Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images

被引:22
作者
Buyssens, P. [1 ]
Gardin, I. [1 ,2 ]
Ruan, S. [1 ]
机构
[1] Univ Rouen, QuantIF, LITIS EA 4108, F-76183 Rouen, France
[2] Ctr Henri Becquerel, Dept Nucl Med, F-76038 Rouen, France
关键词
LIVER-TRANSPLANTATION;
D O I
10.1016/j.irbm.2013.12.007
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
We describe a semi-supervised organ segmentation method for Computed Tomography images. In a first step, a dense oversegmentation of the image is created with an Eikonal-based algorithm. The proposed superpixel algorithm ourperforms state-of-the-art algorithms on classical metrics. In a second step, the semi-supervised segmentation is performed on the underlying Region Adjacency Graph created from the oversegmentation. As the complexity is greatly reduced, the organ segmentation can be performed in real-time. (C) 2013 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:20 / 26
页数:7
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