Merge pull request #168 from BrainLesion/166-feature-adjustment-for-ct-image-defacing

Updated defacer to either use minimum or custom value

Author: neuronflow

brainles_preprocessing/defacing/defacer.py

@@ -1,32 +1,41 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Union
-
+from typing import Union, Optional
+import numpy as np
 from auxiliary.io import read_image, write_image
 
 
 class Defacer(ABC):
-    """
-    Base class for defacing medical images using brain masks.
-
-    Subclasses should implement the `deface` method to generate a defaced image
-    based on the provided input image and mask.
-    """
-
-    @abstractmethod
-    def deface(
+    def __init__(
         self,
-        input_image_path: Union[str, Path],
-        mask_image_path: Union[str, Path],
-    ) -> None:
+        masking_value: Optional[Union[int, float]] = None,
+    ):
         """
-        Generate a defacing mask provided an input image.
+        Base class for defacing medical images using brain masks.
 
-        Args:
-            input_image_path (str or Path): Path to the input image (NIfTI format).
-            mask_image_path (str or Path): Path to the output mask image (NIfTI format).
+        Subclasses should implement the `deface` method to generate a defaced image
+        based on the provided input image and mask.
         """
-        pass
+        # Here, masking value functions as a global value across all images and modalities
+        # If no value is passed, the minimum of a given input image is chosen
+        # TODO: Consider extending this to modality-specific masking values in the future, this should
+        # probably be implemented as a property of the the specific modality
+        self.masking_value = masking_value
+
+        @abstractmethod
+        def deface(
+            self,
+            input_image_path: Union[str, Path],
+            mask_image_path: Union[str, Path],
+        ) -> None:
+            """
+            Generate a defacing mask provided an input image.
+
+            Args:
+                input_image_path (str or Path): Path to the input image (NIfTI format).
+                mask_image_path (str or Path): Path to the output mask image (NIfTI format).
+            """
+            pass
 
     def apply_mask(
         self,
@@ -63,8 +72,17 @@ def apply_mask(
         if input_data.shape != mask_data.shape:
             raise ValueError("Input image and mask must have the same dimensions.")
 
-        # Apply mask (element-wise multiplication)
-        masked_data = input_data * mask_data
+        # check whether a global masking value was passed, otherwise choose minimum
+        if self.masking_value is None:
+            current_masking_value = np.min(input_data)
+        else:
+            current_masking_value = (
+                np.array(self.masking_value).astype(input_data.dtype).item()
+            )
+        # Apply mask (element-wise either input or masking value)
+        masked_data = np.where(
+            mask_data.astype(bool), input_data, current_masking_value
+        )
 
         # Save the defaced image
         write_image(

brainles_preprocessing/defacing/quickshear/quickshear.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Union
+from typing import Union, Optional
 
 import nibabel as nib
 from auxiliary.io import read_image, write_image
@@ -35,15 +35,17 @@ def __init__(
         buffer: float = 10.0,
         force_atlas_registration: bool = True,
         atlas_image_path: Union[str, Path, Atlas] = Atlas.SRI24,
+        masking_value: Optional[Union[int, float]] = None,
     ):
         """Initialize Quickshear defacer
 
         Args:
             buffer (float, optional): buffer parameter from quickshear algorithm. Defaults to 10.0.
             force_atlas_registration (bool, optional): If True, forces atlas registration of the BET mask before defacing to potentially boost quickshear performance. Defaults to True.
             atlas_image_path (Union[str, Path, Atlas], optional): Path to the atlas image or an Atlas enum value that will be used for the optional atlas registrations. Defaults to Atlas.SRI24.
+            masking_value (Optional[Union[int, float]], optional): global value to be inserted in the masked areas. Default is None which leads to the minimum of each respective image.
         """
-        super().__init__()
+        super().__init__(masking_value=masking_value)
         self.buffer = buffer
         self.force_atlas_registration = force_atlas_registration
         self.atlas_image_path = atlas_image_path