Geometric Biomedical Computing k. David E. Breen. College of Computing and
Informatics. Drexel University. Philadelphia, PA USA ...
Geometric Biomedical Computing k
David E. Breen College of Computing and Informatics Drexel University Philadelphia, PA USA
Education Background • B.A., Physics • Ph.D., Computer Engineering – Thesis: Cloth Modeling for CAD – Combined physics, geometric models and computing
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Geometric Biomedical Computing • Research area at the intersection of biology, medicine, engineering and computer science • Develops algorithms and software that solve geometry-related computing problems for a variety of biomedical applications
Research Projects • Interactive Level Set Modeling • Contour-based Surface Reconstruction • Cell Aggregation & Sorting Simulation • Bio-inspired SelfOrganization Algorithms • Biomedical Image Informatics
Level Set Models • Deformable, volumetric, implicit models • Advantages – Easily change topological genus – Ideal for complex deformable models of unknown, changing genus – Free of mesh connectivity and quality issues – No need to reparameterize during deformation
• Have used them for volume segmentation, morphing and geometric modeling
Level Set Morphing • Each point on surface moves in the direction of local normal. Step-size proportional to signed distance to target γB ! !" X ! ! = !! X ! B X !t • Regions inside expand • Regions outside contract • Guaranteed convergence • Not moving points!
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Mug-to-Chain Morph
Head Morph • Initial model derived from an MRI scan • Target model derived from a polygonal model
Interactive LS Modeling • Developed techniques, algorithms and data structures for the direct modification of level set models
Position
Paste
Blend
Interactive Cut, Paste & Blend
Level Set Models Are Ideal For 3D Printing • Guaranteed – Closed – Non-selfintersecting – Physically realizable
Freeform Editing Operators • Can directly pull, push, carve and sketch level set models
Contour-based 3D Surface Reconstruction Create a smooth surface from parallel contours
In collabora*on with K. Museth, U. of Linkoeping, Sweden and J. Nissanov, F. Garcia, Drexel U. College of Medicine
Cell Aggregation Simulation • Developed a computer simulation system to study how cells aggregate via chemotaxis
In collabora*on with P. Lelkes, Drexel School of Biomedical Engineering
Self-organizing Geometric Primitives • Cell biology inspired algorithms for spatial selforganization • Developing local interactions based on cell behaviors that lead to the formation of user-specified macroscopic 2D shapes
Biomedical Image Informatics • Develops and employs techniques from image processing/analysis, pattern recognition, machine learning and data mining. • Goal: Automated extraction of quantitative information and the construction of statistically-sound models of the structures and processes depicted in biomedical images. • Derived from Mission Statement of the UCSB Center for Bio-Image Informatics
Biomedical Image Informatics Three projects • 3D Reconstruction and Analysis of the Developing Drosophila Wing Disc • Video Analysis for the Characterization of Fly Behavior • Shape and Image Analysis for ComputerAided Diagnosis of Breast Tumors
3D Reconstruction/Analysis of the Drosophila Wing Disc • Produced 3D models of the Drosophila wing disc from stack of confocal microscopy images of stained tissue
In collabora*on with C. Dahmann, TU Dresden and F. Jülicher, Max Planck Ins*tute
3D Reconstruction/Analysis of the Drosophila Wing Disc • Produced 3D models of individual cells and calculated geometric parameters
Fly Video Analysis for Alzheimers Disease Study • Verify the usefulness of genetically-altered fruit flies for Alzheimers Disease studies • Quantify movements of flies in videos • Classify/cluster into two groups • Needed for automation Collaborators: Aleister Saunders & Dan Marenda, Drexel U.
Fly Video Analysis for Alzheimers Disease Study • Calculate features – Inter-fly distance and angle, velocities – Time together and time “looking at”
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Classify and cluster individual specimens with good and bad memory and learning abilities
Breast Cancer Histology Analysis • Automate breast cancer diagnosis via analysis of tumor histology images • Correlate tumor spatial & morphological info with health status • Estimate histologic grade and predict metastasis to nearby lymph nodes Collaborators: Fernando Garcia & Mark Zarella. DUCoM
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