The Active Segmentation platform for ImageJ (ASP/IJ) was developed in the scope of GSOC 2016 - 2018. 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. 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.
Motivation: Particle tracking enables researchers to analyze dynamic structures e.g. neurons, cells or subcellular regions in an elaborate way. There are a number of software platforms (for example Tracemate or TrackPy) providing particle linking algorithms based on a combination of techniques like gaussian template matching, Kalman filter to tackle linear motion and nearest-neighbor based search.
The project idea: We will use correlation in feature space to improve the tracking of objects. The object will be defined on a raw 2D image, after which the feature space will be computed across different frames and potential object displacements will be calculated. The trajectory estimate will be denoised, for example by using a Kalman filter. The applications of the proposed method are twofold: 1) object tracking in consecutive frames. 2) object tracking in disjoint framesets by similarity. Application 1 will be focused on using time-lapse light microscopic imaging datasets where the position of cells changes with time. The student will extend the existing ASP/IJ.
● Fix existing issues and bugs
● Add-on to the user interface for trajectory display
● Provide a reference implementation
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 plugin.
Desired skills: Java, signal processing