Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

Table 1 Statistical results on artery tracking and structure identification in real angiographic images.

Images, Num.	EVL(%)	IR(%)	$\bar{L}$	L _∑	Average identification rate of bifurcation $({\bar{N}}^{b} / {\bar{W}}^{b}, i = 3)$						Average identification rates of different patterns (%)
					b = 1	b = 2	b = 3	b = 4	b = 5	Total	i = 1	i = 2	i = 3	i = 4
SAT, 1	99.8	99.6	950	948	1/1	2/2	2/2	3/4	0/0	8/9	100	100	88.9	75
LCA, 128	87.2	98.3	1868	1629	1.00/1.00	2.00/2.00	7.11/7.22	5.13/6.12	0.32/1.34	15.56/17.68	98.9	98.7	88.0	72.8
RCA, 32	91.7	99.1	947	868	1.00/1.00	3.10/3.15	2.65/2.70	0.30/0.33	0.01/0.02	7.05/7.20	98.3	99.5	97.9	77.5
Mean	88.1	98.5	1677	1477	1.00/1.00 (100%)	2.22/2.23 (99.6%)	6.22/6.32 (98.4%)	4.16/4.96 (83.9%)	0.26/1.08 (24.1%)	13.86/15.58 (89.0%)	98.8	98.9	90.0	73.7

Note: L _∑ and $\bar{L}$ denote the length of algorithm-segmented vessels and the total vessel length, respectively. N^b/W^b denotes the rate of bifurcation identification., where N^b denotes the number of correctly detected bifurcations for the b-th level vessel branch, while W^b denotes the total number of the detected bifurcations.

ISSN: 1475-925X