Elastic image registration using hierarchical spatially based mean shift

Authors:
Xuan Yang;Jihong Pei;Wei Sun
Affiliations:
-;-;-
Venue:
Computers in Biology and Medicine
Year:
2013

Citing 10
Cited 1

Principal Warps: Thin-Plate Splines and the Decomposition of Deformations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration

Computer Vision and Image Understanding - Special issue on nonrigid image registration
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Multimodal Medical Image Elastic Registration Using Mean Shift

ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Computing Accurate Correspondences across Groups of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rigid and Articulated Point Registration with Expectation Conditional Maximization

IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel approach for image alignment using a markov–gibbs appearance model

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Salient Feature Region: A New Method for Retinal Image Registration

IEEE Transactions on Information Technology in Biomedicine
HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation

IEEE Transactions on Image Processing

A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel estimation technique for corresponding points using a hierarchical, spatially based mean shift algorithm is proposed. We proposed a spatially based probability estimation using different spatial masks. For a given point on reference image, its corresponding register point is found along the search trajectory generated by optimizing Bhattacharyya coefficient between two windows centered at the points on the register and reference images. The outliers are further eliminated by analyzing statistical information on the displacements of the candidate register points. Experiments on various monomodal medical images show that the proposed method is feasible and fast.