--- a
+++ b/Dataset/sliceimages.py
@@ -0,0 +1,94 @@
+import os, glob
+import nibabel as nib
+import numpy as np
+import cv2
+from bayesnoiseremoval import BayesPreprocessor as bp
+
+# STEP 1 - Load and visualize data
+imgPath = '../../mr_train/'
+maskPath = '../../train_labels/'
+
+imagePathInput = os.path.join(imgPath)
+maskPathInput = os.path.join(maskPath)
+
+imgOutput = './zdim/imagesbayes/'
+maskOutput = './zdim/masks/'
+imageSliceOutput = os.path.join(imgOutput)
+maskSliceOutput = os.path.join(maskOutput)
+
+# STEP 2 - Image normalization
+HOUNSFIELD_MIN = -1000
+HOUNSFIELD_MAX = 2000
+HOUNSFIELD_RANGE = HOUNSFIELD_MAX - HOUNSFIELD_MIN
+
+# STEP 3 - Slicing and saving
+SLICE_X = False
+SLICE_Y = False
+SLICE_Z = True
+
+SLICE_DECIMATE_IDENTIFIER = 3
+
+
+# Normalize image
+def normalizeImageIntensityRange(img):
+    img[img < HOUNSFIELD_MIN] = HOUNSFIELD_MIN
+    img[img > HOUNSFIELD_MAX] = HOUNSFIELD_MAX
+    return (img - HOUNSFIELD_MIN) / HOUNSFIELD_RANGE
+
+
+def readImageVolume(imgPath, normalize=False):
+    img = nib.load(imgPath).get_fdata()
+    if normalize:
+        return normalizeImageIntensityRange(img)
+    else:
+        return img
+
+
+# Save volume slice to file
+
+def saveSlice(img, fname, path):
+    # img = np.uint8(img * 255)
+    fout = os.path.join(path, f'{fname}.png')
+    cv2.imwrite(fout, img)
+    print(f'[+] Slice saved: {fout}', end='\r')
+
+
+def sliceAndSaveVolumeImage(vol, fname, path):
+    (dimx, dimy, dimz) = vol.shape
+    print(dimx, dimy, dimz)
+    cnt = 0
+    if SLICE_X:
+        cnt += dimx
+        print('Slicing X: ')
+        for i in range(dimx):
+            saveSlice(bp.bayes_noise_removal(image=vol[i, :, :], o=140),
+                      fname + f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_x', path)
+
+    if SLICE_Y:
+        cnt += dimy
+        print('Slicing Y: ')
+        for i in range(dimy):
+            saveSlice(bp.bayes_noise_removal(image=vol[:, i, :], o=140),
+                      fname + f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_y', path)
+
+    if SLICE_Z:
+        cnt += dimz
+        print('Slicing Z: ')
+        for i in range(dimz):
+            saveSlice(bp.bayes_noise_removal(image=vol[:, :, i], o=140),
+                      fname + f'-slice{str(i).zfill(SLICE_DECIMATE_IDENTIFIER)}_z', path)
+    return cnt
+
+
+for index, filename in enumerate(sorted(glob.iglob(imagePathInput + '*.nii.gz'))):
+    img = readImageVolume(filename)
+    print(filename, img.shape, np.sum(img.shape), np.min(img), np.max(img))
+    numOfSlices = sliceAndSaveVolumeImage(img, 'heart' + str(index), imageSliceOutput)
+    print(f'\n{filename}, {numOfSlices} slices created \n')
+
+# # Read and process image mask volumes
+# for index, filename in enumerate(sorted(glob.iglob(maskPathInput + '*.nii'))):
+#     img = readImageVolume(filename, False)
+#     print(filename, img.shape, np.sum(img.shape), np.min(img), np.max(img))
+#     numOfSlices = sliceAndSaveVolumeImage(img, 'tooth' + str(index), maskSliceOutput)
+#     print(f'\n{filename}, {numOfSlices} slices created \n')