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On the other hand, the proposed adaptive thresholding method used FCM (Figure 10C) to refine the segmentation boundary based on the initial delineations, therefore, the refined delineations were visually plausible as complementary to the quantitative results given in Table 3 and Figure 9. Figure 9 Comparison of proposed segmentation method with the state-of-the-art image segmentation methods.
Figure 5 Segmentation results by using two alternative segmentation methods.
Therefore, we use multiple segmentation methods.
Nine automatic PET segmentation methods were studied.
The segmentation methods were grouped into categories.
Therefore, automated segmentation of smaller brain structures has been unable to replace manual segmentation methods.
This requires detailed, accurate, and reliable brain MRI segmentation methods.
One of the most impressive nuclei segmentation methods was developed by (Al-Kofahi et al., 2010), who used watershed techniques to separate clustered nuclei and then applied graph cuts to refine the results.
The combination of the two segmentation methods speeds up the entire segmentation processing and improves the segmentation efficiency.
We introduce general mesh segmentation methods before dental specific methods.
The brain tissue segmentation methods described above have been complemented with WML segmentation [ 40].
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