Correlation Of 3D Surfaces From Multiple Modalities In Medical Imaging

Gamboa-Aldeco, Arturo; Fellingham, Linda L.; Chen, George T.Y.

doi:10.1117/12.975430

Cited by 28 publications

(5 citation statements)

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“…1,2 In contrast, retrospective methods are attractive because they can be applied at any time after the acquisition of the image data and require no external devices. The retrospective techniques currently in use are surface matching, 6-10 principal axes transformation, [10][11][12][13][14] and voxel intensity matching. [15][16][17] Coregistration alone may not accurately match the MRI and histological data sets together.…”

Section: Introductionmentioning

confidence: 99%

Registration and warping of magnetic resonance images to histological sections

et al. 1999

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We present a method for coregistration and warping of magnetic resonance images (MRI) to histological sections for comparison purposes. This methodology consists of a modified head and hat surface-based registration algorithm followed by a new automated warping approach using nonlinear thin plate splines to compensate for distortions between the data sets. To test the methodology, 15 male Wistar rats were subjected to focal cerebral ischemia via permanent occlusion of the middle cerebral artery. The MRI images were acquired in separate groups of animals at 16-24 h (n = 9) and 48-168 h (n = 6) postocclusion. After imaging, animals were immediately sacrificed and hematoxylin- and eosin-stained brain sections were obtained for histological analysis. The MRI was coregistered and warped to histological sections. The MRI lesion areas were defined using the Eigenimage (EI) filter technique. The EI is a linear filter that maximizes the projection of a desired tissue (ischemic tissue) while it minimizes the projection of undesired tissues (nonischemic tissue) onto a composite image called an EI. When using coregistration without warping the MRI lesion area demonstrated poor correlation (r = 0.55, p > 0.01) with a percent difference between the two lesion areas of 22.5% +/- 10.8%. After warping, the MRI and histology had significant correlation (r = 0.97, p < 0.01) and a decreased percent difference of 5.56% +/- 4.31%. This methodology is simple and robust for coregistration and warping of MRI to histological sections and can be utilized in many applications for comparison of MRI to histological data.

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Section: Introductionmentioning

confidence: 99%

Registration and warping of magnetic resonance images to histological sections

et al. 1999

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“…The next logical step in 3-D imaging will be the combination of data from different modalities in one 3-D image. Possible combinations are angiography or digital subtraction angiography with CT [107], CT with MRI [108], all 3 of these [109], or laser scanning with CT [33]. Problems such as difference in scaling factors (voxel size), orientation, and patient position during the 2 examinations, and the possible lack of fiducial points that are common in both data sets, will have to be addressed.…”

Section: Discussionmentioning

confidence: 99%

Three‐dimensional imaging in craniofacial surgery

et al. 1989

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Over the past decade, three-dimensional (3-D) imaging has been developed to such a stage of perfection and to such a level of interactive selective imaging of specific anatomic and pathologic structures that craniofacial surgeons can now use this technique effectively in the planning of complicated reconstructive surgery. In addition, modeling techniques have been devised that can be used in surgical simulation and in the manufacture of implants and prosthetic devices. The technical aspects of 3-D imaging are discussed in relation to their applications in craniofacial surgery, and reference is made to the literature describing these techniques in full detail. The results are illustrated with cases that the authors have processed by means of: (a) a clinical research program that was developed on a general purpose computer which provided full flexibility in changing and improving the reconstruction algorithms (Lobregt algorithms and DEC VAX 750 computer), (b) a system under development (Pixar PICS 2000), and (c) a commercial system (Cemax 1500X). Finally, a number of emerging techniques are discussed such as surgical stimulation (electronic sculpting), and trends such as multimodality imaging.

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“…Where, C denotes the normalized crossed-correlation coefficients between images A and B, which are ROIs taken from the (k-1)th and kth frames, respectively. We found the normalized cross-correlation operation between overlapping neighboring images [9]- [11] to be the most effective and accurate method to determine the 2D offset between the two adjacent images. Since the relative change in position of the vehicle, from one frame to the next, is small and dominated by the forward motion of the vehicle, the cross-correlation operation can be run faster and more efficiently by performing the calculation over a small region of the images from the two contiguous frames (Fig.…”

Section: Mosaickingmentioning

confidence: 99%

Use of an Automatic Under-Vehicle Inspection System as a Tool to Streamline Vehicle Screening at Ports of Entry and Security Checkpoints

Ruiz

Head

2012

2012 European Intelligence and Security Informatics Conference

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Vehicle inspection at ports of entry is a critical component of border security. One part of the vehicle screening process involves customs and border protection (CBP) personnel performing a preliminary inspection of the underside of random vehicles by looking under the vehicle through a mirror mounted on a stick, searching for anomalies or foreign objects present on the undercarriage structure and components of a vehicle. If any deviances are detected, the vehicle is directed to a secondary and more thorough inspection. This paper presents a project that aims at automating this preliminary undercarriage inspection by using an automatic under-vehicle inspection system (AUVIS) and image processing algorithms to assist personnel in identifying vehicles for secondary inspection. The inspection system will image the undercarriage of every car in a lane as they approach the CBP agent and use a novel change detection algorithm to compare the captured image to a corresponding reference image of the vehicle's make and model. The software will detect and highlight any differences between the two images to provide a rapid and objective recommendation for secondary inspection.

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Correlation Of 3D Surfaces From Multiple Modalities In Medical Imaging

Cited by 28 publications

References 0 publications

Registration and warping of magnetic resonance images to histological sections

Registration and warping of magnetic resonance images to histological sections

Three‐dimensional imaging in craniofacial surgery

Use of an Automatic Under-Vehicle Inspection System as a Tool to Streamline Vehicle Screening at Ports of Entry and Security Checkpoints

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