This document summarizes research on developing a machine learning system to automatically detect and characterize nanoparticles in micrographs. The research used supervised learning algorithms like support vector machines (SVM) and feature extraction methods like SIFT to classify nanoparticles by size and spatial distribution. Initial results showed SIFT features and features based on neighboring pixels improved classifier accuracy over simple (x,y) coordinates. Future work could optimize clustering methods, classification algorithms, and develop software for industrial nanoparticle analysis.

1. Problem Statement
Materials & Methods
Future Recommendations
ConclusionsResults
References
Acknowledgements
Thank you to FSU, HPMI, and NSF for funding me, to Dr. Okenwa Okoli for administering this
program, for hiring me, and for his advice and hospitality, to Margaret Scheiner and Emily
Hammel for coordinating this program, to Ron Frazier, Dr. Michael DeVine, and all their
presenters for offering their business and operations advice, and to Frank Allen for his
professional and technical advice. Special thanks to Tanmoy Das and Mostafa Gilanifar for
mentoring me, to Grant Kleiner for assistance, and to Dr. Shrivastava for his continued advice
and expertise.
Figure 2: Application of
nanomaterials in “smart”
structures [2].
Figure 3: General flow of research experiments.
• Nanomaterials have potential in cancer
treatment, “smart” structures, improved
solar cells.
• Nanomaterial fabrication not standardized,
not viable on an industrial scale.
• Scale-up requires application of quality
engineering tools for optimizing process
yield, variance reduction, and process
monitoring and control.
• Also requires methods for estimating
dimensions and spatial arrangement, as
these significantly influence nanomaterial
thermal, physical, electromagnetic, and
optical properties.
• Objective: use supervised learning and
machine learning to develop a system for
automatically detecting nanoparticles,
segmenting micrographs, and estimating
size and spatial distribution.
• Machine learning – supervised learning [3], pattern recognition, object
detection.
• Feature extraction – Scale-Invariant Feature Transform (SIFT) [4], also
investigating features based on neighboring pixels.
• Classification – SVM [3], multi-class classifications enabled using k-means
clustering (unsupervised method) with cluster numbers as classes.
• Software – MATLAB, R, developed parallel processing scripts for SVM.
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Signals and Telecommunications, Cambridge, pp. 133-138, 2012.
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• Low values of SVM parameters slightly improve classifier accuracy, but
parameters have little effect.
• Features used have high correlation to performance.
• SIFT features may be overly robust, leaving out critical variations.
• Features based on neighboring pixels are promising.
• Multi-class classification is more effective than binary classification.
• Using multiple training images may lower human error from GT.
• Low SVM parameter values with features based on neighboring pixels
using clustering and a multi-class SVM should yield highly accurate
classifier.
• More experiments are needed to verify these conclusions.
• More experiments, more data, use multiple training images per model.
• Try different clustering (k-medoids, hierarchical) methods and boosted
SVM classification.
• For commercialization: Use C++ or Python for all-in-one software package,
design software for industrial servers (parallel processing), autorun scripts,
user-friendly graphical user interface (GUI).
Quality Issues in Nanomanufacturing
Thaddeus Berger1 (tberger2012@my.fit.edu), Mostafa Gilanifar2, Tanmoy Das2,
Grant Kleiner2 Dr. Abhishek Shrivastava2* (ashrivastava@fsu.edu)
1Florida Institute of Technology, 2Florida State University
Figure 1: Application of
nanomaterials in cancer
treatment [1].
• Data from feature extraction used in R for
SVM. Optimizing SVMs is computationally
expensive, so parallel processing script was
developed, increasing speed by an order
of magnitude. Radial classifiers were used
for most SVMs.
• SVMs were tested against test images
(non-GT) to determine classifier accuracy.
Figure 8: SVM classification plot for
test using image in Figure 7. Upside-
down appearance of plot is due to
flip of y-axis in image processing.
Figure 7: Test image with SIFT
feature keypoints plotted [6].
Figure 6: Example of a radial, soft-
margin SVM classifier.
Figure 9: confusion matrices were
used to calculate classifier
accuracy/error.
Figure 5: GT of image from Figure 4
with SIFT key points plotted [5].
Bottom right shows SIFT descriptors.
Figure 4: Original training image [5].
• 16 original images used. “Ground Truth”
GT (Figure 5) made for each image by
coloring nanorods, each with an individual
RGB (red, green, blue) color code.
• Features extracted using MATLAB. First
feature set used was the (x, y) coordinates,
yielding low classifier accuracy. SIFT
features (Figure 5) extracted next,
improving accuracy. Features based on
neighboring pixels further improved
accuracy.