Please use this identifier to cite or link to this item:
|Title:||An MRF framework for joint registration and segmentation of natural and perfusion images|
|Keywords:||Joint registration and segmentation|
|Source:||Mahapatra, D., Sun, Y. (2010). An MRF framework for joint registration and segmentation of natural and perfusion images. Proceedings - International Conference on Image Processing, ICIP : 1709-1712. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2010.5651441|
|Abstract:||Registration and segmentation provide complementary information about each other. In this paper we propose a method for the joint registration and segmentation (JRS) of images using Markov random fields (MRFs). The use of MRFs allows us to formulate the problem as one of labeling and apply fast discrete optimization techniques like graph cuts. Graph cuts is able to overcome the limitations of previously used active contour frameworks namely, large number of iterations, risk of being trapped in local minima, and sensitivity to initialization. The labels in the MRF formulation indicate joint occurrence of displacement vectors and segmentation class and the energy formulation is able to capture their mutual dependency. Experiments on real patient perfusion data and natural images show that JRS gives better performance than conventional registration and segmentation methods. © 2010 IEEE.|
|Source Title:||Proceedings - International Conference on Image Processing, ICIP|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 14, 2017
WEB OF SCIENCETM
checked on Nov 18, 2017
checked on Dec 17, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.