The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSoC 2016 - 2019. 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).
The project idea: At present, the feature space and the classification results produced by the platform are stored in several files. The idea is that the image features and classification results would be stored in a SQL database for cross-comparisons between sessions. 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. The candidate is encouraged to use an SQLite database engine and a proof-of-concept implementation storing ImageJ measurements 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 the development of the plugin and test cases.
- Implementation and testing - Details of implementation and testing of the platform.
Desired skills: Java, SQL
Mentors: Dimiter Prodanov @dprodanov (dimiterpp@gmail.com ), INCF Belgian Node; (backup) Sumit Vohra, ZIB, Berlin, Germany
References:
ImageJ: https://imagej.nih.gov/
Weka https://www.cs.waikato.ac.nz/ml/weka/
Active Segmentation : https://github.com/sumit3203/ACTIVESEGMENTATION
SQLite: https://www.sqlite.org/index.html