Over-segmentation affects the efficiency of the algorithm and thus an effective over-segmentation algorithm is needed in the future.
Comparisons are reported to assess the extent to which differences in these segmentations affected scoring.
The accuracy of semi-automatic segmentation is affected by a number of factors coupled to the characteristics of SPECT images.
Normally, general sentiment lexicons have low coverage, and different standards of word segmentation can affect the usage of general sentiment lexicons.
Moreover, besides this possible variability in the MR data quality, also the quality of the coregistration between MR and PET images and the performance of tissue segmentation methods affect the performance of PVC, as was recently demonstrated by Greve et al. [8].
To evaluate whether the outliers and other variations in the cell segmentation results affect the biological conclusions drawn from the data, we compared subcellular spot counts on a single cell level.
Variance during segmentation can affect the shape of the contact surface and this in turn can lead to variance within subsequent mechanical analyses.
The high degree of segmentation may affect the efficiency and reliability of these indexes [ 27] and, in routine clinical settings, their applicability.
This section aims at comparing the quality of segmentations obtained using total copy numbers only (c), allelic ratios only (d) and both of them (c, d) and how the quality of the segmentation is affected by the purity of the sample.
Lip segmentation is fundamental to visual lip reading systems, because the accuracy of segmentation result directly affects the recognition rate.
