The chief objective of this project is four-fold; (1) To develop a software module for fully automated boxing of images of single particles; (2) To use geometry-driven diffusion methodology to preprocess the particle images to denoise and enhance them, and to develop algorithms to automate the parameter selection of these schemes; (3) To use our two-stage approach to extract particle boundaries. This stage relies on contour motion estimation via Fast Matching Method and the level set algorithms; (4) To develop a toolbox of filters (criteria such as area, axial ratio, integrated intensity, perimeter-to-area ratio) that can be employed in a project-specific way to reject false hits. The main thrust of the project is a method we use for noise suppression and the subsequent contouring. We view image grids as surfaces in higher dimension and exploit their geometric properties such as curvature and edge-discontinuities to invent initial-valued partial differential equations. These governing equations are expressed as necessary conditions to minimize a specific feature-preserving energy functional and are solved using very fast Additive Operator Split (AOS) methods. We then proceed to use the noise-reduced image to detect the boundaries of the particle projections, the so-called """"""""'boxing step"""""""". Here we are exploring the accuracy of the traditional cross-correlation methods compared to that of a new scheme based on fast contour propagation that results in automatic particle boundary.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
5P01GM064692-04
Application #
7551181
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2006-06-01
Budget End
2007-05-31
Support Year
4
Fiscal Year
2006
Total Cost
$108,821
Indirect Cost
Name
Lawrence Berkeley National Laboratory
Department
Type
DUNS #
078576738
City
Berkeley
State
CA
Country
United States
Zip Code
94720
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