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 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 last GSOC 2023 project implemented a GUI integrating SQLite as a proof of concept. Computed image features and class memberships are stored in a general SQLite database. However, the present table structure is not optimal and can be improved.
The project idea: At present, the feature space and the classification results produced by the platform are stored in several separate files. The candidate should redesign the database and implement necessary changes at the UI level.
Tasks
● Fix existing issues and bugs
● SQL database design
● UI redesign
Minimal set of deliverables
● Requirement specification - Prepared by the candidate after understanding the functionality.
● System Design - Detailed plan for the development of the plugin and test cases.
● Implementation and testing - Details of implementation and testing of the platform.
Skill level: Intermediate
Required skills: Java, SQL
Time commitment: Full-time (350 h)
Lead mentor: Dimiter Prodanov (dimiterpp@gmail.com), INCF Belgian Node
Project website:
- ImageJ: https://imagej.nih.gov/
- Active Segmentation : GitHub - sumit3203/ACTIVESEGMENTATION: Active Segmentation Project
- SQLite: SQLite Home Page
Backup mentors: Sumit Vohra, ZIB, Berlin, Germany
Tech keywords: Image Segmentation