Computer Science ETDs


Guany Wang

Publication Date



In this paper, I will propose a simple and robust method for image and volume data segmentation based on manifold distance metrics. In this approach, pixels in an image are not considered as points with color values arranged in a grid. In this way, a new data set is built by a transform function from one traditional 2D image or 3D volume to a manifold in higher dimension feature space. Multiple possible feature spaces like position, gradient and probabilistic measures are studied and experimented. Graph algorithm and probabilistic classification are involved. Both time and space complexity of this algorithm is O(N). With appropriate choice of feature vector, this method could produce similar qualitative and quantitative results to other algorithms like Level Sets and Random Walks. Analysis of sensitivity to parameters is presented. Comparison between segmentation results and ground-truth images is also provided to validate of the robustness of this method.




image segmentation, manifold data, extraction of surface, uncertainty

Document Type


Degree Name

Computer Science

Level of Degree


Department Name

Department of Computer Science

First Advisor

Kniss, Joe

First Committee Member (Chair)

Kniss, Joe

Second Committee Member

Sen, Pradeep

Third Committee Member

Williams, Lance

Project Sponsors

National Science Foundation