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A Learning Based Algorithm for Automatic Extraction of the Cortical Sulci

Authors :
Songfeng Zheng
Songfeng Zheng
Zhuowen Tu
Alan L. Yuille
Allan L. Reiss
Rebecca A. Dutton
Agatha D. Lee
Albert M. Galaburda
Paul M. Thompson
Ivo Dinov
Arthur W. Toga
Songfeng Zheng
Songfeng Zheng
Zhuowen Tu
Alan L. Yuille
Allan L. Reiss
Rebecca A. Dutton
Agatha D. Lee
Albert M. Galaburda
Paul M. Thompson
Ivo Dinov
Arthur W. Toga
Publication Year :
2011

Abstract

This paper presents a learning based method for automatic extraction of the major cortical sulci from MRI volumes or extracted surfaces. Instead of using a few pre-defined rules such as the mean curvature properties, to detect the major sulci, the algorithm learns a discriminative model by selecting and combining features from a large pool of candidates. We used the Probabilistic Boosting Tree algorithm to learn the model, which implicitly discovers and combines rules based on manually annotated sulci traced by neuroanatomists. The algorithm almost has no parameters to tune and is fast because of the adoption of integral volume and 3D Haar filters. For a given approximately registered MRI volume, the algorithm computes the probability of how likely it is that each voxel lies on a major sulcus curve. Dynamic programming is then applied to extract the curve based on the probability map and a shape prior. Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect makes the approach flexible and it also works on extracted cortical surfaces.

Details

Database :
OAIster
Notes :
application/pdf
Publication Type :
Electronic Resource
Accession number :
edsoai.on1287559256
Document Type :
Electronic Resource