Lung image registration by featured surface matching method

2022 ◽  
pp. 1-10
Author(s):  
Shu-Te Su ◽  
Ming-Chih Ho ◽  
Jia-Yush Yen ◽  
Yung-Yaw Chen
Keyword(s):  
Image Registration ◽  
Surface Matching ◽  
Matching Method

scholarly journals Featured Surface Matching Method for Liver Image Registration

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 59723-59731
Author(s):  
Shu-Te Su ◽  
Ming-Chih Ho ◽  
Jia-Yush Yen ◽  
Yung-Yaw Chen
Keyword(s):  
Image Registration ◽  
Surface Matching ◽  
Matching Method ◽  
Liver Image

Fracture Surface Matching Method for Terracotta Based on Feature Points

2018 ◽  
Vol 55 (4) ◽  
pp. 041005
Author(s):  
赵夫群 Zhao Fuqun ◽  
耿国华 Geng Guohua
Keyword(s):  
Fracture Surface ◽  
Surface Matching ◽  
Feature Points ◽  
Matching Method

Robust point matching method for multimodal retinal image registration

2015 ◽  
Vol 19 ◽  
pp. 68-76 ◽  
Author(s):  
Gang Wang ◽  
Zhicheng Wang ◽  
Yufei Chen ◽  
Weidong Zhao
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Matching Method ◽  
Point Matching

scholarly journals Current accuracy of surface matching compared to adhesive markers in patient-to-image registration

2019 ◽  
Vol 161 (5) ◽  
pp. 865-870 ◽  
Author(s):  
Mireli A. Mongen ◽  
Peter W. A. Willems
Keyword(s):  
Image Registration ◽  
Surface Matching ◽  
Current Accuracy

Study on Image Matching Based on Speed up Robust Features Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 1352-1356
Author(s):  
Shu Guang Wu ◽  
Shu He ◽  
Xia Yang
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Matching ◽  
Comprehensive Analysis ◽  
The Other ◽  
Matching Method ◽  
Matching Accuracy ◽  
Speed Up

Image registration is one of the fundamental problems in digital image processing, which is a prerequisite and key step for further comprehensive analysis,considering the advantages of the algorithm in speed and its disadvantage of more false matching points,a image matching method based on RANSAC and surf isproposed.The experiments results show that compared with the other algorithms,the surf algorithm improves the matching speed，as well as the matching accuracy，and exhibits good performance in terms of resisting rotation，noise,and brightness changes．

Application accuracy in frameless image-guided neurosurgery: a comparison study of three patient-to-image registration methods

2007 ◽  
Vol 106 (6) ◽  
pp. 1012-1016 ◽  
Author(s):  
Peter A. Woerdeman ◽  
Peter W. A. Willems ◽  
Herke J. Noordmans ◽  
Cornelis A. F. Tulleken ◽  
Jan Willem Berkelbach van der Sprenkel
Keyword(s):  
Image Registration ◽  
Target Location ◽  
Imaging Modality ◽  
Fiducial Marker ◽  
Optical Tracking ◽  
Surface Matching ◽  
Frameless Stereotaxy ◽  
Anatomical Landmarks ◽  
Registration Method ◽  
Image Guided

Object The aim of this study was to compare three patient-to-image registration methods in frameless stereotaxy in terms of their application accuracy (the accuracy with which the position of a target can be determined intraoperatively). In frameless stereotaxy, imaging information is transposed to the surgical field to show the spatial position of a localizer or surgical instrument. The mathematical relationship between the image volume and the surgical working space is calculated using a rigid body transformation algorithm, based on point-pair matching or surface matching. Methods Fifty patients who were scheduled to undergo a frameless image-guided neurosurgical procedure were included in the study. Prior to surgery, the patients underwent either computerized tomography (CT) scanning or magnetic resonance (MR) imaging with widely distributed adhesive fiducial markers on the scalp. An extra fiducial marker was placed on the head as a target, as near as possible to the intracranial lesion. Prior to each surgical procedure, an optical tracking system was used to perform three separate patient-to-image registration procedures, using anatomical landmarks, adhesive markers, or surface matching. Subsequent to each registration, the target registration error (TRE), defined as the Euclidean distance between the image space coordinates and world space coordinates of the target marker, was determined. Independent of target location or imaging modality, mean application accuracy (± standard deviation) was 2.49 ± 1.07 mm when using adhesive markers. Using the other two registration strategies, mean TREs were significantly larger (surface matching, 5.03 ± 2.30 mm; anatomical landmarks, 4.97 ± 2.29 mm; p < 0.001 for both). Conclusions The results of this study show that skin adhesive fiducial marker registration is the most accurate noninvasive registration method. When images from an earlier study are to be used and accuracy may be slightly compromised, anatomical landmarks and surface matching are equally accurate alternatives.

A Rapid Matching Algorithm Based on Filtering Feature Points

2011 ◽  
Vol 66-68 ◽  
pp. 1954-1959
Author(s):  
Hong Bo Zhu ◽  
Xue Jun Xu ◽  
Xue Song Chen ◽  
Shao Hua Jiang
Keyword(s):  
Image Registration ◽  
Feature Vector ◽  
High Dimensional ◽  
Feature Points ◽  
Matching Method ◽  
Registration Accuracy ◽  
Matching Algorithm ◽  
Feature Vectors ◽  
Vast Amount

Matching feature points is an important step in image registration. For high- dimensional feature vector, the process of matching is very time-consuming, especially matching the vast amount of points. In the premise of ensuring the registration, filtering the candidate vectors to reduce the number of feature vectors, can effectively reduce the time matching the vectors. This paper presents a matching algorithm based on filtering the feature points on their characteristics of the corner feature. The matching method can effectively improve the matching speed, and can guarantee registration accuracy as well.

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