Share Email Print

Proceedings Paper

A regions of confidence based approach to enhance segmentation with shape priors
Author(s): Vikram V. Appia; Balaji Ganapathy; Amer Abufadel; Anthony Yezzi; Tracy Faber
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

Paper Details

Date Published: 27 January 2010
PDF: 12 pages
Proc. SPIE 7533, Computational Imaging VIII, 753302 (27 January 2010); doi: 10.1117/12.850888
Show Author Affiliations
Vikram V. Appia, Georgia Institute of Technology (United States)
Balaji Ganapathy, Georgia Institute of Technology (United States)
Amer Abufadel, Khoury Group (United States)
Anthony Yezzi, Georgia Institute of Technology (United States)
Tracy Faber, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 7533:
Computational Imaging VIII
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?