CODEWITHSUNDEEP
neural-networkclassificationlibsvm
Piotr Kopczyński
Piotr Kopczyński

Reputation: 51

Getting started with LibSVM for a particular case

I have read quite a lot about LibSVM library, but I would like to ask you for some advices in my particular case. The problem is that I have some 3D medical images (DCE-MRI) of a stomach. My goal is to perform a segmentation of a kidney, and find its three parts. Therefore, I need to train a classifier - I'm going to use SVM and neural network

Feature vectors: What is available is the pixel (voxel) brightness value (I guess the value range is [0; 511]). In total, there are 71 frames, each taken every second. So the crucial feature of every voxel is how the voxel brightness/intensity is changing during the examination time. In my case, every part of a kidney has a different chart (see an example below), so the way how the voxels brightness is changing over the time will be used by the classifier. Simple visualisation of the problem

Training sets: Every training set is a vector of intensity value of one voxel (74 numbers). An example is presented below: [22 29 21 7 19 12 23 25 33 28 25 5 21 18 27 21 11 11 26 12 12 31 15 15 12 29 17 34 30 11 12 24 35 28 27 26 29 22 15 23 24 14 14 37 241 313 350 349 382 402 333 344 332 366 339 383 383 379 394 398 402 357 346 379 365 376 366 365 360 363 376 383 389 385]

Summary and question to you: I have many training sets consisting of 74 values from the range [0; 511]. I have 3 groups of voxels, which have a characteristic feature - the brightness is changing in the similar way. What I want to obtain is a classificator, which after getting one voxel vector with 74 numbers, will assess if the voxel belongs to one of these 3 groups, or to none of them.

Question: how to start with LibSVM, any advices? From what I know now is that I should transform input values to be from the range [0; 1] or [-1; 1]. I have many training sets prepared belonging to one of these 3 groups. I will be grateful for any advice, as I'm a newbie and I just need some tips just to start.

Upvotes: 0

Views: 74

Answers (1)

Runhao Lu
Runhao Lu

Reputation: 51

You can train and use you model like this:

model=svmtrain(train_label,train_feature,'-c 1 -g 0.07 -h 0'); 
% the parameters can be modified
[label, accuracy, probablity]=svmpredict(test_label,test_feaure,model);

train_label must be a vector,if there are more than two kinds of input(0/1),it will be an nSVM automatically. If you have 3 classes, you can label them using {1,2,3}.Its length is equal to the number of samples.

The feature is not restricted. It can be what ever you want.

However, you'd better preprocess them to make the results better. For example, you can change range[0:511] to range[0:1] or minus the mean of the feature.

Notice that the testset data should be preprocessed in the same way.

Hope this will help you!

Upvotes: 1

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