3D Vertebrae Segmentation in CT Images with Random Noises

Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However, the patient receives higher radiation dose in this case. In t...

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Bibliographic Details
Main Authors: Aslan, M S, Ali, A, Farag, A A, Arnold, B, Dongqing Chen, Ping Xiang
Format: Conference Proceeding
Language:English
Subjects:
Bones
Computed tomography
CT images with random noise
Image segmentation
Noise
Robustness
Spine
Three dimensional displays
Vertebrae segmentation
Online Access:Request full text
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Summary:Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However, the patient receives higher radiation dose in this case. In this paper, we use our robust 3D framework to segment vertebral bodies (VBs) in clinical computed tomography (CT) images with different noise levels. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG). Initial segmentation based on the LCG models is then iteratively refined by using Markov Gibbs random field(MGRF) with analytically estimated potentials. Experiments on the data sets show that the proposed segmentation approach is more accurate and robust than other known alternatives.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.560