A robust image registration method based on total variation regularization under complex illumination changes

Aghajani, Khadijeh; Manzuri, Mohammad Taghi; Yousefpour, Rohollah

doi:10.1016/j.cmpb.2016.06.004

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…(spatially-homogeneous signal response, bias field and shading in MRI images) or caused by the imaging modality itself such as perfusion CT which creates some high contrasted regions in the image. In order to obtain accurate registration results and to cope with these problems, many models have been developed for intensity correction [1,21,29,50]. It is important to note that, without intensity correction, both mono-modality and multi-modality models may fail to register the images correctly because bias introduces incorrect intensity values or false edges.…”

Section: Introductionmentioning

confidence: 99%

A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

Theljani¹,

Chen²

2020

Inverse Problems

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Registration aligns features of two related images so that information can be compared and/or fused in order to highlight differences and complement information. In real life images where bias field is present, this undesirable artefact causes inhomogeneity of image intensities and hence leads to failure or loss of accuracy of registration models based on minimization of the differences of the two image intensities. Here, we propose a non-linear variational model for joint image intensity correction (illumination and translation) and registration and reformulate it in a game framework. While a non-potential game offers flexible reformulation and can lead to better fitting errors, proving the solution existence for a non-convex model is nontrivial. Here we establish an existence result using the Schauder's fixed point theorem. To solve the model numerically, we use an alternating minimization algorithm in the discrete setting. Finally numerical results can show that the new model outperforms existing models.

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Section: Introductionmentioning

confidence: 99%

A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

Theljani¹,

Chen²

2020

Inverse Problems

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“…In the past decades, a large number of methods have been proposed for non-rigid registration, such as feature-based algorithms [5][6][7], intensity-based approaches [8][9][10], and physical models [11,12]. However, very few methods focus on registration of pelvis images with reasonable results.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid registration of medical images using a hierarchical framework with normalized mutual information

Li¹,

Zhang²,

Hu³

et al. 2018

biomedicalresearch

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Medical image registration plays a crucial role in medical field. Existing medical registration methods can be categorized as rigid registration and non-rigid registration methods. Compared with rigid registration, the non-rigid registration is more suitable for representing the anatomy's complicated deformation. In this study, we develop a non-rigid registration method in which Normalized Mutual Information (NMI) is used as similarity measurement and B-splines are used for transformation. The algorithm is implemented using a hierarchical framework such that the accuracy of registration is improved by the coarse-to-fine registration schema. Experiments demonstrate that the proposed method produces more accurate results than the affine and demons methods in registration of pelvis CBCT images, which provides an alternative approach to support non-rigid registration of medical images with intensity variation. The proposed algorithm shows potential for facilitating image-guided radiation therapy with higher treatment dose delivered to a precise location through the use of image registration.

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“…The total weighted total variation was used to minimize the reconstruction error. As a result of this, the smoothing effect on the coefficients across the edges had been reduced using weighing function [23]. R.W.K.So et al, presented a novel learning based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya distances.…”

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confidence: 99%

Comparative Analysis of Image Registration for Printed Circuit Boards

Kaur¹,

Kaur²,

Kaur³

2017

IJET

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Abstract-Image registration is a process of overlaying of two or more images taken at different times, different viewpoints and from different sensors. This paper aims to present comparative analysis of different image registration techniques for automated inspection of Printed Circuit Boards. In the automated inspection of Printed circuit Boards using image subtraction, it is essential to match the size and orientation of two images (reference and test image). The major goal of this paper is to provide a comparative analysis of image registration techniques used for automated inspection of Printed circuit boards. Correlation coefficient is using as a similarity measure for compare the result on quantitative basis. The results show the qualitative as well as quantitative analysis of the proposed work.

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A robust image registration method based on total variation regularization under complex illumination changes

Cited by 10 publications

References 22 publications

A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

Non-rigid registration of medical images using a hierarchical framework with normalized mutual information

Comparative Analysis of Image Registration for Printed Circuit Boards

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