I'm on a project of liver tumor segmentation and classification. I used Region Growing and FCM for liver and tumor segmentation respectively. Then, I used Gray Level Co-occurence matrix for texture feature extraction. I have to use Support Vector Machine for Classification. But I don't know how to normalize the feature vectors. Can anyone tell how to program it in Matlab?
To the GLCM program, I gave the tumor segmented image as input. Was I correct? If so, I think, then, my output will also be correct.
My glcm coding, as far as I have tried is,
I = imread('fzliver3.jpg');
GLCM = graycomatrix(I,'Offset',[2 0;0 2]);
stats = graycoprops(GLCM,'all')
t1= struct2array(stats)
I2 = imread('fzliver4.jpg');
GLCM2 = graycomatrix(I2,'Offset',[2 0;0 2]);
stats2 = graycoprops(GLCM2,'all')
t2= struct2array(stats2)
I3 = imread('fzliver5.jpg');
GLCM3 = graycomatrix(I3,'Offset',[2 0;0 2]);
stats3 = graycoprops(GLCM3,'all')
t3= struct2array(stats3)
t=[t1;t2;t3]
xmin = min(t); xmax = max(t);
scale = xmax-xmin;
tf=(x-xmin)/scale
Was this a correct implementation? Also, I get an error at the last line.
My output is:
stats =
Contrast: [0.0510 0.0503]
Correlation: [0.9513 0.9519]
Energy: [0.8988 0.8988]
Homogeneity: [0.9930 0.9935]
t1 =
Columns 1 through 6
0.0510 0.0503 0.9513 0.9519 0.8988 0.8988
Columns 7 through 8
0.9930 0.9935
stats2 =
Contrast: [0.0345 0.0339]
Correlation: [0.8223 0.8255]
Energy: [0.9616 0.9617]
Homogeneity: [0.9957 0.9957]
t2 =
Columns 1 through 6
0.0345 0.0339 0.8223 0.8255 0.9616 0.9617
Columns 7 through 8
0.9957 0.9957
stats3 =
Contrast: [0.0230 0.0246]
Correlation: [0.7450 0.7270]
Energy: [0.9815 0.9813]
Homogeneity: [0.9971 0.9970]
t3 =
Columns 1 through 6
0.0230 0.0246 0.7450 0.7270 0.9815 0.9813
Columns 7 through 8
0.9971 0.9970
t =
Columns 1 through 6
0.0510 0.0503 0.9513 0.9519 0.8988 0.8988
0.0345 0.0339 0.8223 0.8255 0.9616 0.9617
0.0230 0.0246 0.7450 0.7270 0.9815 0.9813
Columns 7 through 8
0.9930 0.9935
0.9957 0.9957
0.9971 0.9970
??? Error using ==> minus
Matrix dimensions must agree.
The images are:
