Patch Analysis-based Surface (Correntropy-induced) Sparse-coding and Max-Pooling (PASS-MP and PASCS-MP)
Version 1.0
Authors:
| Jianfeng Wu | [email protected] |
| Jie Zhang | [email protected] |
| Qingyang Li | [email protected] |
| Yalin Wang | [email protected] |
Pre-required:
Matlab 2012a and above
g++ 4.6 and above
Download:
Step 1: select the vertex information from .m file and convert .m file to .txt file
>>matlab -nojvm
convert_m_txt(fileLocation, lineStart, lineEnd);
fileLocation = ‘/you/jfeature.m/folder/’;
lineStart is the first vertex id;
lineEnd is the last vertex id;
>> cd /PASSS_v1.0/
>> convert_m_txt(‘/your local jfeature folder/jfeature/’, 1, 30000);
Here is an example folder in the package ‘/PASS_v1.0/example/’
>> cd /PASSS_v1.0/
>> convert_m_txt(‘/PASS_v1.0/example/’, 1, 30000);
Step 2: read jfeature into Matlab
If there are left jfeature and right jfeature, please use function twice, each time with 15000 features
if there is only one jfeature file, please use function once
data = readjfeat(fileLocation, vertexDim, featNum, subjectNum)
fileLocation = ‘/you/jfeature_vertex.txt/folder/’;
vertexDim is the dimension of vertex;
featNum is the number of feature number of mTBM/TBM (featNum = 3) or MMS (featNum = 4);
subjectNum is the number of total subjects;
>> cd /PASS_v1.0/
>>MMSdata = readjfeat(‘/you/jfeature_vertex.txt/folder/’, 30000, 4, 8);
Here is an example folder in the package ‘/PASS_v1.0/example/’
>> cd /PASS_v1.0/
>> MMSdata = readjfeat(‘/PASS_v1.0/example/’, 30000, 4, 8);
If your jfeature has two parts: left_jfeature and right_jfeature,
please use following commands:
MMSdata = readjfeat(‘/you/left_jfeature_vertex.txt/folder/’, leftvertexDim, featNum, subjectNum);
MMSdata(leftvertexDim+1:leftvertexDim+rightvertexDim, :, 🙂 = readjfeat(‘/you/right_jfeature_vertex.txt/folder/’, rightvertexDim, featNum, subjectNum);
e.g., the hippocampus jfeature has left_jfeature.m and right_jfeature.m
the left vertex dimension is 15000, the right vertex dimension is 1500
the command will as follows:
>> MMSdata = readjfeat(‘/you/left_jfeature_vertex.txt/folder/’, 15000, 4, 4;
>> MMSdata(15001:30000, :, 🙂 = readjfeat(‘/you/right_jfeature_vertex.txt/folder/’, 15000, 4, 4);
Step 3: reshape the jfeature data into original template shape
[left, right]=reshape_original(data, featstart, featend, leftvertexNum)
data is the jfeature.mat
featstart is the start feature order (1, .., 4)
featend is the end feature order (1, .., 4)
leftvertexNum is the number of vertexes of left_jfeature
e.g., the MMS data is consisting of Radial Distance (RD) and mTBM,
>> cd /PASS_v1.0/
>>[leftMMS, rightMMS] = reshapre_original(MMSdata, 1, 4, 15000);
Step 4: select patches on surface data and save as patchesfile.txt
[patchSamples, patchNum] = select_patches(filename, SampleNum, Sample1, Sample2, featNum);
filename = ‘you_output_patches_filename.txt’;
SampleNum is the total number of subjects
Sample1 is the left feature data
Sample2 is the right feature data
featNum is the dimension of your RD/mTBM/TBM/MMS feature on each vertex
patchNum is the number of patches for each subject
[patchSamples, patchNum_MMS] = select_patches(‘MMS_patches.txt’, 8, leftMMS, rightMMS, 4);
patchNum = 1008 %show on the screen
exit the Matlab
exit()
Step 5.1: learning dictionary and sparse codes by stochastic coordinate coding
This part is implemented by C++.
./run featureDim sampleDim SampleFileName DictionaryFileName FeatureFileName
featureDim is the dimension of sparses feature (usually 5 times of input patch dimension)
sampleDim is the dimension of input features
SampleFileName = ‘MMS_patches.txt’ (the name of patch file learned from step 4)
FeatureFileName = ‘MMS_sparse.txt’ (the name of the output)
DictionaryFileName = ‘MMS_dict.txt’ (the name of the codebook of dictionary learning)
e.g., for hippocampus MMS data, featureDim = 2000, sampleDim = 400
./run 2000 400 MMS_patches.txt MMS_sparse.txt MMS_dict.txt
Step 5.2: learning dictionary and sparse codes by Correntropy-induced stochastic coordinate coding
This part is implemented by C++. The executable file could be download here.
./run_correntropy SampleFileName FeatureFileName RandomInitializedDictionary DictionaryFileName lambda sigma featureDim sampleDim
SampleFileName = ‘MMS_patches.txt’ (the name of patch file learned from step 4)
FeatureFileName = ‘MMS_sparse.txt’ (the name of the output)
RandomInitializedDictionary = ‘MMS_initialdict.txt’ (save the initial dictionary)
DictionaryFileName = ‘MMS_dict.txt’ (the name of the codebook of dictionary learning)
lambda is the regularization parameter
sigma is the kernal size of the correntropy loss function
featureDim is the dimension of sparses feature (usually 5 times of input patch dimension)
sampleDim is the dimension of input features
e.g., for hippocampus MMS data, featureDim = 2000, sampleDim = 400, lambda = 2.2, sigma = 3.6
./run_correntropy MMS_patches.txt MMS_sparse.txt MMS_initialdict.txt MMS_dict.txt 2.2 3.6 2000 400
Step 6: Max-pooling
This part is implemented by C++.
./MaxPooling featureDim batchSize FeatureFileName outputFile
FeatureFileName = ‘MMS_sparse.txt’ (the name of the output from step 5)
featureDim is the dimension of sparses feature (same as step 5)
batchSize is the number of patches for each subject
outputFile = ‘MMS_features.txt’ (the name of the features which we will use do the prediction)
./MaxPooling 2000 1008 MMS_sparse.txt MMS_features.txt