A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
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Segmenting cortical and sub-cortical structures from 3D brain images is of significant practical importance. However, various anatomical structures have similar intensity patterns in MRI, and the automatic segmentation of them is a challenging task. In this paper, we present a new brain segmentation algorithm using a hybrid model. (1) A multi-class classifier, PBT.M2, is proposed for learning/computing multi-class discriminative models. The PBT.M2 handles multi-class patterns more easily than the original probabilistic boosting tree (PBT) [11], and it facilitates the process, eventually, toward whole brain segmentation. (2) We use an edge field, by learning, to constraint the region boundaries. We show the improvements due to the two new aspects both numerically and visually, and also compare the results with those by FreeSurfer [2]. Our algorithm is general and easy to use, and the results obtained are encouraging.