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Table 2 FCM border error, precision, and recall measures for each image in the dataset

From: Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

Img. ID Border Error Precision Recall   Img. ID Border Error Precision Recall
1 99% 1 0.635   51 99.9% 0.99 0.66
2 99.98% 1 0.65   52 132.5% 1 0.5
3 100% 1 0.62   53 69.93% 1 0.45
4 101% 1 0.54   54 100% 1 0.56
5 98% 1 0.66   55 89.47% 1 0.45
6 96% 1 0.55   56 108.13% 1 0.52
7 105% 1 0.645   57 96% 0.99 0.65
8 100% 1 0.66   58 105% 1 0.62
9 89% 1 0.7   59 100% 1 0.56
10 106% 1 0.7   60 78.32% 1 0.51
11 100% 1 0.79   61 96.82% 1 0.53
12 98% 1 0.35   62 106.83% 1 0.34
13 97% 1 0.45   63 100% 1 0.71
14 99% 1 0.76   64 103.33% 0.98 0.56
15 103% 1 0.23   65 101% 1 0.47
16 98% 1 0.63   66 96.86% 0.95 0.52
17 100% 1 0.2   67 100% 1 0.65
18 89% 1 0.54   68 106.83% 1 0.62
19 99% 1 0.33   69 99% 1 0.34
20 99.9% 1 0.67   70 106.67% 1 0.49
21 92.9% 1 0.65   71 102.3% 1 0.65
22 98% 1 0.71   72 99.9% 1 0.71
23 78.3% 1 0.56   73 123% 1 0.48
24 96.8% 1 0.45   74 105.3% 1 0.53
25 106% 1 0.5   75 103.6% 1 0.5
26 123% 1 0.65   76 98% 1 0.58
27 105.4% 1 0.56   77 106.8% 1 0.45
28 104.7% 1 0.59   78 107% 1 0.76
29 98% 0.99 0.501   79 89.3% 1 0.69
30 95% 1 0.63   80 96.8% 1 0.59
31 93.7% 1 0.34   81 100% 1 0.63
32 96.8% 1 0.49   82 102.3% 1 0.34
33 100% 1 0.53   83 103.3% 1 0.56
34 101% 1 0.43   84 100% 1 0.32
35 98% 1 0.39   85 100% 1 0.67
36 103% 1 0.65   86 89% 1 0.65
37 98% 1 0.62   87 106.6% 1 0.71
38 100% 1 0.6   88 99% 1 0.56
39 89% 1 0.46   89 106.8% 0.97 0.5
40 106.6% 1 0.48   90 118.4% 1 0.48
41 93.6% 1 0.54   91 98.3% 1 0.65
42 96.8% 1 0.59   92 99.6% 1 0.62
43 100% 1 0.57   93 122.4% 1 0.45
44 89% 1 0.63   94 100% 1 0.48
45 107% 1 0.76   95 106.6% 1 0.56
46 89.3% 1 0.64   96 93.6% 1 0.43
47 96.8% 1 0.45   97 96.8% 0.99 0.51
48 106.7% 1 0.48   98 100% 1 0.53
49 99% 1 0.56   99 89% 1 0.49
50 99.9% 1 0.59   100 107% 1 0.53