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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
Published on: June 16, 2014
Semiautomated analytical image correlation.
T Gregory Schaaff1, J M McMahon, Peter J Todd
1Chemical Sciences Division, Oak Ridge National Laboratory, Tennessee 37831-6365, USA.
Analytical Chemistry
|September 19, 2002
Summary
Machine vision, using pattern recognition and image processing, enables semiautomated correlation of optical microscopy and secondary ion mass spectrometry images. This allows combining complementary data from different imaging techniques for enhanced analysis.
Area of Science:
- Materials Science
- Analytical Chemistry
- Computer Vision
Background:
- Machine vision employs pattern recognition and digital image processing algorithms.
- Integrating data from diverse microscopy techniques presents analytical challenges.
Purpose of the Study:
- To develop a semiautomated method for correlating optical microscopy and secondary ion mass spectrometry (SIMS) images.
- To enable the combination of complementary information from disparate imaging modalities.
Main Methods:
- Application of image processing algorithms to correlate digital images.
- Utilizing relative positions of major constituents to correlate minor constituents invisible in optical images.
- Translating precise coordinates between analytical images from different methods.
Main Results:
- Semiautomated correlation between optical and secondary ion images is achievable.
- Minor constituents in SIMS images can be located relative to major constituents.
- Precise feature localization across different imaging techniques is demonstrated.
Conclusions:
- The developed machine vision approach facilitates the integration of complementary data from optical microscopy and SIMS.
- This semiautomated system enhances the capability to combine disparate imaging methods for comprehensive analysis.
