This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.
Optical imaging offers exquisite sensitivity and resolution for assessing biological tissue in microscopy applications; however, for samples that are greater than a few hundred microns in thickness (such as whole tissue biopsies), spatial resolution is substantially limited by the effects of light scattering. To improve resolution, time-and angular-domain methods have been developed to reject detection of highly scattered light. This work utilizes a modified version of a commonly used Monte Carlo light propagation software package (MCML) to present the first comparison of time-and angular-domain improvements in spatial resolution with respect to varying sample thickness and optical properties (absorption and scattering). Specific comparisons were made at various tissue thicknesses (1-6 mm) assuming either typical (average) soft tissue scattering properties, μ s ' = 10 cm −1 , or low scattering properties, μ s ' = 3.4 cm −1 , as measured in lymph nodes.
In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.
The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.
In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.
We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.
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