ImageJ is a public domain Java image processing program extensively used in life sciences. The program was designed with an open architecture that provides extensibility via Java plugins. User-written plugins make it possible to solve almost any image processing or analysis problem or integrate the program with 3rd party software. The program was designed with an open architecture that provides extensibility via Java plugins. User-written plugins make it possible to solve almost any image processing or analysis problem or integrate the program with 3rd party software.
The Active Segmentation plugin is a complete redesign of Trainable Weka Segmentation (TWS) for ImageJ. The platform was developed in the context of GSOC 2016 by Sumit Vohra. The Active Segmentation was developed with the main goal of providing a general purpose environment that allows biologists and other domain experts to use transparently state-of-the-art techniques in machine learning to improve their image segmentation results.
The Active Segmentation provides generic functionality and user friendly interface so that the user can include the state of the art filters and machine learning frameworks from the WEKA library:
- active learning,
- multi-instance learning designed by third party in a robust manner.
The platform is still under development, although the main functionality has been completed in the context of GSOC 2016. In last Google summer of code, the major focus was on integrating generic filter families and specifically on one family of filters i.e. Gaussian Scale Space.
We would like to expand the existing functionality of the Active Segmentation plugin to incorporate learning from entire images presented as instances. In this way image classification functionality can be achieved.
The project will start by examining the existing Active Segmentation plugin with the purpose to add extra functionality of incorporating statistical features (in addition to the filters already present) as an extra module.
The immediate objectives of the development are to
- Develop a proof of concept module, which computes Zernike image moments based on a Region of Interest.
- Propose the necessary design changes in Active Segmentation to incorporate entire image features.
- Update the existing Graphical user Interface to handle both filters and statistical features.
- Update the meta-data export functionality to handle the new set of features.
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 plugin.
Required skills: Experience with Java
Desired skills: experience with ImageJ, machine learning preferably WEKA
Mentors: Dimiter Prodanov, INCF Belgian Node; (backup) Sumit Vohra, KU Leuven
Contact info: email@example.com
Project proposal template can be downloaded from here: project_template_2016
Application follows the rules of GSOC 2017.
Candidates must include a CV, completed proposal and assignment in their application.
Google summer of code page can be consulted here .
The assignment is available here