A robust active shape model using an expectation-maximization framework

Active shape models (ASM) have been extensively used in object segmentation problems because they constrain the solution, using shape statistics. However, accurately fitting an ASM to an image prone to outliers is difficult and poor results are often obtained. To overcome this difficulty we propose a robust algorithm based on the Expectation-Maximization framework that assigns different weights (confidence degrees) to the observations extracted from the image. This reduces the influence of outliers since they often receive low weights. We tested the proposed algorithm with synthetic and real images (e.g., lip images and cardiac ultrasound images) achieving promising results. The proposed algorithm performs significantly better than the standard ASM implementation.