Abstract
Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established elastic registration methods, but none of them can so far preserve discontinuities in the displacement field. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this paper, we exploit the fact that motion segmentation could play a guiding role during discontinuity preserving registration. The motion segmentation is embedded in a continuous cut framework guaranteeing convexity for motion segmentation. Furthermore we show that a primal-dual method can be used to estimate a solution to this challenging variational problem. Experimental results are presented for MR images with apparent breathing induced sliding motion of the liver along the abdominal wall.
Highlights
Image registration became an indispensable tool for many medical applications including the Image-Guided Therapy systems
Vese and Chan [6] introduced a level set framework based approach to efficiently solve the Mumford and Shah minimization problem for segmentation. Another influential approach based on the Total Variation (TV) norm, known to preserve discontinuities, was proposed by Rudin et al [7], the ROF model
The beneficial behaviour of the TV norm was exploited in recent registration and optical flow methods, as, for example, by [8, 9]
Summary
Image registration became an indispensable tool for many medical applications including the Image-Guided Therapy systems. The concept of the directiondependent regularization of the displacement field has been introduced; see, for example, [3]. The drawback of these methods is the necessity of providing a good manual segmentation of the boundaries. Vese and Chan [6] introduced a level set framework based approach to efficiently solve the Mumford and Shah minimization problem for segmentation. Another influential approach based on the Total Variation (TV) norm, known to preserve discontinuities, was proposed by Rudin et al [7], the ROF model. The beneficial behaviour of the TV norm was exploited in recent registration and optical flow methods, as, for example, by [8, 9]
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