Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Segmentation of medical images using a genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Journal of Cognitive Neuroscience
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Fully automatic kidneys detection in 2d CT images: a statistical approach
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Non-invasive medical imaging by means of computed tomography (CT) and fMRI helps clinicians to improve diagnostics and - hopefully - treatment of patients. Due to better image resolutions as well as ever increasing numbers of patients who undergo these procedures, the amount of data that have to be analyzed puts great strain on radiologists. In an ongoing development with SALK (Salzburger Landeskrankenhaus) we propose a system for automated screening of CT data for cysts in the patient's kidney area. The proper detection of kidneys is non-trivial, due the high variance of possible size, location, levels of contrast and possible pathological anomalies a human kidney can expose in a CT slice. We employ large-scale, semi-automatically generated dictionaries (based on 107 training images) to be used in injunction with principal component analysis (PCA). Heterogeneous clusters of CPU-, GPGPU-, and Cell BE-processors are used for high-throughput-screening of CT data. For data-parallel programming CUDA, OpenCL and the IBM Cell SDK have been used. Task parallelism is based on OpenMPI and a dynamic load-balancing scheme, which demonstrates very low latencies by means of double-buffered, multi-threaded queues.