Sentence examples for individual segmentation from inspiring English sources

As an alternative to the development of better individual segmentation algorithms, iris segmentation fusion as a novel fusion scenario [3] was proposed in [4].

Individual segmentation is based on local pairwise cues designed to pop out all cells simultaneously from their common background, while the constraints are derived from mutual agreement analysis on patch segmentations from a previous round of segmentation.

The proposed fusion algorithm assesses the usefulness of individual segmentation input to avoid a deterioration of results even if one of two segmentation results to be combined is inaccurate.

ROCs of all tested scenarios for pairwise combinations plotting individual segmentation algorithm's performance with the combined fusion result are given in Fig. 7 for the IITD database and LG feature extractor, Fig. 8 for IITD and QSW, Fig. 9 for CASIA and LG, and Fig. 10 for CASIA and QSW.

The proposed multi-segmentation fusion method implementing the framework in Fig. 1 uses noise masks as results of individual segmentation results to generate a best-fitted inner P and outer L boundary curve marking the true possibly occluded iris within the eye image (of course also a corresponding resulting noise mask can easily be generated).

Probability maps of cerebral infarction were rendered group-wise using the individual segmentation results of each animal.

Consider first the basic problem of obtaining individual segmentations for each of a number of samples.

Overlap of the normalized individual cortical segmentation maps resulted in a group cortical segmentation of 9014 gray matter voxels.

Individual trabecula segmentation (ITS) can be applied to HR-pQCT scans to segment the trabecular network into individual trabecular plates and rods, and provides an in-depth examination of the 3D microstructural characteristics through a series of independent ITS parameters.

Fig. 1 The flow chart of PAG template and individual PAG segmentation.

This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification.