Cortical Surface Registration Using Texture Mapped Point Clouds and Mutual Information

This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.

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A Method for Automated Cortical Surface Registration and Labeling

Biomedical Image Registration - Lecture Notes in Computer Science ◽

10.1007/978-3-642-31340-0_19 ◽

2012 ◽

pp. 180-189 ◽

Cited By ~ 23

Author(s):

Anand A. Joshi ◽

David W. Shattuck ◽

Richard M. Leahy

Keyword(s):

Surface Registration ◽

Cortical Surface

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Optimization of landmark selection for cortical surface registration

2009 IEEE Conference on Computer Vision and Pattern Recognition ◽

10.1109/cvpr.2009.5206560 ◽

2009 ◽

Cited By ~ 1

Author(s):

Anand Joshi ◽

David Shattuck ◽

Dimitrios Pantazis ◽

Quanzheng Li ◽

Hanna Damasio ◽

...

Keyword(s):

Surface Registration ◽

Cortical Surface ◽

Landmark Selection ◽

Selection For

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Cortical surface registration for image-guided neurosurgery using laser-range scanning

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2003.815868 ◽

2003 ◽

Vol 22 (8) ◽

pp. 973-985 ◽

Cited By ~ 113

Author(s):

M.I. Miga ◽

T.K. Sinha ◽

D.M. Cash ◽

R.L. Galloway ◽

R.J. Weil

Keyword(s):

Surface Registration ◽

Cortical Surface ◽

Laser Range ◽

Image Guided ◽

Laser Range Scanning ◽

Range Scanning

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Comparison of landmark-based and automatic methods for cortical surface registration

NeuroImage ◽

10.1016/j.neuroimage.2009.09.027 ◽

2010 ◽

Vol 49 (3) ◽

pp. 2479-2493 ◽

Cited By ~ 89

Author(s):

Dimitrios Pantazis ◽

Anand Joshi ◽

Jintao Jiang ◽

David W. Shattuck ◽

Lynne E. Bernstein ◽

...

Keyword(s):

Surface Registration ◽

Cortical Surface ◽

Automatic Methods

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Toward mutual information based automatic registration of 3D point clouds

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2012.6386053 ◽

2012 ◽

Cited By ~ 6

Author(s):

Gaurav Pandey ◽

James R. McBride ◽

Silvio Savarese ◽

Ryan M. Eustice

Keyword(s):

Mutual Information ◽

Point Clouds ◽

Automatic Registration ◽

3D Point Clouds

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Tracking the 3D Configuration of Human Joint Using an MR Image Registration Technique

ASME 2010 5th Frontiers in Biomedical Devices Conference and Exhibition ◽

10.1115/biomed2010-32057 ◽

2010 ◽

Author(s):

Seyed Kamaleddin Mostafavi Yazdi ◽

Farzam Farahmand ◽

Ali Jafari

Keyword(s):

Medical Imaging ◽

Image Registration ◽

Medical Image ◽

Point Clouds ◽

Surface Modeling ◽

Surface Registration ◽

Mr Image ◽

Data Points ◽

Anatomical Information ◽

Human Joint

Surface registration is a necessary step and widely used in medical image-aided surgery. It’s relevance to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities. In this study, the kinematic relations between two point clouds with different coordinate definitions have been generated. Using Influence Method of surface modeling for extracting point clouds functions, the transformation matrix would be resulted. The proposed method was applied for an experimental femur data points (651 points) using the MRI images. These data points were transformed in a 30 degrees flexion of knee. This transformation contains [0,−9.5, 1] degrees for yaw, pitch and roll rotation and [−3, 14,−13] translation. The related results shows [0, 9.3, 0.95] degrees for rotation and [−2.85, 14.11,−13.07] translation.

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ROBUST SURFACE-MATCHING REGISTRATION BASED ON THE STRUCTURE INFORMATION FOR IMAGE-GUIDED NEUROSURGERY SYSTEM

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421400091 ◽

2021 ◽

pp. 2140009

Author(s):

XINRONG CHEN ◽

FUMING YANG ◽

ZIQUN ZHANG ◽

BAODAN BAI ◽

LEI GUO

Keyword(s):

Arbitrary Point ◽

Point Clouds ◽

Gaussian Mixture ◽

Surface Registration ◽

Surface Matching ◽

Probability Density Distribution ◽

Registration Method ◽

Structure Information ◽

Registration Error ◽

Image Guided

Image-to-patient space registration is to make the accurate alignment between the actual operating space and the image space. Although the image-to-patient space registration using paired-point is used in some image-guided neurosurgery systems, the current paired-point registration method has some drawbacks and usually cannot achieve the best registration result. Therefore, surface-matching registration is proposed to solve this problem. This paper proposes a surface-matching method that accomplishes image-to-patient space registration automatically. We represent the surface point clouds by the Gaussian Mixture Model (GMM), which can smoothly approximate the probability density distribution of an arbitrary point set. We also use mutual information as the similarity measure between the point clouds and take into account the structure information of the points. To analyze the registration error, we introduce a method for the estimation of Target Registration Error (TRE) by generating simulated data. In the experiments, we used the point sets of the cranium surface and the model of the human head determined by a CT and laser scanner. The TRE was less than 2[Formula: see text]mm, and the TRE had better accuracy in the front and the posterior region. Compared to the Iterative Closest Point algorithm, the surface registration based on GMM and the structure information of the points proved superior in registration robustness and accurate implementation of image-to-patient registration.

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