By Manuela Pereira, Mario Freire
The large quantity of knowledge that a few clinical and organic functions generate require designated processing assets that warrantly privateness and safety, making a the most important desire for cluster and grid computing. Biomedical Diagnostics and scientific applied sciences: employing High-Performance Cluster and Grid Computing disseminates wisdom concerning excessive functionality computing for clinical functions and bioinformatics. Containing a defining physique of analysis at the topic, this serious reference resource contains a invaluable selection of state-of-the-art learn chapters for these operating within the wide 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
Barreira, Penedo, Mariño, & Ansia, 2003) Medium High when combined with noisy images Medium Very high High Very High Parallel genetic algorithm refinement (Fan, Jiang, & David, 2002) Low Low Not tested by the authors, probably Low Very high Very high Medium Hybrid methods (Metaxas & Ting, 2004; Yifei, Shuang, Ge, & Daling, 2007) Low Low Medium Medium High Medium Deformable Organisms (Chris McIntosh & Ghassan Hamarneh, 2006; C. McIntosh & G. Hamarneh, 2006) High Low Low Very high Very High High the deformable organisms.
The nodes of this mesh would be incorporating the whole image and each node would have the ability to move in a predefined neighborhood, thus evolving the shape of the whole mesh. Based on the boundary information all nodes would be classified into two categories, internal and external. The former ones model the inner topology of the object while the latter perform more similarly to the active contours, trying to fit to the edges of the object in the image. This solution had the goal of combining together the features of region-based and boundary-based segmentation techniques.
Mirtich, B. (1997). A Survey of Deformable Modeling in Computer Graphics. Cambridge: Mitsubishi Electric Research Lab. , & raud. (1999). Some Remarks on the Equivalence between 2D and 3D Classical Snakes and Geodesic Active Contours. International Journal of Computer Vision, 34(1), 19–28. 1023/A:1008168219878 Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional. , & Funka-Lea, G. (2004). Multi-label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials.