The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2021. The plugin provides a general-purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. ImageJ is a public domain Java image processing program extensively used in life and material sciences. The program was designed with an open architecture that provides extensibility via plugins computing different filters and region descriptors (i.e. image features).
SQLite is a C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. SQLite is the most used database engine in the world and is available on many platforms.
The project idea: At present, the feature space and the classification results produced by the platform are stored in several separate files. The idea is that the types and values of image features and classification outcomes would be stored in an SQLite database for cross-comparisons between sessions. The candidate is required to use the SQLite database engine in order to integrate it with the GUI of ASP/IJ.
Tasks
- Fix existing issues and bugs
- SQL database design
- GUI implementation and integration
Minimal set of deliverables
- Requirement specification - Prepared by the candidate after understanding the functionality.
- System Design - Detailed plan for development of the plugin and test cases.
- Implementation and testing - Details of implementation and testing of the platform.
Desired skills: Java, SQL
Effort: 350 h
Mentors: Dimiter Prodanov @dprodanov (dimiterpp@gmail.com ), INCF Belgian Node; (backup) Sumit Vohra, ZIB, Berlin, Germany
Tech keywords: Java, SQL
References
- ImageJ: https://imagej.nih.gov/
- Active Segmentation : https://github.com/sumit3203/ACTIVESEGMENTATION
- SQLite: SQLite Home Page