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Table 3 Parameter values fit separately to psychophysical and fMRI data for subject djh
Table 4 Parameter values fit separately to psychophysical and fMRI data for subject gmb
TABLE 1. Range of Orbital Soft Tissue Motion Measured with MRI-DCM in 6 Normal Subjects
Table 1. Example 3: some experiments with simultaneous reconstruction for shape and optical parameters.
2006
Table 2. Comparison of the registration error in terms of squared sum of intensity differences (SSD) and correlation coefficient (CC) for the patient study in Figure 3. The region of increased uptake corresponding to the tumour has been excluded.
1998
"... In PAGE 6: ... The region of increased uptake corresponding to the tumour has been excluded from the calculations. The results are summarised in Table2 and show that the non-rigid transformation model is better able to correct the motion of the breast than the rigid and affine transformations. 4 Discussion We have developed a fully automated algorithm for the non-rigid registration of 3D breast MRI based on normalised mutual information.... ..."
Cited by 15
Table 3: mode values of two test subjects (normalized by standard deviation)
"... In PAGE 8: ... Scan data are processed from two test individuals not included in the training database. Mode values obtained by regression are under 3 times standard deviation (see Table3 ) and for most of them under standard deviation. The mean accuracy of their reconstruction is 4 mm for the skull which is still less than our registration noise.... ..."
Table 2.1. Simultaneous object state estimation and localization for a moving robot.
Table 10. Simultaneous registration and cali- bration as a function of the number of maps ( = 0:1; = 0:1; Ns = 100).
1999
Cited by 10
Table 2. Comparison of the registration error in terms of squared sum of intensity differences (SSD) and correlation coefficient (CC) for the patient study in Figure 3. The region of increased uptake corresponding to the tumour has been excluded.
"... In PAGE 6: ... The region of increased uptake corresponding to the tumour has been excluded from the calculations. The results are summarised in Table2 and show that the non-rigid transformation model is better able to correct the motion of the breast than the rigid and affine transformations. 4 Discussion We have developed a fully automated algorithm for the non-rigid registration of 3D breast MRI based on normalised mutual information.... ..."
Table 3: Error measurements for segmentation of clinical MRI cases.
"... In PAGE 10: ... The segmentation method was able to handle multiple challenges without any a priori information or shape constraints that include the extraction of highly-convoluted white matter surfaces, the extraction of separate ventricular structures for the CSF, and handling of different volume sizes of the three structures in a simultaneous segmentation scheme. Error measurements for the segmentation of the three clinical cases are reported in Table3 . These results report overall clinically satisfactory (useful) performance of the proposed segmentation method.... ..."
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