Analysis of HEp-2 cells in microscope images plays an important role in diagnosing certain diseases. An automated method for segmentation of cells can be part of a system for early identification and diagnosis, leading to improved outcomes for patients.
We are interested in implementation and testing of a specific algorithm, a level set method for segmentation of HEp-2 Cell Fluorescence Microscope Images.
Under supervision and feedback from supervisors, the student will implement an automated segmentation system in code.
The outcome of this project can potentially help pathologists and doctors in accuracy of disease diagnosis.
Skills and experience
The project can be tailored to your specific skill set, but generally you should have the following skills:
- MATLAB and C/C++ coding languages
- knowledge of, or willingness to learn about, machine learning and deep learning techniques.
Contact the supervisor for more information.