Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Hybrid parallelization of a seeded region growing segmentation of brain images for a GPU cluster

The introduction of novel imaging technologies always carries new challenges regarding the processing of the captured images. Polarized Light Imaging (PLI) is such a new technique. It enables the mapping of single nerve fibers in postmortem human brains in unprecedented detail. Due to the very high resolution at sub-millimeter scale, an immense amount of image data has to be reconstructed three-dimensionally before it can be analyzed. Some of the steps in the reconstruction pipeline require a previous segmentation of the large images. This task of image processing creates black-and-white masks indicating the object and background pixels of the original images. It has turned out that a seeded region growing approach achieves segmentation masks of the desired quality. To be able to process the immense number of images acquired with PLI, the region growing has to be parallelized for a supercomputer. However, the choice of the seeds has to be automated in order to enable a parallel execution. A hybrid parallelization has been applied to the automated seeded region growing to exploit the architecture of a GPU cluster. The hybridity consists of an MPI parallelization and the execution of some well-chosen, data-parallel subtasks on GPUs. This approach achieves a linear speedup behavior so that the runtime can be reduced to a reasonable amount

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

In this paper, we address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50x and 85x with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.Comment: 37 pages, 16 figure