diff --git a/scripts/preprocess_visualize.py b/scripts/preprocess_visualize.py
new file mode 100644
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+++ b/scripts/preprocess_visualize.py
@@ -0,0 +1,64 @@
+"""
+This script is to visualize a preprocessed image based on the plans.json file which is self configured by nnUNet.
+
+Usage:
+python preprocess_visualize.py HOME_FOLDER/nnUNetTrainer__nnUNetPlans__{CONFIG}/plans.json PATH_TO_INPUT_IMAGE.nii.gz PATH_TO_OUTPUT_FOLDER NNUNET_CONFIG
+
+Expected output: This script will create an output folder which would have all the patches that would be extracted from the input image for the training.
+
+Authors: Rohan Banerjee
+"""
+
+import os
+import json
+import argparse
+import SimpleITK as sitk
+import numpy as np
+
+def extract_patches(input_image, patch_size):
+    input_np = sitk.GetArrayFromImage(input_image)
+    depth, height, width = input_np.shape
+    patches = []
+
+    for i in range(0, depth, patch_size[2]):
+        for j in range(0, height, patch_size[1]):
+            for k in range(0, width, patch_size[0]):
+                patch = input_np[i:i+patch_size[2], j:j+patch_size[1], k:k+patch_size[0]]
+                patches.append(patch)
+
+    return patches
+
+def preprocess_patch(patch, preprocessing_plan):
+
+    config_3d_fullres = preprocessing_plan['configurations']['3d_fullres']
+
+    mean_intensity = np.mean(patch)
+    std_intensity = np.std(patch)
+
+    z_score_patch = (patch - mean_intensity) / std_intensity
+
+    return z_score_patch
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Preprocess NIFTI image with a nnUNet plans.json file")
+    parser.add_argument("json_file", help="Path to the plans.json configuration file")
+    parser.add_argument("input_nifti", help="Path to the input NIFTI image")
+    parser.add_argument("output_folder", help="Path to the output folder for saving patches")
+    parser.add_argument("config", help="nnUNet Training configuration. Eg: 2d, 3d_fullres etc")
+
+    args = parser.parse_args()
+
+    with open(args.json_file, 'r') as plan_file:
+        preprocessing_plan = json.load(plan_file)
+
+    input_image = sitk.ReadImage(args.input_nifti)
+    patch_size = preprocessing_plan['configurations'][args.config]['patch_size']
+
+    patches = extract_patches(input_image, patch_size)
+    for i, patch in enumerate(patches):
+        z_score_patch = preprocess_patch(patch, preprocessing_plan)
+
+        patch_output_path = os.path.join(args.output_folder, f"patch_{i}.nii.gz")
+        sitk.WriteImage(sitk.GetImageFromArray(z_score_patch), patch_output_path)
+
+    print("Patches saved in", args.output_folder)
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