Supervised manifold distance segmentation

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.

Name thesis:degree_name

Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Department of Computer Science

Year

2009

Author dc:creator

• Wang, Guany

Contributors dc:contributor

• Kniss, Joe
• Sen, Pradeep
• Williams, Lance

• image segmentation
• manifold data
• extraction of surface
• uncertainty

Language dc:language

English

Identifier

https://digitalrepository.unm.edu/cs_etds/73

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:cs_etds-1072