Biomedical Diagnostics and Clinical Technologies: Applying by Manuela Pereira, Mario Freire

By Manuela Pereira, Mario Freire

The large quantity of knowledge that a few clinical and organic functions generate require distinctive processing assets that warrantly privateness and defense, making a an important desire for cluster and grid computing. Biomedical Diagnostics and medical applied sciences: using High-Performance Cluster and Grid Computing disseminates wisdom relating to excessive functionality computing for scientific functions and bioinformatics. Containing a defining physique of analysis at the topic, this serious reference resource encompasses a priceless choice of state-of-the-art study chapters for these operating within the extensive box of scientific informatics and bioinformatics.

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1993). Minimum crossentropy thresholding. Pattern Recognition, 26, 617–625. , & Montagnat, J. (2005).

It is however rather sensitive to noise, it has problems with successful detecting of thin objects and the delivered results are often over-segmented. On the other hand, the FCM algorithm classifies the image by grouping similar data points in feature spaces into clusters. This unsupervised technique that has been successfully applied to feature analysis, clustering, and classifier designs in the fields such as astronomy, geology, medical imaging, target recognition, and image segmentation. Its disadvantage is the fact that it does not deal with the problem of intensity inhomogeneity.

Ability to perform successful segmentation using such simple initial shape is desirable, because it eliminates the need to perform manual initialization. Sensitivity to noise: describes the ability of the method to operate on noisy data (ro- Figure 11. Results of a complex shape segmentation using the vessel crawlers (C. McIntosh & G. Hamarneh, 2006) 23 Techniques for Medical Image Segmentation • • • bustness of the method). High sensitivity to noise is not desirable. Topology changes: describes the ability of the method to successfully detect changes of the model topology during the segmentation process.

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