Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information

Abstract

Recently, the Coherent Point Drift (CPD) algorithm has become a very popular and efficient method for point set registration. However, this method does not take into consideration the neighborhood structure information of points to find the correspondence and requires a manual assignment of the outlier ratio. Therefore, CPD is not robust for large degrees of degradation. In this paper, an improved method is proposed to overcome the two limitations of CPD. A structure descriptor, such as shape context, is used to perform the auxiliary calculation of the correspondence, and the proportion of each GMM component is adjusted by the similarity. The outlier ratio is formulated in the EM framework so that it can be automatically calculated and optimized iteratively. The experimental results on both synthetic data and real data demonstrate that the proposed method described here is more robust to deformation, noise, occlusion, and outliers than CPD and other state-of-the-art algorithms.

A Review of Point Feature Based Medical Image Registration

“…One issue is that the weight parameter w that estimates the level of noise and number of outliers in the GMMs is manually selected, and the other issue is that the CPD method does not take the neighborhood information of the points in the same point sets into account. To overcome these two shortcomings of the original CPD algorithm, Peng et al [71] proposed a robust CPD algorithm, which uses the shape context [51][52][53][54] to describe the neighborhood structure of the points and employs the EM framework to calculate and optimize w automatically. The robust CPD algorithm is a great improvement of the traditional CPD approach, where detailed information is incorporated into the registration procedure.…”

“…Although many local similarity descriptors have been proposed to eliminate the drawbacks of the CPD methods, there are still great difficulties in describing the complex anatomical structures as well as the local deformation of the medical images. Despite the fact that applications in medical image registration are restricted by the drawbacks mentioned above, the CPD method is still successfully applied in the registration of multi-phase coronary CT images [25] and adjacent CT slices [71].…”

“…Some statistical optimization models have been applied to optimize these point set matching algorithms such as the GMM and the CPD method. The minimum description length (MDL) [36], Barnes-Hut algorithm [41], expectation-maximization (EM) [25,35,71] and support vector machine (SVM) [67] are presented as methods based on the point set distribution.…”

“…These criterions effectively evaluate the registration between two point sets subsampled from the vessel centerlines or bifurcations in vasculature interventions [17,20,21,29,31,47,61], contour or surfaces of bone in orthopaedics [34,42], edge points and some other local feature points of multiple kinds of medical images [8,27,35,71,78], because these criterions can take advantage of the distance measures directly without introducing additional errors. However, the drawback of the RMS is also its dependency on the distance measure.…”

“…CPD algorithm is a robust and efficient method for point set registration and has been used widely in the medical image registration. Peng et al improved the CPD algorithm by using the shape context (SC-CPD) algorithm [51][52][53][54] to find the correspondence between the point sets and to calculate the outlier ratio automatically to avoid manual assignments [71]. Point sets are extracted from the edges of slices to incorporate more information for registration.…”