Table 2.

Performance of the correspondence metrics from computer-extracted lesion features that yielded r⩾0.5 in differentiating corresponding image pairs from noncorresponding ones. r is the correlation coefficient for the corresponding dataset and r^′ is for the noncorresponding dataset. The value after “±” is the standard error (se) associated with each AUC.

	Corresponding pairs		Noncorresponding pairs		AUC±se
	r	p value	r′	p value
I. Density and morph. features
FI,1: Gradient texture	0.53	<0.001	0.27	0.01	0.56±0.03
FI,2: Average gray level	0.58	<0.001	−0.10	0.39	0.54±0.03
FI,3: Equivalent diameter	0.62	<0.001	0.14	0.22	0.66±0.03
II. Texture features
* Lesion based
FII,1: Correlation	0.56	<0.001	0.13	0.25	0.65±0.03
FII,2: Info. corr. 1	0.50	<0.001	0.06	0.61	0.67±0.03
FII,3: Info. corr. 2	0.53	<0.001	0.09	0.40	0.67±0.03
FII,4: Max. corr.	0.53	<0.001	0.11	0.35	0.66±0.03
** ROI based
FII,5: Contrast	0.58	<0.001	0.16	0.15	0.54±0.03
FII,6: Correlation	0.67	<0.001	0.24	0.03	0.56±0.03
FII,7: Diff. variance	0.61	<0.001	0.20	0.07	0.53±0.03
FII,8: Entropy	0.51	<0.001	0.15	0.17	0.56±0.03
FII,9: Info. corr. 1	0.62	<0.001	0.16	0.15	0.61±0.03
FII,10: Info. corr. 2	0.62	<0.001	0.14	0.21	0.57±0.03
FII,11: Max. corr.	0.61	<0.001	0.11	0.33	0.55±0.03
FII,12: Sum. Average	0.63	<0.001	0.27	0.01	0.59±0.03
FII,13: Sum. Entropy	0.53	<0.001	0.16	0.15	0.57±0.03
FII,14: Sum. Variance	0.61	<0.001	0.41	<0.001	0.50±0.03
III. Distance feature
FIII,1: Distance	0.88	<0.001	0.23	0.04	0.81±0.02