TEXTURE ANALYSIS
LIVER Fibrosis
L. Tran, T. Rohou, PA Éliat, Y. Gandon, JD de Certaines
Rennes University Hospital
COST B21 - Slovenia, Bled
March 2007, 29th-31st
Method and Data
„
Analysis
on MRonImages
Liver
Fibrosis
Texture
Analysis
Liver ofMR
Images
Texture
„
„
„
MaZda software - version 4.5
„ MaZda software - version 4.5
2D and 3D MRI-TA
2D MRI-TA and 3D MRI-TA
„
„
Database
Database
21 patients (7 F0 / 3 F1 / 1 F2 / 2 F3 / 8 F4)
„„ 3T MRI from Rennes University Hospital
„
„
„ T1w
3T THRIVE
MRI from Rennes University Hospital
3D
21 patients
Regions
„ 3D of
T1winterest
THRIVE
„
„
„
2D-TA
„
„
„
„
5 selected slices from each series of images
1 ROI drawn on each slice (= 5 ROIs per patient)
ROIs size : >100 pixels
3D-TA
„
„
2 VOIs per patient
VOIs size: about 255 voxels
1
Objectives
Classification of Fibrosis Grading
„
„
„
„
„
Healthy (F0) vs Diseased (F1+F2+F3+F4)
F0 vs (F3+F4)
F0 vs (F1+F2)
(F1+F2) vs (F3+F4)
F0 vs (F1+F2) vs (F3+F4)
2
Healthy (F0) vs Diseased (F1+F2+F3+F4)
2D-TA
3D-TA
Misclassified (%)
Fisher
Misclassified (%)
PA
MI
MI+PA+F
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
46
35
46
39
24
30
28
35
Raw+kNN
48
40
38
36
33
31
24
31
LDA+kNN
20
20
30
30
38
38
20
20
LDA+kNN
21
21
36
36
33
33
7
7
PCA+kNN
49
35
48
39
24
30
25
35
PCA+kNN
48
43
40
36
31
29
34
31
Misclassified (%)
Misclassified (%)
Fisher
PA
MI
Fisher
MI+PA+F
PA
MI
MI+PA+F
Dataset
Train
Test
Train
Test
Train
Test
Train
Test
Dataset
Train
Test
Train
Test
Train
Test
Train
Test
NDA-1
17
54
9
49
14
37
11
57
NDA-1
0
58
3
50
7
50
0
58
NDA-2
4
51
1
37
6
60
1
37
NDA-2
0
58
3
58
3
50
0
58
3
Healthy (F0) vs Diseased (F1+F2+F3+F4)
*features
1 Perc.90%
2 135dr_RLNonUni
3 Mean
4 Perc.99%
5 Perc.50%
6 S(4,0)DifEntrp
7 S(4,-4)Correlat
8 S(0,3)InvDfMom
9 S(0,1)SumAverg
10 S(0,3)Contrast
11 S(2,2)SumOfSqs
12 S(4,4)Entropy
13 Teta4
14 GrKurtosis
15 S(2,0)SumAverg
16 S(5,-5)SumOfSqs
17 S(0,5)SumOfSqs
18 S(5,0)SumVarnc
19 S(0,5)InvDfMom
20 S(0,5)Contrast
21 Vertl_ShrtREmp
22 Vertl_Fraction
23 Vertl_LngREmph
24 S(0,3)SumOfSqs
25 S(0,4)SumOfSqs
26 S(2,-2)Correlat
27 S(3,0)InvDfMom
28 S(2,-2)Contrast
29 Teta2
30 S(3,-3)InvDfMom
2D-TA
MI+PA+F/LDA
Misclassified : 20%
„
„
3D-TA
MI+PA+F/LDA
Misclassified : 7%
Mostly COM parameters selected
Better results with 3D-TA than 2D-TA
*features
1 S(4,-4,0)DifEntrp
2 S(0,3,0)SumEntrp
3 S(3,3,0)DifVarnc
4 S(4,4,0)SumVarnc
5 S(4,-4,0)Correlat
6 S(4,-4,0)Contrast
