By Manuela Pereira, Mario Freire
The big quantity of knowledge that a few clinical and organic purposes generate require targeted processing assets that warrantly privateness and defense, making a an important want for cluster and grid computing. Biomedical Diagnostics and medical applied sciences: utilising High-Performance Cluster and Grid Computing disseminates wisdom concerning excessive functionality computing for scientific purposes and bioinformatics. Containing a defining physique of analysis at the topic, this serious reference resource contains a useful selection of state-of-the-art examine chapters for these operating within the vast 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
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. two stage Methods (based on a coarse approximation and refinement) Ardon and Cohen have proposed their segmentation method in (Ardon, Cohen, & Yezzi, 2005) and then further described it and extended it in (Ardon & Cohen, 2006) and (Ardon, Cohen, & Yezzi, 2007).
230-245). , Bland, P. , & Meyer, C. R. (2003). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging, 22(4), 483–492. , & Penedo, M. (2006). Topological Active Nets Optimization Using Genetic Algorithms. In Image Analysis and Recognition (pp. 272-282). Jain, A. , Duin, R. P. , & Jianchang, M. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37. 824819 Jain, A.
However the authors have not suggested or tested that solution in their publications. Parallel genetic algorithm refinement Method Work presented in (Fan, Jiang, & David, 2002) by Fan and Jiang introduces a two-stage approach for the segmentation problem, including steps of a quick approximation and then shape refine- 15 Techniques for Medical Image Segmentation ment with more precise method. As we can see, this is similar to the 3D Minimal Paths approach presented by Ardon and Cohen, although only the general concept is analogous.