By Manuela Pereira, Mario Freire
The big quantity of knowledge that a few clinical and organic purposes generate require detailed processing assets that warrantly privateness and safeguard, making a an important desire for cluster and grid computing. Biomedical Diagnostics and scientific applied sciences: utilizing High-Performance Cluster and Grid Computing disseminates wisdom relating to excessive functionality computing for scientific functions and bioinformatics. Containing a defining physique of study at the topic, this serious reference resource encompasses a beneficial selection of state-of-the-art learn chapters for these operating within the huge box of clinical informatics and bioinformatics.
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Extra resources for Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing
In a recent publication (Lefèvre, 2007) Lefèvre has introduced a new formulation for the marker introduction based on a feature calculation and pixel classification. Their method has s performed well, segmenting color images of size 481×321 pixels in about 15 seconds, with significant improvement in the quality of obtained segmentation comparing to the traditional methods. The tests however are rather poor in their nature, as they have been performed with images not related to medicine, using a portable computer.
When no point could be moved to a more optimal position in its nearest neighborhood the algorithm was stopped. As it can be seen using a greedy algorithm to solve the optimum finding problem introduces the risk of stopping at a local minimum instead of the global one. This issue was further examined in (Ibáñez, Barreira, Santos, & Penedo, 2006) by Ibáñez and Barreira and new optimization techniques were introduced, namely a genetic algorithm. Authors have used a standard approach for genetic algorithms presented in (Goldberg, 1989), introducing their own solutions for crossover, mutation, spread and group mutation operators.
Information Processing in Medical Imaging. , & Sapiro, G. (1995). Geodesic active contours. Paper presented at the Proceedings of the Fifth International Conference on Computer Vision. , Staib, L. , & Duncan, J. S. (1996). Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging, 15(6), 859–870. , & Kim, Y. (1997). A methodology for evaluation of boundary detection algorithms on medical images. IEEE Transactions on Medical Imaging, 16(5), 642–652.