@inproceedings{pub6670,
    abstract = {Atlas construction is an important technique in medical image analysis that plays a central role in understanding
the variability of brain anatomy. The construction often requires applying image processing operations to multiple
images (often hundreds of volumetric datasets), which is challenging in computational power as well as memory
requirements. In this paper we introduce MIP, a Multi-Image Processing streaming framework to harness the
processing power of heterogeneous CPU/GPU systems. In MIP we introduce specially designed streaming algorithms
and data structures that provides an optimal solution for out-of-core multi-image processing problems both
in terms of memory usage and computational efficiency. MIP makes use of the asynchronous execution mechanism
supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline
of out-of-core approaches. Consequently, with computationally intensive problems, the MIP out-of-core solution
could achieve the same performance as the in-core solution. We demonstrate the efficiency of the MIP framework
on synthetic and real datasets.},
    year = {2011},
    title = {Optimal Multi-Image Processing Streaming Framework on Parallel Heterogeneous Systems},
    booktitle = {Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization 2011. Eurographics Symposium on Parallel Graphics and Visualization (EGPGV-2011), April 10-11, Bangor,, Wales, United Kingdom},
    editor = {T. Kuhlen and R. Pajarola and K. Zhou},
    publisher = {o.A.},
    author = {Linh K. Ha and Jens Krüger and Joao Comba and Sarang Joshi and Cláudio T. Silva}
}