Accelerated implementation of level set based segmentation

Marc J Piggott, Pascal Vallotton, John A Taylor, Tomasz P Bednarz


An Open Computing Language implementation of a level set solver for 2D and 3D image segmentation tasks is presented. An adaptive time stepping algorithm is implemented using an optimised parallel reduction kernel to compensate for a loss of algorithmic parallelisation. For a 2D data set (256x256) the execution is accelerated by a factor of 20 in the adaptive case and 100 in the non-adaptive case compared to a CPU implementation, facilitating real time interactive parameter tuning. For a 3D data set (384x397x41) the acceleration factors are 200 and 270 for the adaptive and non-adaptive cases, respectively. Though a single iteration of the adaptive method is slower compared to the non-adaptive scheme, it automatically enforces the Courant--Friedrichs--Lewy condition and reduces the number of user-tuned parameters while safely allowing larger time steps. Open Computing Language optimisations and techniques are discussed.

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level set method;OpenCL;GPU;segmentation;upwinding;adaptive timestepping

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