Title :
Level set segmentation with both shape and intensity priors
Author :
Chen, Siqi ; Radke, Richard J.
Author_Institution :
Dept. of Electr., Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
fDate :
Sept. 29 2009-Oct. 2 2009
Abstract :
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes´ rule to the segmentation problem, the cost function decomposes into shape and image energy parts. The shape energy is based on recently proposed nonparametric shape distributions, and we propose a new image energy model that incorporates learned intensity information from both foreground and background objects. The proposed variational level set segmentation framework has two main advantages. First, by characterizing image information with regional intensity distributions, there is no need to balance image energy and shape energy using a heuristic weighting factor. Second, by incorporating learned intensity information into the image model using a nonparametric density estimation method and an appropriate distance measure, our segmentation framework can handle problems where the interior/exterior of the shape has a highly inhomogeneous intensity distribution. We demonstrate our segmentation algorithm using challenging pelvis CT scans.
Keywords :
Bayes methods; image segmentation; set theory; Bayes rule; appropriate distance measure; heuristic weighting factor; image energy parts; intensity distribution; intensity prior information; nonparametric density estimation method; nonparametric shape distributions; shape energy; training set; variational level-set-based segmentation formulation; Active contours; Cost function; Density measurement; Image segmentation; Level set; Pelvis; Power engineering and energy; Shape measurement; Solid modeling; Systems engineering and theory;
Conference_Titel :
Computer Vision, 2009 IEEE 12th International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4420-5
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2009.5459290