The figure presents selected views of a compact microscope imaging system (CMIS) that includes a miniature video microscope, a Cartesian robot (a computer-controlled three-dimensional translation stage), and machine-vision and control subsystems. The CMIS was built from commercial off-the-shelf instrumentation, computer hardware and software, and custom machine-vision software. The machine-vision and control subsystems include adaptive neural networks that afford a measure of artificial intelligence.
The CMIS can perform several automated tasks with accuracy and repeatability — tasks that, heretofore, have required the full attention of human technicians using relatively bulky conventional microscopes. In addition, the automation and control capabilities of the system inherently include a capability for remote control. Unlike human technicians, the CMIS is not at risk of becoming fatigued or distracted: theoretically, it can perform continuously at the level of the best human technicians. In its capabilities for remote control and for relieving human technicians of tedious routine tasks, the CMIS is expected to be especially useful in biomedical research, materials science, inspection of parts on industrial production lines, and space science.
The CMIS can automatically focus on and scan a microscope sample, find areas of interest, record the resulting images, and analyze images from multiple samples simultaneously. Automatic focusing is an iterative process: The translation stage is used to move the microscope along its optical axis in a succession of coarse, medium, and fine steps. A fast Fourier transform (FFT) of the image is computed at each step, and the FFT is analyzed for its spatial-frequency content. The microscope position that results in the greatest dispersal of FFT content toward high spatial frequencies (indicating that the image shows the greatest amount of detail) is deemed to be the focal position.
In addition to automatic focusing, the machine-vision system is capable of performing the following other functions:
- Adaptive Thresholding: This function enables the choice of the best contrast needed for other image processing.
- Auto-Imaging Scanning: The microscope can scan along any or all of three Cartesian coordinate axes within a sample in order to find an object of interest.
- Identification and Classification of Objects: The system can find, classify, and label objects [e.g., living cells of one or more type(s) of interest] within a predetermined area of interest.
- Motion Detection: Movements of objects in a predetermined area of interest can be observed and quantified.
- Transition Mapping: In a sample containing small particles (e.g., colloids or living cells), small transitions between groups of particles can be detected. Examples of transitions include those between order and disorder, large and small objects, light and dark regions, and movement and non-movement. For example, in the case of a colloidal suspension containing a liquid and an adjacent solid phase, this function can be helpful in locating the zone of transition between the two phases.
This work was done by Mark McDowell of Glenn Research Center. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Electronics/Computers category.
Inquiries concerning rights for the commercial use of this invention should be addressed to
NASA Glenn Research Center
Commercial Technology Office
Attn: Steve Fedor
Mail Stop 4-8
21000 Brookpark Road
Cleveland
Ohio 44135.
Refer to LEW-17484.
