Mark D. Benedict, PhDUES, Inc.
Generating Realistic Damage Models for Composite Materials using the Python Programming
Language
μCT Scan Based ModelingGoal: Micro/Meso scale high fidelity models of as-processed composite materialsGeneration of simulation input from μCT scans requires segmentation Optimal Segmentation in a low contrast environment is challengingContrast enhancement techniques tend to introduce many artifacts
Registered Volumetric ImageStacks of microCT Images Geometric and Materials Property Description for Simulation High Fidelity Models
Integration of Image Segments into a 3D Geometric Description
Independent Mesh Method (IMM) used to model same specimen.
Simulation geometry generated from a priori knowledge of specimen geometry.
Processing or “sanding” was performed on the assumed geometry to improve results by compensating for inability to properly describe the specimen geometry.
Lesson Learned: it would be desirable to generate the simulation geometry from actual sample including processing it has experienced
Image Segmentation Using Available Commercial Packages
Goal: Separate fiber tows from matrix from inclusions, voids, etc.
Overlap in intensity makes thresholding of limited utility
Limited contrast due to similar densities of composite components
Scan artifacts at high gradient regions (voids, inclusions)
Texture is a salient feature of these images
Threshold BasedOver Segmentation
Typical rawCT image
“Ringing”Artifact
Inclusion
Orthogonal scan of same dataset
Inclusion
Custom Image Segmentation
What to do when you dont know what to do?: Read many papers
Literature review, particularly from the CV community offered promise.
Broad range of algorithms - What is the best implementation for exploration?
E. Sharon et al. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001
Z. Lin et al. Visualisation 2000, Pan-Sydney Workshop on Visual Information Processing,December, 2000.
Conferences in Research and Practice in InformationTechnology, Vol. 2. P. Eades and J. Jin, Eds
Why Python?
Batteries Included (Numpy,ndimage,Mayavi,pysparse)Rapid Implementation of pseudocodeInteractive coding.
Method 1: Unseeded Region Growth
Versatile, minimal parameter, segmentation technique.
Morphological: Boundary growth
Limited intensity range can cause problems with assigning pixels to a given region. Anisotropic filters should be considered to convey texture information
Potential scan order/growth order dependance of result.
Extension to multi-spectral data and Volumetric data is straightforward but limited by scaling performance of algorithm.
Unallocated Border Pixels
Identified Regions
z is next pixel to add to a region
Difference Measure
Acceptance Criterion
Unseeded Region Growth Segmentation Results
Excellent discretization of intensity range as seen in histogram
Regions in the interior of matrix are still over segmented
Boundaries are partitioned as Regions of their own due to local mixing of statistics from the two adjacent regions!
Method II: Pulse Coupled Neural Network
Algorithm inspired by experimental observation of image segmentation process with a mammalian visual cortex
One to One correspondence between image pixels and neurons, neurons only connected to nearest neighbors
Activation of a neuron (pixel) makes it more likely that its neighbors will also activate...creating a Pulse of activation
Typically each time dependent group of activations, or pulse, correlates well with a segment in the image
Iterative, Dense Matrix Algebra=Efficient implementations
Popular method, many variants
Easily extended to Volumetric Data
Feeding Input
Linking Input
Activation Value
Activation Function
Threshold Value
M
W
X
Si,j
1
VF
VL
exp(-!F)
Fi,j
exp(-!L)
Feeding
Linkingβ
Ui,j
Threshold
Heaviside(Ui,j)
Surrounding Neurons
Yi,j
VTexp(-!T)
Ti,j
ModulationReceptive Fields Pulse Generator
One Iteration of PCNN algorithm
Pulse Coupled Neural Network Segmentation Results
PCNN provides a reduced histogram that can be Usefully thresholded.
Excellent pre-filter
Region Growth-PCNN results are very similar to the URG results. Faster algorithm however
Noticeable problems with boundaries thickness in RG-PCNN
No ability to separate tows
RG-PCNN Results
PCNN Results
Method III: Segmentation by Weighted Aggregation
Segmentation as Graph Partitioning and Coarsening
Use multi-scale measures of intensity, texture, shape, and boundary integrity
Algebraic Multigrid Solver
Linear Time complexity
Can be extended to Volumetric Data?
Standard AMG “pyramid”
Graph
Segment
State vector
Saliency Function
Termination Criteria
Segmentation by Weighted Aggregation Results
Excellent segmentation of matrix from tows and glue.
Boundary class still present. Further investigation warranted.
Morphologic opening operations might yield sufficiently smooth classification.
Results need to be applied to Volumetric data.
Conclusion:
Use Texture Information in Weighted Aggregation to segment tows from one anotherMove Segmentation routines into Volumetric analysisPython Takeaway:
“Batteries Included’ such as numpy, ndimage, pysparse greatly accelerate implementationCombo Develop/Deploy
References:URG:Visualisation 2000, Pan-Sydney Workshop on Visual Information Processing,December, 2000. Conferences in Research and Practice in InformationTechnology, Vol. 2. P. Eades and J. Jin, Eds
PCNN:Z Wang et. al. ”Review of pulse-coupled neural networks” Image and Vision Computing, Vol 28 pp.5-13 (2009)
Segmentation by Weighted Aggregation:E Sharon, A Brandt, and R Basri,”Segmentation and boundary detection using multiscale intensity measurements” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol 1(2001)
IMM:Endel V. Iarve, David H. Mollenhauer, Eric G. Zhou,Timothy Breitzman, and Thomas J. Whitney, ”Independent mesh method-based prediction of local and volume average fields in textile composites” Composites Part A, Vol 40, Issue 12, pp.1880-1890 (2009)
Peridynamics:S. Silling et, al.,”Peridynamic states and constitutive modeling”J Elasticity, Vol 88, No.2, pp.151-184 (2007):
The author would like to thank David Mollenhauer (AFRL/RXBC) and John Maguire (AFRL/RXM) for their support, guidance, and input.
All work performed at AFRL/RXBN, WPAFB, Dayton, Ohio under contract CR&D III TO 86, FA8650-07-D-5800