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