![Computer Science ETDs](../../assets/md5images/a8c896d0fc7ed4cb27fb019ff52dbb1d.jpg)
Computer Science ETDs
Publication Date
12-1-2009
Abstract
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.
Language
English
Keywords
image segmentation, manifold data, extraction of surface, uncertainty
Document Type
Thesis
Degree Name
Computer Science
Level of Degree
Masters
Department Name
Department of Computer Science
First Committee Member (Chair)
Kniss, Joe
Second Committee Member
Sen, Pradeep
Third Committee Member
Williams, Lance
Project Sponsors
National Science Foundation
Recommended Citation
Wang, Guany. "Supervised manifold distance segmentation." (2009). https://digitalrepository.unm.edu/cs_etds/73