Mr. Michael W. Graham Profile

Michael W. Graham

at Pennsylvania State Univ

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Author

Publications (3)

Proceedings Article | 12 March 2008 Paper

Integrated system for planning peripheral bronchoscopic procedures

Jason Gibbs, Michael Graham, Kun-Chang Yu, William Higgins

Proceedings Volume 6916, 69160H (2008) https://doi.org/10.1117/12.768707

KEYWORDS: Image segmentation, Biopsy, Information technology, Chest, Lung cancer, Video, 3D image processing, Image registration, Image processing, Bronchoscopy

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Proceedings Article | 11 March 2008 Paper

Robust system for human airway-tree segmentation

Michael Graham, Jason Gibbs, William Higgins

Proceedings Volume 6914, 69141J (2008) https://doi.org/10.1117/12.768706

KEYWORDS: Image segmentation, Image processing algorithms and systems, Reconstruction algorithms, Visualization, Image filtering, Lung, Nonlinear filtering, Scanners, Chest, Bronchoscopy

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Proceedings Article | 10 March 2006 Paper

Globally optimal model-based matching of anatomical trees

Michael Graham, William Higgins

Proceedings Volume 6144, 614415 (2006) https://doi.org/10.1117/12.651719

KEYWORDS: 3D image processing, Image segmentation, 3D modeling, Mathematical modeling, Computer programming, Computed tomography, 3D scanning, Chest, Model-based design, 3D metrology

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Modern MDCT and micro-CT scanners are able to produce high-resolution three-dimensional (3D) images of anatomical trees, such as the airway tree and the heart and liver vasculature. An important problem arising in many contexts is the matching of trees depicted in two different images. Three basic steps are used in order to match two trees: (1) image segmentation, to extract the raw trees from a given pair of 3D images; (2) axial-analysis, to define the underlying centerline structure of the trees; and (3) tree matching, to match the centerline structures of the trees. We focus on step (3). This task is complicated by several problems associated with current segmentation and axial-analysis methods, including missing branches, false branches, and other topological errors in the extracted trees. We propose a model-based approach in which the extracted trees are assumed to arise from an initially unknown common structure corrupted by a sequence of modelled topological deformations. We employ a novel mathematical framework to directly incorporate this model into the matching problem. Under this framework, it is possible to define the set of matches that are consistent with a given deformation model. The optimal match is the member of this set that maximizes a user-definable similarity measure. We present several such similarity measures based upon geometrical attributes (e.g., branch lengths, branching angles, and relative branchpoint locations as measured from the 3D image data). We locate the globally optimal match via an efficient dynamic programming algorithm. Our primary analytical result is a set of sufficient conditions on the user-definable similarity measure such that our dynamic programming algorithm is guaranteed to locate an optimal match. Experimental results have been generated for 3D human CT chest scans and micro-CT coronary arterial-tree images of mice. The resulting matches are in good agreement with correspondences defined by human experts.

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