Catalyst Projects provide support for Historically Black Colleges and Universities to work towards establishing research capacity of faculty to strengthen science, technology, engineering and mathematics undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. This project at Vorhees College seeks to develop computed tomography (CT) image reconstruction and segmentation methods using optimal sampling lattices and provides an opportunity for undergraduate students to enhance their education through research experiences in computer modeling. The researcher has established a strong collaboration with faculty at the University of South Carolina.
Computed tomography is an important tool to create the internal image of a physical body. Image reconstruction is to compute the internal image from the scanned data and image segmentation may help to locate the internal objects or their boundaries in the image. The usual CT computations are done on a Cartesian lattice whose pixels are squares or cubes. However optimal sampling lattices, such as 2D hexagonal and 3D face centered cubic and body centered cubic lattices, provide more efficient sampling and better adjacency relation than the traditional Cartesian lattices. In this project, for the 2D case, images will be reconstructed from scanned data using the filtered back-projection method over a hexagonal lattice and in a regular hexagonal region. Then image segmentation methods such as graph-cuts on the reconstructed images are applied. Because a CT machine rotates to perform scans from different directions, a 2D object to be scanned may be assumed to be circular. Since the circular region can be embedded into a regular hexagon more tightly than into a square, fewer number of lattice points may be involved and the lattice points can be efficiently indexed for image segmentation. Hence the computational time for image segmentation may be reduced and the quality may be improved. Computer simulations will be done to evaluate the new algorithms in terms of image segmentation quality and computational efficiency.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
