Accelerating Biomedical Image Processing Using Massively Parallel Processors
Award last edited on: 1/9/17

Sponsored Program
Awarding Agency
Total Award Amount
Award Phase
Solicitation Topic Code

Principal Investigator
John Melonakos

Company Information

AccelerEyes LLC (AKA: ArrayFire )

3423 Piedmont Road Ne Suite 330
Atlanta, GA 30305
   (800) 570-1941
Location: Single
Congr. District: 05
County: Fulton

Phase I

Contract Number: 1R43LM012359-01
Start Date: 9/1/16    Completed: 2/28/17
Phase I year
Phase I Amount
During the last decade the quantity of bioimaging data has grown tremendously. Current estimates indicate that the average hospital in the USA houses some 665 TB of data of which approximately 80% is composed of unstructured image data from CT, MRI, and X­ray machines. This huge quantity of data is expected to grow at a rate of 20­40% annually, meaning hospitals could generate a total of one exabyte of new biomedical imaging data this year. The last decade has also seen the development of several new computing platforms. In particular, multi­core and massively parallel processors are ubiquitous. Of these new platforms, the sheer computational power in modern Graphical Processing Units (GPUs) have created a computing era where it is feasible for a developer to purchase a personal supercomputer with 10+ teraflops of processing ability for less than $20,000. One of the most popular components of modern biomedical imaging software, the Insight ToolKit (ITK), could benefit greatly from GPU computing. There have been two attempts to implement ITK's functionality on the GPU and although there were impressive results (accelerations between 5 ­ 800x); both projects were ultimately abandoned. As it stands, our GPU accelerated ArrayFire library already contains about 26% of ITK's core functionality, more than any competing software. Within the context of this proposal we seek to expand ArrayFire's support of ITK's functionality and create tools that will help developers use ArrayFire to leverage the massively parallel computing capabilities of GPUs from their ITK applications.

Public Health Relevance Statement:

Public Health Relevance:
We seek to accelerate image processing algorithms in one of bioimaging's most popular libraries, the Insight ToolKit (ITK), by porting a subset of this libraryto Graphical Processing Units (GPUs). Previous work has shown that ITK's functionality can be accelerated by 5­800 times. Such acceleration will dramatically reduce the time spent processing images and enable new approaches to medical image analysis.

Project Terms:
Acceleration; Algorithms; bioimaging; Caregivers; Code; Computer software; Country; Data; data management; Development; Generations; Healthcare; High Performance Computing; Hospitals; Housing; Image; Image Analysis; image processing; imaging software; insight; International; Knowledge; Libraries; Magnetic Resonance Imaging; Medical Imaging; Memory; migration; novel strategies; open source; operation; parallel computer; Performance; Persons; Phase; Process; public health relevance; Recording of previous events; Roentgen Rays; Science; Series; Software Tools; Speed; Structure; Students; supercomputer; Surveys; Techniques; Technology; Time; tool; web site; Work;

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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