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4) Boundary uncertainty maps is usually produced utilizing distinctive tactics, but their corresponding segmentation general performance was quite similar (
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was applied simultaneously in morphological operations and Gaussian filter because it can be sure that pixels in the middle region of boundary uncertainty map have much more substantial contrast or intensity, as compared to the counterparts in other areas.
Table 8 showed the efficiency in the created method when applying unique values to the parameters while in the morphological operations and Gaussian filter. With the desk, our formulated approach obtained a remarkable All round performance if the morphological functions and Gaussian filter shared the exact same benefit for each impression dataset, which may proficiently highlight the center areas of boundary uncertainty maps, as revealed in Determine 6.