Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information

Tang, Qi; Li, Lei; Song, Jikun; Zhu, Chunhua; Tang, Lu

doi:10.1002/acm2.12612

Cited by 20 publications

(11 citation statements)

References 18 publications

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“…Gradient normalized mutual information function of B-ultrasound algorithm. [6][7] In this paper, a gradient normalized mutual information function ( )…”

Section: Methodsmentioning

confidence: 99%

“…Gradient normalized mutual information function of B-ultrasound algorithm. 6 - 7 In this paper, a gradient normalized mutual information function ( I GNMI ) is constructed as a similarity measure to increase registration accuracy and stability. Let the reference image and the image to be registered in this paper be A, B, and the normalized mutual information of A and B is…”

Section: Methodsmentioning

confidence: 99%

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Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing

Dong

Zhao

et al. 2021

Pak J Med Sci

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Objectives: This study was to explore the application value of B-ultrasound in guiding puncture biopsy of chronic kidney disease (CKD) and the clinical nursing effects under the guidance of B-ultrasound. Methods: Pathological examination of kidney biopsy was performed on 94 patients with CKD under the guidance of ultrasound from August 2020 to December 2020.; patients were observed for symptoms such as low back pain, backache, hematuria, and subcapsular hematoma. Color Doppler ultrasonography was performed on the punctured patients on day 1, 2, and 3 to observe whether there was subrental hematoma. The pathological results were analyzed and the success rate of percutaneous renal biopsy under ultrasound guidance was analyzed. Before the patient was discharged, investigate the satisfaction with the nursing work. Results: (1) After the puncture, 45 patients developed low back pain and low back pain symptoms, 12 cases developed subcapsular hematoma; 8 cases showed gross hematuria, 62 cases showed microscopic hematuria, and the rest had no obvious symptoms; (2) the nursing satisfaction rate of 94 cases was as high as 95.7%. Conclusion: US-PRB is a safe and effective auxiliary examination method, which can improve the success rate of puncture and reduce postoperative complications. Effective nursing can reduce the incidence of postoperative complications and improve patient satisfaction. doi: https://doi.org/10.12669/pjms.37.6-WIT.4831 How to cite this:Dong L, Li J, Zhao M, Ren J. Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing. Pak J Med Sci. 2021;37(6):1564-1568. doi: https://doi.org/10.12669/pjms.37.6-WIT.4831 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Gradient normalized mutual information function of B-ultrasound algorithm. [6][7] In this paper, a gradient normalized mutual information function ( )…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing

Dong

Zhao

et al. 2021

Pak J Med Sci

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“…NMI is a concept whose properties have been studied in several ways and has been used for medical image registration problems 41 . Compared with mutual information, it is easy to compare the correlation between X and Y with normalized mutual information.…”

Section: Nmiedamentioning

confidence: 99%

NMIEDA: Estimation of distribution algorithm based on normalized mutual information

Lin

Xie

2020

Concurrency and Computation

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A new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two variables and then generate a dependency forest model. Second, based on the concept of sporadic model building and a reward and punishment scheme in Selfish Gene, NMIEDA provides a new updating mechanism that accelerates the convergence speed. Finally, a new sampling mechanism is adopted in NMIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition-based learning scheme and the mutation operator. The simulation results on benchmark problems and real-world problems demonstrate that NMIEDA often outperforms several other bivariate algorithms. K E Y W O R D S estimation of distribution algorithm, new sampling mechanism, new updating mechanism, NMIEDA, normalized mutual information However, there are two major problems in these current bivariate EDAs. First, the size of the population seriously affects the performance of the bivariate EDAs. 24,39 On one hand, bivariate EDA under small population size is unable to represent the general information of the selected individuals

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“…To estimate the best parameters of rigid transformation for an optimal registration and to solve an ill-posed problem due to sparse information on intra- and inter-correspondence of scenes in multiple images, the mutual information (MI) criterium, which takes intensity information into account, is a valuable solution [ 18 , 19 , 20 ]. MI is a measure of common information in source and target images.…”

Section: Introductionmentioning

confidence: 99%

“…The results have indicated that the image registration becomes stable. Yang et al [ 20 ] proposed a new method that combines the normalized mutual information with spatial information for nonrigid medical image registration. The algorithm was validated on a simulated brain image with single-modality and multi-modality.…”

Section: Introductionmentioning

confidence: 99%

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images

Moldovanu

Toporaș

Biswas

et al. 2020

Entropy

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A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.

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Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information

Cited by 20 publications

References 18 publications

Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing

Application of B-ultrasound information image in Renal Puncture Biopsy treatment and Nursing

NMIEDA: Estimation of distribution algorithm based on normalized mutual information

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images

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