Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Image Registration for Digital Subtraction Angiography
International Journal of Computer Vision
Histogram-Based Image Registration for Digital Subtraction Angiography
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Optimally Rotation-Equivariant Directional Derivative Kernels
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Computer Vision and Image Understanding
An Efficient Method for Image Registration in DSA
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 02
An Iterative Refinement DSA Image Registration Algorithm Using Structural Image Quality Measure
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Multiresolution Search Strategy for Elastic Registration of X-Ray Angiography Images
ICBMI '11 Proceedings of the 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation
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Digital subtraction angiography (DSA) is a widely used technique for visualization of vessel anatomy in diagnosis and treatment. However, due to unavoidable patient motions, both externally and internally, the subtracted angiography images often suffer from motion artifacts that adversely affect the quality of the medical diagnosis. To cope with this problem and improve the quality of DSA images, registration algorithms are often employed before subtraction. In this paper, a novel elastic registration algorithm for registration of digital X-ray angiography images, particularly for the coronary location, is proposed. This algorithm includes a multiresolution search strategy in which a global transformation is calculated iteratively based on local search in coarse and fine sub-image blocks. The local searches are accomplished in a differential multiscale framework which allows us to capture both large and small scale transformations. The local registration transformation also explicitly accounts for local variations in the image intensities which incorporated into our model as a change of local contrast and brightness. These local transformations are then smoothly interpolated using thin-plate spline interpolation function to obtain the global model. Experimental results with several clinical datasets demonstrate the effectiveness of our algorithm in motion artifact reduction.