7 S(4,4,0)DifEntrp
8 S(4,4,0)DifVarnc
9 S(0,0,2)SumOfSqs
10 S(0,0,1)SumVarnc
11 Kurtosis3D
12 S(4,0,0)SumOfSqs
13 S(5,5,0)InvDfMom
14 S(0,0,4)Entropy
15 S(1,0,0)SumOfSqs
16 S(4,-4,0)Entropy
17 S(3,3,0)SumAverg
18 S(4,4,0)InvDfMom
19 S(4,0,0)InvDfMom
20 S(0,0,1)DifVarnc
21 S(0,0,1)SumOfSqs
22 S(0,1,0)SumOfSqs
23 S(0,0,5)InvDfMom
24 S(0,2,0)SumOfSqs
25 S(1,1,0)SumOfSqs
26 S(1,-1,0)Contrast
27 S(2,0,0)SumOfSqs
28 S(1,-1,0)DifVarnc
29 S(3,-3,0)SumOfSqs
30 S(1,-1,0)SumOfSqs
4
F0 vs (F3+F4)
2D-TA
Misclassified (%)
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
51
41
34
38
24
24
27
18
LDA+kNN
44
44
33
33
54
53
15
15
PCA+kNN
48
41
34
38
22
24
27
19
3D-TA
MI+PA+F/LDA
3D-TA
Misclassified (%)
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
47
56
41
41
41
32
29
53
LDA+kNN
18
18
29
29
29
29
0
0
PCA+kNN
44
56
41
41
38
32
29
44
Misclassified : 0%
*features
1 S(0,3,0)SumEntrp
2 S(3,3,0)DifVarnc
3 S(4,4,0)DifEntrp
4 S(3,3,0)Contrast
5 S(4,-4,0)DifEntrp
6 S(4,-4,0)Correlat
7 S(2,-2,0)SumVarnc
8 S(2,2,0)SumEntrp
9 S(4,-4,0)Contrast
10 S(4,4,0)DifVarnc
11 Skewness3D
12 S(3,3,0)SumEntrp
13 S(2,-2,0)SumEntrp
14 S(4,-4,0)Entropy
15 S(0,5,0)SumOfSqs
16 Kurtosis3D
17 S(1,0,0)SumOfSqs
18 S(4,4,0)SumVarnc
19 S(5,-5,0)SumEntrp
20 S(0,0,1)DifVarnc
21 S(0,0,1)SumOfSqs
22 S(0,1,0)SumOfSqs
23 S(0,0,5)InvDfMom
24 S(1,-1,0)Contrast
25 S(0,2,0)SumOfSqs
26 S(1,-1,0)SumOfSqs
27 S(4,-4,0)InvDfMom
28 S(1,-1,0)DifVarnc
29 S(5,-5,0)DifEntrp
30 S(1,-1,0)InvDfMom
5
F0 vs (F1+F2)
2D-TA
2D-TA
MI+PA+F/LDA
Misclassified (%)
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
35
42
27
36
18
40
38
53
LDA+kNN
38
38
29
29
22
22
5
5
PCA+kNN
36
42
29
36
22
40
44
49
3D-TA
Misclassified (%)
Fisher
Misclassified : 5%
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
55
23
45
27
27
50
41
36
LDA+kNN
18
18
28
27
27
27
NA
NA
PCA+kNN
55
23
45
27
27
50
41
36
*features
1 S(2,-2)SumVarnc
2 S(0,2)InvDfMom
3 S(0,5)SumOfSqs
4 S(4,4)SumOfSqs
5 S(2,-2)SumEntrp
6 WavEnHL_s-3
7 S(2,2)Correlat
8 S(4,0)Entropy
9 S(0,1)InvDfMom
10 S(0,3)Correlat
11 S(0,3)SumOfSqs
12 S(4,0)SumAverg
13 S(5,5)DifEntrp
14 Perc.10%
15 S(0,4)InvDfMom
16 S(4,-4)DifVarnc
17 S(5,-5)SumEntrp
18 Mean
19 WavEnHH_s-3
20 WavEnLL_s-2
21 S(3,-3)InvDfMom
22 S(2,0)Contrast
23 S(2,0)Correlat
24 S(2,0)DifVarnc
25 S(1,-1)DifVarnc
26 S(1,-1)Contrast
27 WavEnLL_s-3
28 S(0,4)SumOfSqs
29 GrMean
30 WavEnHH_s-1
6
(F1+F2) vs (F3+F4)
2D-TA
3D-TA
