Table 7 Values of objective measures for Dataset 2, where bold faces indicate better results.

From: Similarity measure for intuitionistic fuzzy sets and its applications in pattern recognition and multimodal medical image fusion

Image

Method

Mean

SD

SF

FMI

AG

Xydeas

\(S_1\)

PCA-DWT

37.72

58.07

7.42

0.88

6.96

0.83

CNP-MIF

43.52

66.06

7.50

0.89

8.04

0.80

DTNP-MIF

45.14

64.93

23.80

0.88

6.61

0.70

MDLSR-RFM

41.24

63.02

7.36

0.79

7.93

0.59

IFS-based method

45.80

69.02

30.36

0.90

7.97

0.85

Proposed method

46.06

69.61

32.59

0.89

8.31

0.84

\(S_2\)

PCA-DWT

34.55

49.67

6.16

0.89

4.25

0.80

CNP-MIF

40.72

56.99

6.45

0.90

5.08

0.77

DTNP-MIF

42.54

57.11

14.36

0.89

4.39

0.66

MDLSR-RFM

37.36

52.95

6.42

0.80

5.09

0.53

IFS-based method

42.90

59.90

16.95

0.91

5.03

0.83

Proposed method

43.29

60.16

18.52

0.89

5.19

0.82

\(S_3\)

PCA-DWT

37.75

58.56

7.19

0.87

6.14

0.83

CNP-MIF

42.32

64.87

7.31

0.88

7.20

0.80

DTNP-MIF

45.46

66.42

21.08

0.86

5.85

0.71

MDLSR-RFM

38.51

59.77

7.16

0.79

7.10

0.60

IFS-based method

46.18

70.21

27.15

0.89

7.11

0.89

Proposed method

46.49

70.80

29.05

0.87

7.38

0.84

\(S_4\)

PCA-DWT

45.53

55.97

7.54

0.90

6.98

0.76

CNP-MIF

49.30

63.99

7.62

0.91

8.14

0.73

DTNP-MIF

51.52

59.76

22.70

0.90

7.02

0.65

MDLSR-RFM

48.79

64.54

7.51

0.82

8.05

0.50

IFS-based method

52.58

66.66

28.15

0.91

8.04

0.80

Proposed method

52.93

67.47

31.01

0.90

8.68

0.77

\(S_5\)

PCA-DWT

48.11

58.69

8.12

0.89

7.67

0.74

CNP-MIF

52.28

67.29

8.21

0.89

8.92

0.72

DTNP-MIF

54.58

65.44

25.57

0.89

8.35

0.65

MDLSR-RFM

51.35

67.05

8.17

0.76

8.93

0.43

IFS-based method

65.45

81.36

26.28

0.92

7.48

0.82

Proposed method

65.52

81.82

28.97

0.91

7.81

0.79