NIST PREP Opportunity in Exporting Large Stitched Microscopy Images in Java
The National Institute of Standards and Technology (NIST) Information Technology Laboratory (ITL) has prepared the following project description for recruitment within the NIST Professional Research Experience Program (PREP). Priority will be given to the best "match" between students and the project, rather than by project alone.
For questions regarding the NIST ITL recruitment using PREP please contact:
Mark Przybocki (email@example.com); (301) 337-0767
For questions regarding the individual project descriptions, please contact the identified mentor.
For this PREP opportunity we are seeking students who are U.S. citizens.
Students interested in being considered for a NIST PREP placement in the Information Technology Laboratory should send the following items to Mark Przybocki (firstname.lastname@example.org):
- Resume/CV that highlights your coursework, experience and interests
- Identification of the project(s) for which you wish to be considered
- A letter of recommendation from a current Morgan State University Faculty member
Project Title: Exporting Large Stitched Microscopy Images in Java
Position: Graduate Student (MS or PhD)
Mentor: Walid Keyrouz (email@example.com)
The Software and Systems Division experiments with operating and understanding the performance implications of large microscopy image analysis (10K to 100K pixels per side). This project aims to expand the capabilities of the Microscopy Image Stitching Tool (MIST). Currently the Java-based software has serious limitations in file formats and file sizes when exporting a stitched image. The project will evolve around the following:
- Investigating available java libraries to export large microscopy images in a variety of file formats
- Experimenting with these libraries to prototype file saving functionality along with its supported file formats
- Benchmarking the memory usage and runtimes of these libraries
- Updating the MIST export feature using the new approach
- Expanding the export functionality to utilize a variety of blending modes (overlay, average, and linear)
Through this experimentation and benchmarking of these file writing libraries, the student will be exposed to design concerns that affect performance in terms of memory utilization and execution times. Additionally, they will understand the downstream impacts that loading their saved images will have on image processing workflows, such as the effects of stripe-oriented versus tile-oriented image storage. These will help align the student’s expertise with that of the group.
Desired Candidate Qualifications:
- Computer Science student with a solid foundation of Java programming and basic data structures (equivalent to ACM’s CS1 & CS2)
- Experience using statistical analysis software (e.g., SPSS, Stata, R, Excel)
- Knowledge of image file formats
- Programming in Python and C++ is beneficial, but not necessary