Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Segmentation of medical images using a genetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
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
High-Throughput-Screening of medical image data on heterogeneous clusters
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
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Automated classification of medical (computed tomography) images may ultimately lead to faster and improved diagnosis, benefiting both patients and clinicians. We describe a software system, that can be trained for classification purposes in the area of medical image processing. The underlying algorithm is based on a set of perceptron-like feature detectors, which are combined to short feature vectors. Those are used to form self-organized Kohonen maps, which will be used for the classification of new image data. The exact description of the feature detectors is derived from a large set of sample images by way of an evolutionary strategy. This leads to a computationally demanding process of iterated image decomposition, Kohonen map training and quality assessment. To make our method feasible, we rely on clusters of rather cheap commodity hardware, namely general purposes graphics processing units (GPGPU [5]) and the STI Cell Broadband Engine Architecture (Cell), as it comes with the PS3 gaming console.