Brain-Tissue-Segmentation-Using-Expectation-Maximization

Medical Image Segmentation and Applications (MISA) LAB task.

Functions Used in two codes::

1. show_slice(img, slice_no):

   Inputs: Name of the Image Array, img=name.get_fdata() Slice number you want to knoe,Slice no = 24 output: Plot Image.

2. gmm(x, mean, cov):

   Inputs: x (numpy.ndarray): nxd dimentional array. where n= number of samples d= dimention mean (numpy.ndarray): d-dimentional mean value. cov (numpy.ndarray): dxd dimentional covariance matrix.

   output: (numpy.ndarray): Gaussian mixture for every point in feature space.

3. dice_similarity(Seg_img, GT_img,state):

   Inputs: Seg_img (numpy.ndarray): Segmented Image. GT_img (numpy.ndarray): Ground Truth Image. State: "nifti" if the images are nifti file "arr" if the images are an ndarray

   output: Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM.

4. Dice_and_Visualization_of_one_slice(Seg_img, GT_img,state,number_of_slice): """Example Use: Dice_and_Visualization_of_one_slice(Seg,Label_img,"arr",30)"""

   Inputs: Seg_img (numpy.ndarray): Segmented Image. GT_img (numpy.ndarray): Ground Truth Image. State: "nifti" if the images are nifti file "arr" if the images are an ndarray output: Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM. Ploting image