19 February 2013 Vasculature segmentation using parallel multi-hypothesis template tracking on heterogeneous platforms
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Abstract
We present a parallel multi-hypothesis template tracking algorithm on heterogeneous platforms using a layered dispatch programming model. The contributions of this work are: an architecture-specific optimised solution for vasculature structure enhancement, an approach to segment the vascular lumen network from volumetric CTA images and a layered dispatch programming model to free the developers from hand-crafting mappings to particularly constrained execution domains on high throughput architecture. This abstraction is demonstrated through a vasculature segmentation application and can also be applied in other real-world applications. Current GPGPU programming models define a grouping concept which may lead to poorly scoped lo­ cal/ shared memory regions and an inconvenient approach to projecting complicated iterations spaces. To improve on this situation, we propose a simpler and more flexible programming model that leads to easier computation projections and hence a more convenient mapping of the same algorithm to a wide range of architectures. We first present an optimised image enhancement solution step- by-step, then solve a separable nonlinear least squares problem using a parallel Levenberg-Marquardt algorithm for template matching, and perform the energy efficiency analysis and performance comparison on a variety of platforms, including multi-core CPUs, discrete GPUs and APUs. We propose and discuss the efficiency of a layered-dispatch programming abstraction for mapping algorithms onto heterogeneous architectures.
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Dong Ping Zhang, Lee Howes, "Vasculature segmentation using parallel multi-hypothesis template tracking on heterogeneous platforms", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550P (19 February 2013); doi: 10.1117/12.2002698; https://doi.org/10.1117/12.2002698
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