A non-rigid image registration method based on multi-level B-spline and L2-regularization

Ji, Huizhong; Li, Yusen; Dong, Enqing; Xue, Peng; Xiong, Wenshuo; Sun, Wei; Tang, Zhenchao; Zhang, Dejing; Fang, Wei

doi:10.1007/s11760-018-1274-0

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…based on Glioma location. A non-rigid transformation model using B-splines was selected for the alignment and transformation stage based on the detected landmarks and their correspondence 41 – 43 . The transformation parameters were estimated using a registration optimization algorithm to minimize the differences between corresponding landmarks and achieve accurate alignment 44 .…”

Section: Methodsmentioning

confidence: 99%

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Khorasani,

Dadashi serej,

jalilian

et al. 2023

Sci Rep

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Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for glioma grading purposes by fusing advanced Magnetic Resonance Imaging (MRI) images. Ninety-six subjects underwent an Apparent diffusion coefficient (ADC) map and Susceptibility-weighted imaging (SWI) MRI scan. After preprocessing, the different medical image fusion methods used to fuse ADC maps and SWI were Principal Component Analysis (PCA), Structure-Aware, Discrete Cosine Harmonic Wavelet Transform (DCHWT), Deep-Convolutional Neural network (DNN), Dual-Discriminator conditional generative adversarial network (DDcGAN), and Laplacian Re-Decomposition (LRD). The Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Relative Signal Contrast (RSC) were calculated for qualitative and quantitative analysis. We found high fused image quality with LRD and DDcGAN methods. Further quantitative analysis showed that RSCs in fused images in Low-Grade glioma (LGG) were significantly higher than RSCs in High-Grade glioma (HGG) with PCA, DCHWT, LRD, and DDcGAN. The Receiver Operating Characteristic (ROC) curve test highlighted that LRD and DDcGAN have the highest performance for glioma grade classification. Our work suggests using the DDcGAN and LRD networks for glioma grade classification by fusing ADC maps and SWI images.

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Section: Methodsmentioning

confidence: 99%

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Khorasani,

Dadashi serej,

jalilian

et al. 2023

Sci Rep

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“…In recent years, a large number of non-rigid registration models for medical images has been reported, such as the free form deformation (FFD) models based on B-spline [9][10][11], finite elements model (FEM) [12][13][14][15], viscous fluid model [16,17], and Demons [4,5,[18][19][20][21][22] etc. Since FFD based on B-spline employs the cubic B-spline to model the elastic deformation and each B-spline curve is only related to four adjacent control points, it can provide a high degree of flexibility for estimating the local motions of human tissues and organs.…”

Section: Related Workmentioning

confidence: 99%

“…Since FFD based on B-spline employs the cubic B-spline to model the elastic deformation and each B-spline curve is only related to four adjacent control points, it can provide a high degree of flexibility for estimating the local motions of human tissues and organs. Although FFD based on B-spline does not require assumptions about the elastic properties of the tissues and organs compared to physics-based deformation models, it is unable to effectively describe the global and large-scale deformation of human tissues and organs [9][10][11]. Image registration based on FEM transforms complex deformation into discrete and simple deformation elements, and then utilises these elements as the basic unit combined with the overall topological structure to fit the whole complex elastic deformation.…”

Section: Related Workmentioning

confidence: 99%

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Lei

et al. 2021

IET Image Processing

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Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nyström approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Then, a moving image can be registered to the fixed image by the deformation field 13 . B‐splines have local supports, which means local deformation can be calculated from only a couple of surrounding control points 9,14 . This property is beneficial for local deformation modeling and fast implementation of the B‐spline–based methods 9 .…”

Section: Introductionmentioning

confidence: 99%

“…13 B-splines have local supports, which means local deformation can be calculated from only a couple of surrounding control points. 9,14 This property is beneficial for local deformation modeling and fast implementation of the B-spline-based methods. 9 Demons-based methods are another well-known group of nonrigid registration methods.…”

Section: Introductionmentioning

confidence: 99%

Complete region of interest reconstruction by fusing multiview deformable three‐dimensional transesophageal echocardiography images

et al. 2022

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Background While three‐dimensional transesophageal echocardiography (3D TEE) has been increasingly used for assessing cardiac anatomy and function, it still suffers from a limited field of view (FoV) of the ultrasound transducer. Therefore, it is difficult to examine a complete region of interest without moving the transducer. Existing methods extend the FoV of 3D TEE images by mosaicing multiview static images, which requires synchronization between 3D TEE images and electrocardiogram (ECG) signal to avoid deformations in the images and can only get the widened image at a specific phase. Purpose This work aims to develop a novel multiview nonrigid registration and fusion method to extend the FoV of 3D TEE images at different cardiac phases, avoiding the bias toward the specifically chosen phase. Methods A multiview nonrigid registration and fusion method is proposed to enlarge the FoV of 3D TEE images by fusing dynamic images captured from different viewpoints sequentially. The deformation field for registering images is defined by a collection of affine transformations organized in a graph structure and is estimated by a direct (intensity‐based) method. The accuracy of the proposed method is evaluated by comparing it with two B‐spline–based methods, two Demons‐based methods, and one learning‐based method VoxelMorph. Twenty‐nine sequences of in vivo 3D TEE images captured from four patients are used for the comparative experiments. Four performance metrics including checkerboard volumes, signed distance, mean absolute distance (MAD), and Dice similarity coefficient (DSC) are used jointly to evaluate the accuracy of the results. Additionally, paired t‐tests are performed to examine the significance of the results. Results The qualitative results show that the proposed method can align images more accurately and obtain the fused images with higher quality than the other five methods. Additionally, in the evaluation of the segmented left atrium (LA) walls for the pairwise registration and sequential fusion experiments, the proposed method achieves the MAD of (0.07 ± 0.03) mm for pairwise registration and (0.19 ± 0.02) mm for sequential fusion. Paired t‐tests indicate that the results obtained from the proposed method are more accurate than those obtained by the state‐of‐the‐art VoxelMorph and the diffeomorphic Demons methods at the significance level of 0.05. In the evaluation of left ventricle (LV) segmentations for the sequential fusion experiments, the proposed method achieves a DSC of (0.88 ± 0.08), which is also significantly better than diffeomorphic Demons at the 0.05 level. The FoVs of the final fused 3D TEE images obtained by our method are enlarged around two times compared with the original images. Conclusions Without selecting the static (ECG‐gated) images from the same cardiac phase, this work addressed the problem of limited FoV of 3D TEE images in the deformable scenario, obtaining the fused images with high accuracy and good quality. The proposed method could provide an alternative to the c...

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A non-rigid image registration method based on multi-level B-spline and L2-regularization

Cited by 11 publications

References 18 publications

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Complete region of interest reconstruction by fusing multiview deformable three‐dimensional transesophageal echocardiography images

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