An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration

Wong, Alexander

doi:10.1109/titb.2009.2035693

Cited by 5 publications

(8 citation statements)

References 32 publications

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“…In the equation (1) the signifies a locus in picture cosmos and C is the intent performance that appraises the distinction amongst the pictures. The target is to uncover a viable result as of the key cosmos of probable arithmetical renovations that lessens the intent performance established on this devising.…”

Section: The Existing MC Methodologymentioning

confidence: 99%

“…Techniques that make use of extensive exploration in excess of all feasible conversions are capable to keep away from the concern of resident optima along the unification flat surface, then by the price of elevated-computational intricacy i.e., just courteous for plain conversions and pixellevel precision. IR with an MC proposal was handy in [1]. The system works a sampling scheme to depict progressively more credible key aspirants from a multidimensional solution space.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The multimodal icon registering [1] is the emerging technique for the automated diagnosis system. The geometric arrangement or registering different modality imageries is a vital errand in abundant uses in threedimensional (3-D) medicinal icon processing.…”

Section: Introductionmentioning

confidence: 99%

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Wlamc Scheme To Monomodal Brain Icon Registering

Sasikala¹,

Sharma²

2019

IJITEE

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An advanced method to get the better of the challenges of medical icon registering is anticipated, using Wang Landau Adaptive Monte Carlo (WLAMC) approach. The WangLandau (WL) technique is a distinct Adaptive Monte Carlo (AMC) procedure that has engendered a lot of curiosity in the medical icon registering (IR) technique owing to some remarkable simulation implementations. The multiresolution centered process for registering multimodal imageries by using AMC structure is the efficient existing approach for icon registering anywhere arbitrary finding aspirants are caused as of an all dimensional outcome spread out of probable geometric conversions on each of the repetition by using a testing approach. The remedy applicants generated are appraised instituted upon the Pearson category-Seven inaccuracy amongst the stage instants of the pictures in deciding the key contender by the deepest faulty left over. Even the AMC approach is efficient, it has some drawbacks and it can be eliminated with this proposed WLAMC approach. The experiments are performed on the real time medical imageries and the comparison of results illustrates that the approach put forward performs considerably well for monomodal brain images than the previous icon registering techniques.

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Section: The Existing MC Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Wlamc Scheme To Monomodal Brain Icon Registering

Sasikala¹,

Sharma²

2019

IJITEE

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“…Moreover, intensity and geometric information, such as buildings and water areas, may differ widely because of the different imaging conditions and speckle noise [16]. Consequently, the global transformation estimation that depends heavily on the selection of appropriate feature points often results in low registration accuracy [17,18]. …”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the registration performance may be poor when the SAR images to be registered are strongly corrupted by the speckle. Moreover, these approaches can be computationally expensive when solving registration of large SAR images [17]. For practical applications, it is required to have a real-time procedure for SAR image registration.…”

Section: Introductionmentioning

confidence: 99%

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

Xiang

Wang

You

2018

Sensors

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The Chinese GF-3 satellite launched in August 2016 is a Synthetic Aperture Radar (SAR) satellite that has the largest number of imaging modes in the world. It achieves a free switch in the spotlight, stripmap, scanSAR, wave, global observation and other imaging modes. In order to further utilize GF-3 SAR images, an automatic and fast image registration procedure needs to be done. In this paper, we propose a novel image registration technique for GF-3 images of different imaging modes. The proposed algorithm consists of two stages: coarse registration and fine registration. In the first stage, we combine an adaptive sampling method with the SAR-SIFT algorithm to efficiently eliminate obvious translation, rotation and scale differences between the reference and sensed images. In the second stage, uniformly-distributed control points are extracted, then the fast normalized cross-correlation of an improved phase congruency model is utilized as a new similarity metric to match the reference image and the coarse-registered image in a local search region. Moreover, a selection strategy is used to remove outliers. Experimental results on several GF-3 SAR images of different imaging modes show that the proposed algorithm gives a robust, efficient and precise registration performance, compared with other state-of-the-art algorithms for SAR image registration.

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Multimodal image registration based on the expectation–maximisation methodology

et al. 2017

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In this study, a new framework for multimodal image registration is proposed based on the expectation–maximisation (EM) methodology. This framework allows to address simultaneously parametric and elastic registrations independently on the modality of the target and source images without making any assumptions about their intensity relationship. The EM formulation for the image registration problem leads to a regularised quadratic optimisation scheme to compute the displacement vector field (DVF) that aligns the images and depends on their joint intensity distribution. At the first stage, a parametric transformation is assumed for the DVF, where the resulting quadratic optimisation is computed recursively to calculate its optimal parameters. Next, a general unknown deformation models the elastic part of the DVF, which is represented by an additive structure. The resulting optimisation process by the EM formulation results in a cost function that involves data and regularisation terms, which is also solved recursively. A comprehensive evaluation of the parametric and elastic proposals is carried out by comparing to state‐of‐the‐art algorithms and images from different application fields, where an advantage is visualised by the authors’ proposal in terms of a compromise between accuracy and robustness.

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An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration

Cited by 5 publications

References 32 publications

Wlamc Scheme To Monomodal Brain Icon Registering

Wlamc Scheme To Monomodal Brain Icon Registering

An Automatic and Novel SAR Image Registration Algorithm: A Case Study of the Chinese GF-3 Satellite

Multimodal image registration based on the expectation–maximisation methodology

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