, North Ryde, NSW 1670, Australia
- *Corresponding Author:
- Wang DD
CSIRO Mathematics, Informatics and Statistics
Locked Bag 17, North Ryde
NSW 1670, Australia
E-mail: [email protected]
Received date: June 26, 2014; Accepted date: August 21, 2014; Published date: October 10, 2014
Citation: Wang DD, Bourke D, Domanski L, Vallotton P (2014) Speeding up the Analysis of Neuron Morphology using Parallel Processing. J Mol Imag Dynamic 4:115. doi:10.4172/2155-9937.1000115
Copyright: © 2014 Wang DD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Researchers using High Content Screening systems generate thousands of fluorescently labeled cell images from which they measure subtle but important phenotypic changes summarized by dozens of parameters. Large image datasets and fast turnaround requirement have made the efficient High Content Analysis a challenging task. This paper studies multi-core based high performance image analysis and its application to data and compute-intensive High Content Analyses. A vertical parallelization strategy is employed and an automated parallelization framework is implemented to automatically dispatch image processing tasks. The strategy is based on allocation of different images to separate processors so that each image is analyzed sequentially on a single processor and multiple images are processed by separate processors in parallel. Experiments demonstrate that this approach, of a generic character, considerably increases throughput.