--- a
+++ b/utils/preprocess.py
@@ -0,0 +1,110 @@
+import os
+import SimpleITK as sitk
+import numpy as np
+from skimage import exposure
+
+def anisotropic_diffusion_denoise_3d(input_volume, conductance_parameter, time_step, number_of_iterations):
+    '''
+    Denoise the input volume using anisotropic diffusion.
+
+    Args:
+        input_volume ('SimpleITK Image'): Input volume.
+        conductance_parameter ('float'): Conductance parameter.
+        time_step ('float'): Time step.
+        number_of_iterations ('int'): Number of iterations.
+
+    Returns:
+        output_volume ('SimpleITK Image'): Denoised volume.
+
+    '''
+    # Convert the input volume to float
+    input_volume_float = sitk.Cast(input_volume, sitk.sitkFloat32)
+
+    anisotropic_diffusion = sitk.GradientAnisotropicDiffusionImageFilter()
+    anisotropic_diffusion.SetConductanceParameter(conductance_parameter)
+    anisotropic_diffusion.SetTimeStep(time_step)
+    anisotropic_diffusion.SetNumberOfIterations(number_of_iterations)
+
+    output_volume_float = anisotropic_diffusion.Execute(input_volume_float)
+    output_volume = sitk.Cast(output_volume_float, sitk.sitkInt16)
+
+    return output_volume
+
+def bilateral_filter_3d(input_volume, domain_sigma, range_sigma):
+    '''
+    Apply bilateral filter to the input volume.
+
+    Args:
+        input_volume ('SimpleITK Image'): Input volume.
+        domain_sigma ('float'): Domain sigma.
+        range_sigma ('float'): Range sigma.
+
+    Returns:
+        output_volume ('SimpleITK Image'): Filtered volume.
+    '''
+    filtered_slices = []
+
+    for z in range(input_volume.GetDepth()):
+        # Get 2D slice
+        input_slice = sitk.Extract(input_volume, (input_volume.GetWidth(), input_volume.GetHeight(), 1), (0, 0, z))
+
+        # Apply bilateral filter to the 2D slice
+        bilateral_filter = sitk.BilateralImageFilter()
+        bilateral_filter.SetDomainSigma(domain_sigma)
+        bilateral_filter.SetRangeSigma(range_sigma)
+        output_slice = bilateral_filter.Execute(input_slice)
+
+        # Append the filtered slice to the list
+        filtered_slices.append(sitk.GetArrayFromImage(output_slice)[0]) # [0] to get the slice from the 3D volume
+
+    # Convert the list of slices to a NumPy array
+    filtered_slices_array = np.array(filtered_slices)
+    output_volume = sitk.GetImageFromArray(filtered_slices_array)
+
+    # Copy the information from the input volume to the output volume
+    output_volume.CopyInformation(input_volume)
+
+    return output_volume
+
+
+def clahe_3d(input_volume, clip_limit=0.1):
+    """
+    Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to a 3D image.
+    
+    Args:
+        input_volume (SimpleITK Image): The input image.
+
+    Returns:
+        SimpleITK Image: The output image after applying CLAHE.
+    """
+    # Get the minimum and maximum intensity values of the input image
+    min_max_filter = sitk.MinimumMaximumImageFilter()
+    min_max_filter.Execute(input_volume)
+
+    original_min = min_max_filter.GetMinimum()
+    original_max = min_max_filter.GetMaximum()
+
+    # Get the dimensions of the 3D volume
+    size_x, size_y, size_z = input_volume.GetSize()
+
+    # Determine kernel sizes in each dim relative to image shape
+    kernel_size = (size_x // 5, size_y // 5, size_z // 2)
+
+    # get the image from the input volume
+    input_volume_image = sitk.GetArrayFromImage(input_volume)
+
+    # Normalize intensity values to the range [0, 1]
+    # input_volume_normalized = sitk.RescaleIntensity(input_volume_image, outputMinimum=0, outputMaximum=1)
+    input_volume_normalized = exposure.rescale_intensity(input_volume_image, in_range='image', out_range=(0, 1))
+
+    # Apply CLAHE to the normalized image
+    input_volume_enhanced = exposure.equalize_adapthist(input_volume_normalized, kernel_size=kernel_size, clip_limit=clip_limit)
+
+    # Rescale intensity values to the original range
+    input_volume_rescaled = exposure.rescale_intensity(input_volume_enhanced, in_range='image', out_range=(original_min, original_max))
+
+    # Convert the NumPy array to a SimpleITK image
+    output_result = sitk.GetImageFromArray(input_volume_rescaled)
+    output_result.CopyInformation(input_volume)
+
+    return output_result
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