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Figure 1 shows the proposed framework of the segmentation scheme.
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The main features of our framework are the segmentation of quantitative T1 maps to delineate the cortical boundaries (Bazin et al., 2014), and the use of an equivolume layering model to define an intracortical coordinate system that follows the anatomical layers of the cortex (Waehnert et al., 2014).
The analytical results show that our framework enables the segmentation of customers with respect to different point of views.
Figure 1 Flowchart of the segmentation framework.
Figure 1 reveals the flowchart of the segmentation framework.
The obtained average F-measure values for the different bandwidths and abridging parameters are displayed in Figure 3. Figure 3 F -measure values obtained for the different parametrizations of the segmentation framework.
Our research prototype implementation of the segmentation framework was analyzed concerning three different aspects: the quality of the output, the scaling on different devices with various number of processors and the time demand of the algorithm on images with different size.
Note that since the BSDS benchmark evaluates quality based on boundary information, we generated soft boundary maps in the following way: the luminance channel of the segmentation framework's output was subject to morphological dilation using a 3 × 3 cross-shaped structuring element.
We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation.
Within this framework of health care segmentation [ 3- 5] in 2004, the local government of Bogotá decided to apply a new initiative through the implementation of a Primary Health Care (PHC) strategy with a comprehensive approach.
None of the segmentation techniques was perfect.
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