Abstract
Active shape models (ASMs) have been shown to be a powerful tool to aid the interpretation of images. ASM model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. However, in many real applications, specifically those found in the area of medical image analysis, this assumption may be inaccurate. Robust parameter estimation methods have been used elsewhere in machine vision and provide a promising method of improving ASM search performance. This paper formulates M-estimator and random sampling approaches to robust parameter estimation in the context of ASM search. These methods have been applied to several sets of medical images where ASM search robustness problems have previously been encountered. Robust parameter estimation is shown to increase tolerance to outliers, which can lead to improved search robustness and accuracy.
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