Research Article
Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier
Pseudocode 1
Pseudocode of the proposed system.
Input: T2-weighted MR brain images. | Parameter: , total number of images | Step 1 (weiner filter) | for | Read the images and apply wiener filter | end | Step 2 (2D-DWT) | For | Read in the image file | Apply the DWT using for the 3rd level using “Haar” wavelet to extract the wavelet coefficients. | A matrix [] is employed to store all the coefficients. | End | Step 3. Reduce the features from the coefficients using PPCA | for | Apply PPCA transformation on the obtained wavelet coefficients. | Put the new dataset in a matrix . | End | Step 4 (RSE classification using 5 5 cross-validation) | Divide the input data and target data into 5 different groups randomly | For | Use the th group for test, and other 4 groups to train the RSE algorithm. | Classify test image | End | Calculate average specificity, sensitivity, and accuracy. |
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