Misclassified (%)
Fisher
Misclassified (%)
PA
MI
MI+PA+F
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
33
14
37
19
37
16
26
16
Raw+kNN
25
25
18
32
25
18
21
21
LDA+kNN
24
24
24
24
23
23
9
9
LDA+kNN
18
18
4
4
7
7
NA
NA
PCA+kNN
31
16
37
17
34
16
24
16
PCA+kNN
32
25
21
32
25
18
21
21
3D-TA
PA/LDA
2D-TA
MI+PA+F/LDA
Misclassified : 5%
Misclassified : 4%
7
F0 vs (F1+F2) vs (F2+F3)
2D-TA
Misclassified (%)
Fisher
PA
MI
3D-TA
MI+PA+F/LDA
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
58
45
64
53
51
36
51
42
LDA+kNN
43
43
53
53
42
41
33
32
PCA+kNN
58
46
63
53
59
34
59
41
3D-TA
Misclassified (%)
Fisher
PA
MI
MI+PA+F
Standardization
No
Yes
No
Yes
No
Yes
No
Yes
Raw+kNN
62
60
69
62
60
50
60
50
LDA+kNN
36
36
36
38
55
50
5
5
PCA+kNN
62
62
69
64
60
50
60
50
Misclassified : 5%
*features
1 S(0,0,2)SumOfSqs
2 S(4,4,0)DifEntrp
3 S(3,3,0)DifVarnc
4 S(5,-5,0)Correlat
5 S(0,0,2)SumVarnc
6 S(4,-4,0)DifEntrp
7 S(3,3,0)Contrast
8 S(4,4,0)AngScMom
9 S(0,3,0)SumEntrp
10 S(4,-4,0)Correlat
11 S(1,-1,0)Entropy
12 S(0,0,1)SumOfSqs
13 S(0,5,0)InvDfMom
14 Kurtosis3D
15 S(5,5,0)Correlat
16 S(5,0,0)SumAverg
17 Skewness3D
18 S(4,-4,0)SumEntrp
19 S(0,0,4)Entropy
20 S(3,3,0)SumAverg
21 S(5,-5,0)AngScMom
22 S(5,-5,0)Entropy
23 S(0,1,0)AngScMom
24 S(0,1,0)Entropy
25 Perc.10%3D
26 S(1,0,0)SumOfSqs
27 S(2,-2,0)Entropy
28 Perc.90%3D
29 S(2,-2,0)AngScMom
30 S(0,0,5)InvDfMom
8
Synthesis : better classification results
Misclassification with MI+PA+F/ LDA
Classes
2D-TA
3D-TA
F0 vs Diseased
20%
7%
F0 vs (F3+F4)
15%
0%
F0 vs (F1+F2)
5%
NA
(F1+F2) vs (F3+F4)
9%
NA
F0 vs (F1+F2) vs (F3+F4)
33%
5%
„
„
3D-TA returns better results than 2D-TA
Best method for fibrosis grading classification :
„
„
Selection of parameters : MI+PA+F
Analysis and classification : LDA+kNN
9
Conclusions
„
„
„
„
MRI-TA is able to discriminate fibrosis grading
Selection of parameters
„
Mostly from coocurrence matrix (COM)
„
30 parameters from MI+PA+F usually gives better classification results
LDA + kNN
„
Gives the best classification
„
No influence with or without standardization of features (more robust than PCA or Raw
analysis?)
„
NB: about 50% of misclassified with NDA+ANN in test dataset
2D-TA versus 3D-TA
„
3D-TA results better than 2D-TA
„
„
Less ROIs in 3D than in 2D
NB: impossible to perform 3D-TA, MI+PA+F/LDA in F0 vs (F1+F2) and (F1+F2) vs (F3+F4)
(-> negatives values, integer, and values close to 0)
10