scholarly journals Nonrigid 3D Medical Image Registration and Fusion Based on Deformable Models

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Peng Liu ◽  
Benjamin Eberhardt ◽  
Christian Wybranski ◽  
Jens Ricke ◽  
Lutz Lüdemann
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Internal Displacement ◽  
Intensity Difference ◽  
Reference Image ◽  
Medical Image Registration ◽  
Volume Deformation ◽  
Clinical Images ◽  
Registration Method ◽  
Affine Registration

For coregistration of medical images, rigid methods often fail to provide enough freedom, while reliable elastic methods are available clinically for special applications only. The number of degrees of freedom of elastic models must be reduced for use in the clinical setting to archive a reliable result. We propose a novel geometry-based method of nonrigid 3D medical image registration and fusion. The proposed method uses a 3D surface-based deformable model as guidance. In our twofold approach, the deformable mesh from one of the images is first applied to the boundary of the object to be registered. Thereafter, the non-rigid volume deformation vector field needed for registration and fusion inside of the region of interest (ROI) described by the active surface is inferred from the displacement of the surface mesh points. The method was validated using clinical images of a quasirigid organ (kidney) and of an elastic organ (liver). The reduction in standard deviation of the image intensity difference between reference image and model was used as a measure of performance. Landmarks placed at vessel bifurcations in the liver were used as a gold standard for evaluating registration results for the elastic liver. Our registration method was compared with affine registration using mutual information applied to the quasi-rigid kidney. The new method achieved 15.11% better quality with a high confidence level of 99% for rigid registration. However, when applied to the quasi-elastic liver, the method has an averaged landmark dislocation of 4.32 mm. In contrast, affine registration of extracted livers yields a significantly () smaller dislocation of 3.26 mm. In conclusion, our validation shows that the novel approach is applicable in cases where internal deformation is not crucial, but it has limitations in cases where internal displacement must also be taken into account.

AUTOMATIC ELASTIC MEDICAL IMAGE REGISTRATION BASED ON IMAGE INTENSITY

2005 ◽  
Vol 05 (02) ◽  
pp. 351-369 ◽  
Author(s):  
XIUYING WANG ◽  
DAVID DAGAN FENG
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Simulated Data ◽  
Initial Guess ◽  
Reference Image ◽  
Medical Image Registration ◽  
Image Intensity ◽  
Affine Registration ◽  
Computational Performance ◽  
Displacement Vectors

An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.

scholarly journals A Medical Image Registration Method Based on Progressive Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qian Zheng ◽  
Qiang Wang ◽  
Xiaojuan Ba ◽  
Shan Liu ◽  
Jiaofen Nan ◽  
...  
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Reference Image ◽  
Medical Image Registration ◽  
Registration Method ◽  
Multimodal Registration ◽  
Coarse Registration ◽  
Registration Result

Background. Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy. Methods. As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method. Results. For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods. Conclusions. The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

A Novel Medical Image Registration Based on Contour Features and MultiScale Information

2014 ◽  
Vol 513-517 ◽  
pp. 3020-3023
Author(s):  
Yun Feng Yang ◽  
Cheng Xin Lin ◽  
Peng Xiao Wang ◽  
Jia Li ◽  
Bo Li
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Clinical Medicine ◽  
Computational Time ◽  
Medical Image Registration ◽  
Canny Operator ◽  
Registration Method ◽  
Contour Lines ◽  
Principal Axes ◽  
Edge Features

Medical image registration is the important technique in the clinical medicine field. A novel hierarchical registration method of the medical images based on multiscale information and contour line is proposed in the paper. At First, contour lines of the couple images are extracted based on the edge features obtained by Canny operator, and contour lines of the couple images are resample in order to reduce the calculation cost in the registration process. Secondly, the Principal Axes method is used to accomplish the rough registration based on the resampled contour lines. Thirdly, multiscale image serials obtained by down-sample transform are used to accomplish the couple images fine registration. Experiment results show that the method not only can achieve more accurate registration results, but also can reduce the computational time greatly. The accurate registration results also can be achieved in the noisy environment.

scholarly journals A Methodology of Three-Dimensional Medical Image Registration Based on Conformal Geometric Invariant

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Liang Hua ◽  
Kean Yu ◽  
Lijun Ding ◽  
Juping Gu ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Subjective Evaluation ◽  
Three Dimensional ◽  
Medical Image Registration ◽  
Geometric Invariant ◽  
Registration Accuracy ◽  
Registration Method ◽  
Cloud Data ◽  
Dual Vector

A three-dimensional multimodality medical image registration method using geometric invariant based on conformal geometric algebra (CGA) theory is put forward for responding to challenges resulting from many free degrees and computational burdens with 3D medical image registration problems. The mathematical model and calculation method of dual-vector projection invariant are established using the distribution characteristics of point cloud data and the point-to-plane distance-based measurement in CGA space. The translation operator and geometric rotation operator during registration operation are built in Clifford algebra (CA) space. The conformal geometrical algebra is used to realize the registration of 3D CT/MR-PD medical image data based on the dual vector geometric invariant. The registration experiment results indicate that the methodology proposed in this paper is of stronger commonality, less computation burden, shorter time consumption, and intuitive geometric meaning. Both subjective evaluation and objective indicators show that the methodology proposed here is of high registration accuracy and suitable for 3D medical image registration.

scholarly journals A Novel Technique for Prealignment in Multimodality Medical Image Registration

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Wu Zhou ◽  
Lijuan Zhang ◽  
Yaoqin Xie ◽  
Changhong Liang
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Orientation Angle ◽  
Search Space ◽  
Statistical Correlation ◽  
Medical Image Registration ◽  
Registration Method ◽  
Novel Technique ◽  
Orientation Histogram ◽  
Multimodality Image

Image pair is often aligned initially based on a rigid or affine transformation before a deformable registration method is applied in medical image registration. Inappropriate initial registration may compromise the registration speed or impede the convergence of the optimization algorithm. In this work, a novel technique was proposed for prealignment in both monomodality and multimodality image registration based on statistical correlation of gradient information. A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction. Experimental results showed that it was effective to acquire the orientation angle between two unregistered images with advantages over the existed method based on edge-map in multimodalities. Applying the orientation detection into the registration of CT/MR, T1/T2 MRI, and monomadality images with respect to rigid and nonrigid deformation improved the chances of finding the global optimization of the registration and reduced the search space of optimization.

Sign in / Sign up

Export Citation Format

Share Document