Registration of Video Images to Tomographic Images by Optimising Mutual Information Using Texture Mapping

Clarkson, Matthew J.; Rueckert, Daniel; King, Andrew P.; Edwards, Philip; Hill, Derek L. G.; Hawkes, David J.

doi:10.1007/10704282_62

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Although extensively used within image-to-image alignment [46]- [48], there are no readily apparent means for calculating mutual information in this context. The difficulty arises in determining correspondence among point cloud intensity distributions.…”

Section: B Registrationmentioning

confidence: 99%

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

Miga

Sinha

Cash

et al. 2003

IEEE Trans. Med. Imaging

140

118

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In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 ± 0.2, 2.0 ± 0.3, and 1.2 ± 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 ± 1.0, 0 9 ± 0.6, and 1.3 ± 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

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Section: B Registrationmentioning

confidence: 99%

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

Miga

Sinha

Cash

et al. 2003

IEEE Trans. Med. Imaging

140

118

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“…Leventon et al [19] and ourselves [20] have previously demonstrated that a registration algorithm based on maximizing the mutual information between optical and rendered image intensities alone can be improved by using multiple rather than single optical views. Additional registration accuracy and robustness towards varying illumination can be achieved by using texturemapped renderings [21]. Other authors have successfully used similar texture-mapped models for tracking the human head throughout image sequences [22].…”

Section: Pose Estimationmentioning

confidence: 99%

Using photo-consistency to register 2D optical images of the human face to a 3D surface model

Clarkson¹,

Rueckert

Hill³

et al. 2001

IEEE Trans. Pattern Anal. Machine Intell.

Self Cite

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AbstractÐIn this paper, we propose a novel method to register two or more optical images to a 3D surface model. The potential applications of such a registration method could be in medicine; for example, in image guided interventions, surveillance and identification, industrial inspection, computer assisted manufacture, computer assisted maintenance, or telemanipulation in remote or hostile environments. Registration is performed by optimizing a similarity measure with respect to the transformation parameters. We propose a novel similarity measure based on ªphoto-consistency.º For each surface point, the similarity measure computes how consistent the corresponding optical image information in each view is with a lighting model. The relative pose of the optical images must be known. We validate the system using data from an optical-based surface reconstruction system and surfaces derived from magnetic resonance (MR) images of the human face. We test the accuracy and robustness of the system with respect to the number of video images, video image noise, errors in surface location and area, and complexity of the matched surfaces. We demonstrate the algorithm working on 10 further optical-based reconstructions of the human head and skin surfaces derived from MR images of the heads of five volunteers. Matching four optical images to a surface model produced a 3D error of between 1.45 and 1.59 mm, at a success rate of 100 percent, where the initial misregistration was up to 16 mm or degrees from the registration position.

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“…As a result, it can be difficult even for a skilled surgeon to infer bone shape from a single endoscopic image. Better visualizations are achieved by overlaying endoscopic images with 3D surfaces obtained from CT (Computed Tomography) scanned images (Clarkson et al 1999;Dey et al 2002). However, this technique requires us to solve a complex registration problem between the CT and endoscopic images during the surgery.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Image Shape-from-Shading Framework for Near-Lighting Perspective Endoscopes

Narasimhan

Jaramaz

2009

Int J Comput Vis

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This article formulates a near-lighting shapefrom-shading problem with a pinhole camera (perspective projection) and presents a solution to reconstruct the Lambertian surface of bones using a sequence of overlapped endoscopic images, with partial boundaries in each image. First we extend the shape-from-shading problem to deal with perspective projection and near point light sources that are not co-located with the camera center. Secondly we propose a multi-image framework which can align partial shapes obtained from different images in the world coordinates by tracking the endoscope. An iterative closest point (ICP) algorithm is used to improve the matching and recover complete occluding boundaries of the bone. Finally, a complete and consistent shape is obtained by simultaneously regrowing the surface normals and depths in all views. In order to fulfill our shape-from-shading algorithm, we also calibrate both geometry and photometry for an oblique-viewing endoscope that are not well addressed before in the previous literatures. We demonstrate the accuracy of our technique using simulations and experiments with artificial bones.

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Registration of Video Images to Tomographic Images by Optimising Mutual Information Using Texture Mapping

Cited by 11 publications

References 10 publications

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

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

Using photo-consistency to register 2D optical images of the human face to a 3D surface model

A Multi-Image Shape-from-Shading Framework for Near-Lighting Perspective Endoscopes

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