Contour-Based Image Registration Using Mutual Information

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image-guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi-branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity-based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi-view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971 ± 0.585 mm and 1.266 ± 0.416 mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures.

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“…The (x, y) is coordinate in 2D image. Then, a searching function of the joint probability 31 is established by…”

Section: G Coarse-to-fine Estimationmentioning

confidence: 99%

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study

Jin

Zhang

et al. 2023

J Applied Clin Med Phys

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“…However, a closer look at (1) reveals that finding the a that minimizes the expression is equivalent to finding a that maximizes: Finding a maximizing (2) can be solved efficiently by computations in the Fourier domain using the Fast Fourier Transform where the wrap-around effect is NOT avoided; i.e., the computation can be derived using:…”

Section: Registration Of Contoursmentioning

confidence: 99%

“…Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images [1,9,16]. Optimization based registration of images is achieved by optimizing a similarity criterion, such as a correlation coefficient, a correlation function, a sum of absolute differences or correlation information entropy [6].…”

Section: Introductionmentioning

confidence: 99%

Measuring and evaluating ground truth for boundary detection in medical images

Cheng

Flores‐Mir

Major

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In this paper we describe a methodology for measuring the ground truth for boundary detection methods in medical images. An interface is used to allow medical experts to outline boundaries of interest on images. Following this, the accuracy of the experts in repeating the ground truth detection process is evaluated. Finally, an approach is described for comparing the ground truth as provided by experts with actual boundaries detected automatically. Experimental results are presented to validate the proposed algorithms.

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“…c) Contour-based FD. In [14], A. Alvarez et al proposed a contour-based image registration using mutual information. In [21], Li et al introduced an elastic contour matching scheme based on the active contour model to perform multi-sensor image registration.…”

Section: Related Workmentioning

confidence: 99%

“…Various feature descriptors (chain-code [11], length code method [12] etc.) and similarity measures (Bessel method [13], mutual information [14] etc.) can be applied to determinate the correspondence points.…”

Section: Introductionmentioning

confidence: 99%

Contour-Based Image Registration using Bipartite Graph Matching with Munkres Algorithm

Zhu¹,

Wen²,

Ye³

et al. 2014

Appl. Math. Inf. Sci.

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Image registration is a vital research topic in image processing. In this paper, we present a novel method to register color image and depth information captured by Kinect for Xbox 360. A contour-based approach is employed to find the correspondence points and estimate the transformation parameters model between color image and depth image. In our paper, the boundary contours are detected by Canny edge detector and located to sub-pixel level by a B-spline model. The shape descriptors of contours are computed by shape context. The correspondence points on both similar contours will have similar shape descriptors, which enables us to solve a bipartite graph matching with Munkres algorithm. Based on the correspondence points, the transform model that best aligns the two images can be estimated via RANSAC method. Experiments on a series of natural images are used for compare with some existing approaches and demonstrate the effectiveness of our method.

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Contour-Based Image Registration Using Mutual Information

Cited by 4 publications

References 14 publications

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study

Measuring and evaluating ground truth for boundary detection in medical images

Contour-Based Image Registration using Bipartite Graph Matching with Munkres Algorithm

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