Reliability Estimation for Statistical Shape Models

One of the drawbacks of statistical shape models is their occasional failure to converge. Although visually this fact is usually easy to recognize, there is no automatic way to detect it. In this paper, we introduce a generic reliability measure for statistical shape models. It is based on a probabi...

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Bibliographic Details
Published in:IEEE transactions on image processing 2008-12, Vol.17 (12), p.2442-2455
Main Authors: Sukno, F.M., Frangi, A.F.
Format: Article
Language:English
Subjects:
Active appearance model
Active shape model
Algorithms
Artificial Intelligence
Biometry - methods
Computer Simulation
Convergence
Data mining
Face - anatomy & histology
Face recognition
Heart
Humans
Image analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Models, Biological
Models, Statistical
Pattern Recognition, Automated - methods
reliability
Reproducibility of Results
Sensitivity and Specificity
Shape measurement
Statistical distributions
statistical shape models
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Summary:One of the drawbacks of statistical shape models is their occasional failure to converge. Although visually this fact is usually easy to recognize, there is no automatic way to detect it. In this paper, we introduce a generic reliability measure for statistical shape models. It is based on a probabilistic framework and uses information extracted by the model itself during the matching process. The proposed method was validated with two variants of active shape models in the context facial image analysis. Experimental results on more than 3700 facial images showed a high degree of correlation between the segmentation accuracy and the estimated reliability metric.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2008.2006604