Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix

Luo, Nan; Wang, Quan

doi:10.1049/iet-cvi.2017.0130

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…Distance transformation based method [25], [26] is a popular method to extract the center line. This method determines the centerline by locating the group of points farthest from the coronary artery boundary.…”

Section: ) Methods Based On Distance Transformationmentioning

confidence: 99%

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

et al. 2019

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Aiming at the problem that the traditional coronary artery centerline extraction method is computationally intensive and requires a large number of manual interventions, a fully automatic coronary artery centerline tracking extraction method is proposed. First, adaptive adaptation of coronary vessels was performed based on different Fresenius norms designed for coronary scale. A multi-scale differential operator is constructed based on the vascular gray-scale distribution to establish a discriminant function, thereby obtaining the initial position and tracking direction of the seed point. Then, the new ridge point and tracking direction are optimally detected within the local arc length of the initial direction to ensure that the correct ridge position can still be obtained with large error interference. Finally, the pseudo vascular centerline is removed using the vascular topology feature detection method. The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease. INDEX TERMS Extraction method, coronary artery blood, vessel Centerline, CT coronary angiography.

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Section: ) Methods Based On Distance Transformationmentioning

confidence: 99%

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

et al. 2019

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“…Then repeat the above steps till convergence, and select the model that has the largest number of inliers. Detailed implementation of the algorithm can be referred to literature (Luo and Wang 2018).…”

Section: Feature Point Matchingmentioning

confidence: 99%

A landmark-supervised registration framework for multi-phase CT images with cross-distillation

Rao,

Lyu,

Feng

et al. 2024

Phys. Med. Biol.

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Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient's anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process. In this paper, we establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points. We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of Target Registration Error (TRE). Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma (HCC) and the intrahepatic cholangiocarcinoma (ICC) dataset, respectively. The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

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“…Zeng [2] presents a weighted structural local sparse appearance model to further improve the robustness of tracking. Luo [3] proposes a fast and effective pruning method to eliminate the outlier correspondences, especially the sharp ones. However, artificial neural networks require a great deal of specialized knowledge and repetitive experiments.…”

Section: Introductionmentioning

confidence: 99%

Control the number of skip‐connects to improve robustness of the NAS algorithm

Zhang

Zhou

2021

IET Computer Vision

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Recently, the gradient-based neural architecture search has made remarkable progress with the characteristics of high efficiency and fast convergence. However, two common problems in the gradient-based NAS algorithms are found. First, with the increase in the raining time, the NAS algorithm tends to skip-connect operation, leading to performance degradation and instability results. Second, another problem is no reasonable allocation of computing resources on valuable candidate network models. The above two points lead to the difficulty in searching the optimal sub-network and poor stability. To address them, the trick of pre-training the super-net is applied, so that each operation has an equal opportunity to develop its strength, which provides a fair competition condition for the convergence of the architecture parameters. In addition, a skip-controller is proposed to ensure each sampled sub-network with an appropriate number of skip-connects. The experiments were performed on three mainstream datasets CIFAR-10, CIFAR-100 and ImageNet, in which the improved method achieves comparable results with higher accuracy and stronger robustness.

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Effective outlier matches pruning algorithm for rigid pairwise point cloud registration using distance disparity matrix

Cited by 9 publications

References 44 publications

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography

A landmark-supervised registration framework for multi-phase CT images with cross-distillation

Control the number of skip‐connects to improve robustness of the NAS algorithm

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