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Before running the codes, SPM and libsvm toolbox for Matlab need to be installed %Automated segmentation of mouse brain images using extended MRF. %Bae MH, Pan R, Wu T, Badea A. %Neuroimage. 2009 Jul 1;46(3):717-25. doi: 10.1016/j.neuroimage.2009.02.012. Epub %2009 Feb 21. File list: assign_edge_nums_lattice.m ......To assign edge numbers calc_interaction_param.m ......calculate the init parameters eval_accuracy.m ......Evaluate Accuracy eval_performance.m......Evaluate Performance of the model Gen_CV_MRS_trainset.m......generate MRS trainset Gen_CV_set.m ......Generate testing dataset Gen_CV_test.m Gen_CV_test_set.m Gen_CV_trainset.m ...... Generate the eMRF trainset Gen_image_matrix.m ......convert .img files to matlab data matrix Gen_matrix_mouse.m ......generate the input data matrix Mix_over_sampling.m ...... Mixed Ratio Sampling Gen_prior_prob_mat.m ...... Generate Prior Prob mat ICM_eMRF_CV.m...... Main function of the eMRF model mkInitialLabel.m mkPriorProb.m run_eMRF_cv.m 1. Convert Original Image files to Data Matrix: 1.1 Run 'Gen_image_matrix.m' to convert .img files to matlab data matrix 1.2 Run 'Gen_matrix_mouse.m' to generate the input data matrix for following procedure. 2. Prepare MRS or simple over-sampling training set. 2.1 Run 'Gen_CV_MRS_trainset.m': using the function,this code creates the MRS training set. 3. Run 'Run_eMRF_cv.m': this code runs MRS-SVM with 5-fold CV to get the probabilistic output P(Y|X). The output of this code is 'intense_prob' which is P(Y|X) 4. Run 'calc_interaction_param.m' to get the int_param which is an input for eMRF: you need to change the file name of load in order to calculate all the samples(default one is load 'C:\SVM software\libsvm-mat-2.84-1\Data\labelr3') 5. Run 'Gen_prior_prob_mat.m' to get other 2 inputs for eMRF model: prior_prob and initLabel 6. Run the ICM to calculate P(Y|X,L) 6.1 Run 'ICM_eMRF_CV.m': this is a code for runing the ICM with different w1,w2 and w3 using 5-fold CV.
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