Features-driven Multi-modality Registration and Fusion of Functional and Structural Medical Images

The internship project with Pacific Nuclear Medicine will develop a software tool that brings two images obtained from different imaging modalities into spatial alignment (aka multi-modal registration). This is a necessary task for the creation of correct image fusions, which are essential for diagnosis and correction of nuclear medicine images. Additionally, the project will develop a feature-registration framework in which pattern-recognition techniques and feature-extraction methods to increase the accuracy and robustness of the registration algorithm will be employed. In particular, the project will extract shape-features available in the image data and perform processing (e.g. distance transformations) to generate more descriptive, representative data, which will perform registration on. Through the incorporation of features into the algorithm, the internship team will propose that the global and local registration of the images can be performed with higher accuracy and efficiency. Finally the developed algorithm shall be tested on the collected medical datasets using established validation methods and be encapsulated in a user-friendly graphical interface.

Lisa Tang
Faculty Supervisor: 
Dr. Ghassan Hamarneh
British Columbia