How do I classify using SVM Classifier in Matlab?
- by Gomathi
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. I gave the parameters exactly as in the example provided in the documentation itself.
The output I obtained was
stats =
autoc: [1.857855266614132e+000 1.857955341199538e+000]
contr: [5.103143332457753e-002 5.030548650257343e-002]
corrm: [9.512661919561399e-001 9.519459060378332e-001]
corrp: [9.512661919561385e-001 9.519459060378338e-001]
cprom: [7.885631654779597e+001 7.905268525471267e+001]
cshad: [1.219440700252286e+001 1.220659371449108e+001]
dissi: [2.037387269065756e-002 1.935418927908687e-002]
energ: [8.987753042491253e-001 8.988459843719526e-001]
entro: [2.759187341212805e-001 2.743152140681436e-001]
homom: [9.930016927881388e-001 9.935307908219834e-001]
homop: [9.925660617240367e-001 9.930960070222014e-001]
maxpr: [9.474275457490587e-001 9.474466930429607e-001]
sosvh: [1.847174384255155e+000 1.846913030238459e+000]
savgh: [2.332207337361002e+000 2.332108469591401e+000]
svarh: [6.311174784234007e+000 6.314794324825067e+000]
senth: [2.663144677055123e-001 2.653725436772341e-001]
dvarh: [5.103143332457753e-002 5.030548650257344e-002]
denth: [7.573115918713391e-002 7.073380266499811e-002]
inf1h: [-8.199645492654247e-001 -8.265514568489666e-001]
inf2h: [5.643539051044213e-001 5.661543271625117e-001]
indnc: [9.980238521073823e-001 9.981394883569174e-001]
idmnc: [9.993275086521848e-001 9.993404634013308e-001]
The thing is, I run the program for three images. But all three gave me the same output.
When I used graycoprops()
stat =
Contrast: 4.721877658740964e+005
Correlation: -3.282870417955449e-003
Energy: 8.647689474127760e-006
Homogeneity: 8.194621855726478e-003
stat =
Contrast: 2.817160447307697e+004
Correlation: 2.113032196952781e-005
Energy: 4.124904827799189e-004
Homogeneity: 2.513567163994905e-002
stat =
Contrast: 7.086638436309059e+004
Correlation: 2.459637878221028e-002
Energy: 4.640677159445994e-004
Homogeneity: 1.158305728309460e-002
The images are:
