3D Vertebral Body Segmentation Using Shape Based Graph Cuts

Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs). In this paper, we propose a novel 3D shape based method to segment VBs in clinical computed tomography (CT) images without any user intervention. The proposed method depends...

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Bibliographic Details
Main Authors: Aslan, M S, Ali, A, Farag, A A, Rara, H, Arnold, B, Ping Xiang
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Bones
Computed tomography
Image segmentation
MGRF model
Probabilistic logic
Shape
shape based grapg cuts
Three dimensional displays
Vertebrae segmentation
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Summary:Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs). In this paper, we propose a novel 3D shape based method to segment VBs in clinical computed tomography (CT) images without any user intervention. The proposed method depends on both image appearance and shape information. 3D shape information is obtained from a set of training data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. To segment a VB, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.961