A Biomedical Imaging Acceleration Testbed: NSF Award Number 0946463


David Kaeli, William Karl, Homer Pien, Badrinath Roysam, Nayda Santiago, Miriam Leeser


This project will develop the methodology for rapid parallelization of biomedical imaging applications which will result in a testbed that will provide the capability to "right-size" a multi-GPU system to best meet the goals of any biomedical imaging application. This testbed will utilize a web-based framework that will allow a larger community to leverage the technology available and it will also contain a rich library of parallelized biomedical imaging codes.



The Medusa Cluster:

The Tesla system:

Other GPU Hardware:


  • Microscopy

    Plans the Microscopy domain have have been formulated as follows.

    Step 1: Multiscale Multi-dimensional Image Pre-processor (MMiPP): Enable near-interactive exploration of scale space characteristics of images, rapid identification of optimal scale space settings & orientation diversity at different scales, and image reconstruction methods for denoising, generic smoothing, texture analysis, enhancement, and specialized enhancement (with a focus on stick, plate, and blob segmentation).

    Step 2: Basic Differential geometry package: First and second order derivates, Jacobian, Hessian, and Weingarten matrices, curvature, ridges, distance maps, plateness, ballness, and stickness fields.

    Step 3: Flexible tensor voting package: (i) voxel based (ii) object based. Both extended to handle ball, plate, and tube classes, leveraging Stage 2.

    Step 4: GPU implementation of time-critical parts of existing FARSIGHT segmentation codes for blobs, network of tubes (thin/thick/hollow), leveraging codes from previous 3 stages. Focus on rapid parameter selection methods.

  • Hyperspectral Imaging

  • Tomosynthesis Mammography

  • Cardiac CT IRT

  • Vascular Tracing

    Recent Talks and Tutorials

  • Rodrigo Dominguez recently gave a CUDA tutorial at Spelman College.
    Tutorial slides can be found here.