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Segmentation of Vessel Geometries from Medical Images Using GPF Deformable Model

2012

Conference Paper

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We present a method for the reconstruction of vascular geometries from medical images. Image denoising is performed using vessel enhancing diffusion, which can smooth out image noise and enhance vessel structures. The Canny edge detection technique which produces object edges with single pixel width is used for accurate detection of the lumen boundaries. The image gradients are then used to compute the geometric potential field which gives a global representation of the geometric configuration. The deformable model uses a regional constraint to suppress calcified regions for accurate segmentation of the vessel geometries. The proposed framework show high accuracy when applied to the segmentation of the carotid arteries from CT images.

Author(s): Si Yong Yeo and Xianghua Xie and Igor Sazonov and Perumal Nithiarasu
Book Title: International Conference on Pattern Recognition Applications and Methods
Year: 2012

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

BibTex

@inproceedings{Yeo:ICPTRGA:2012,
  title = {Segmentation of Vessel Geometries from Medical Images Using GPF Deformable Model},
  author = {Yeo, Si Yong and Xie, Xianghua and Sazonov, Igor and Nithiarasu, Perumal},
  booktitle = {International Conference on Pattern Recognition Applications and Methods},
  year = {2012}
}