Abstract
The paper describes a machine learning approach for improving active shape model segmentation, which can achieve high detection rates. Rather than represent the image structure using intensity gradients, we extract local edge features for each landmark using steerable filters. A machine learning algorithm based on AdaBoost selects a small number of critical features from a large set and yields extremely efficient classifiers. These non-linear classifiers are used, instead of the linear Mahalanobis distance, to find optimal displacements by searching along the direction perpendicular to each landmark. These features give more accurate and reliable matching between model and new images than modeling image intensity alone. Experimental results demonstrated the ability of this improved method to accurately locate edge features.
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Li, S., Zhu, L., Jiang, T. (2004). Active Shape Model Segmentation Using Local Edge Structures and AdaBoost. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_15
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DOI: https://doi.org/10.1007/978-3-540-28626-4_15
Publisher Name: Springer, Berlin, Heidelberg
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