A Digital Technique for Accurate Change Detection in Nuclear Medical Images - With Application to Myocardial Perfusion Studies Using Thallium-201

Singh, Manbir; Frei, Werner; Shibata, Tsutomu; Huth, G.C.; Telfer, Nancy

doi:10.1109/tns.1979.4329692

Cited by 41 publications

(21 citation statements)

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“…A practical choice for {f j (x,y)} is the set of bivariate polynomials. 8,20 The first three terms, {f 0 (x,y) = 1, f 1 (x,y) = x, f 2 (x,y) = y}, give the set of all possible linear transformations, which includes translation, rotation, skew, and scaling. Nonlinear transformations that correct for deformations can be made by including second-order terms:…”

Section: Transformationsmentioning

confidence: 99%

Extending Unbiased Stereology of Brain Ultrastructure to Three-dimensional Volumes

Fiala

Harris

2001

Journal of the American Medical Informatics Association

181

160

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Section: Transformationsmentioning

confidence: 99%

Extending Unbiased Stereology of Brain Ultrastructure to Three-dimensional Volumes

Fiala

Harris

2001

Journal of the American Medical Informatics Association

181

160

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“…In this study, we have tested the 2 nd -order polynomial transformation allowing the following transformations in 3D: translation, rotation, scaling, skewness, and perspective [Singh et al 1979, Schowengerdt et al, 1983. It is not clear whether further expansion of polynomial order would produce better co-registration.…”

Section: Discussionmentioning

confidence: 99%

“…This coarse alignment was achieved by a first order Polynomial transformation (Singh et al 1979), where the coefficients were estimated iteratively by minimizing the mean squared error differences between the intensities of corresponding pixels. This procedure was performed to minimize effects of differences in spatial resolution and orientation between the two modalities.…”

Section: Initial Alignment Of 3d Mri and Postmortem Volumesmentioning

confidence: 99%

“…The polynomial based warping [Singh et al 1979, Kim et al 2000 ] may be described by the following equations.…”

Section: Mri and Postmortem Volume Co-registrationmentioning

confidence: 99%

“…Warping relies on a modified n th -order non-linear polynomial approach initially developed by one of us for a different application [Singh et al 1979]. The warping algorithm was adapted to this application by evaluating seven different voxel similarity measures to discover a similarity measure optimal for this problem.…”

Section: Introductionmentioning

confidence: 99%

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Co‐registration of in vivo human MRI brain images to postmortem histological microscopic images

Singh

Rajagopalan

Kim

et al. 2008

Int J Imaging Syst Tech

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Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRIpostmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects.

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Singh

Rajagopalan

Kim

et al. 2008

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects.

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A Digital Technique for Accurate Change Detection in Nuclear Medical Images - With Application to Myocardial Perfusion Studies Using Thallium-201

Cited by 41 publications

References 1 publication

Extending Unbiased Stereology of Brain Ultrastructure to Three-dimensional Volumes

Extending Unbiased Stereology of Brain Ultrastructure to Three-dimensional Volumes

Co‐registration of in vivo human MRI brain images to postmortem histological microscopic images

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