Cell Tracking Using Geometrical Features
The project aims to provide a robust mechanism for cell tracking using 2D raw image objects. Through the use of Mean Square Distance method the potential object displacements will be calculated. Another set of images will be fed as time lapse protocol. Trajectory estimate will be denoised using different modern filters such as IMM,Weiner and Multiple Channel Linear Correlation Filter.
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.
Cell Tracking has gained importance in recent times due to the growing extensive research in Biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by cellular experiments, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of cell objects in time-lapse microscopy images.
Purpose-The project offers an amalgamation of programming and life science. I had been constantly on the hunt for an opportunity to work in this area and I hope I will be able to deliver the best from my side. Moreover it would be a great learning experience for me and open up opportunities in this domain for further exploration.Its a win-win situation for me.I hope that my interest in this domain will help in realising an intuitive dimension to this project.
- Provide a robust mechanism for cell tracking using 2D raw image objects
- Use the Mean Square Distance method to calculate the potential object displacement
- Denoise the trajectory estimate using different modern filters such as IMM,Weiner and Multiple Channel Linear Correlation Filter