Implementing real-time adaptive filtering for medical applications on the cell processor using a generic multicore framework
2008
Adaptive filtering is a compute-intensive algorithm aimed at effectively reducing noise without blurring the structures
contained in a set of digital images. In this study, we take a generalized approach for adaptive filtering based on seven
oriented filters, each individual filter implemented by a two-dimensional (2D) convolution with a mask size of 11 by 11
pixels. Digital radiology workflow imposes severe real-time constraints that require the use of hardware acceleration
such as provided by multicore processors. Implementing complex algorithms on heterogeneous multicore architectures is
a complex task especially for taking advantage of the DMA engines. We have implemented the algorithm on a Cell
Broadband Engine (CBE) processor clocked at 3.2 GHz using a generic framework for multicore processors. This
implementation is capable of filtering images of 512 2 pixels at a throughput of 40 frames per second while allowing
changing the parameters in real time. The resulting images are directed to the DR monitor or to the real-time computed
tomography (CT) reconstruction engine.
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