diff --git a/brainles_preprocessing/brain_extraction/brain_extractor.py b/brainles_preprocessing/brain_extraction/brain_extractor.py
index 03a716b..fb662d6 100644
--- a/brainles_preprocessing/brain_extraction/brain_extractor.py
+++ b/brainles_preprocessing/brain_extraction/brain_extractor.py
@@ -4,6 +4,7 @@
 from pathlib import Path
 from typing import Optional, Union
 from enum import Enum
+import numpy as np
 
 from auxiliary.io import read_image, write_image
 from brainles_hd_bet import run_hd_bet
@@ -14,7 +15,23 @@ class Mode(Enum):
     ACCURATE = "accurate"
 
 
-class BrainExtractor:
+class BrainExtractor(ABC):
+    def __init__(
+        self,
+        masking_value: Optional[Union[int, float]] = None,
+    ):
+        """
+        Base class for skull stripping medical images using brain masks.
+
+        Subclasses should implement the `extract` method to generate a skull stripped image
+        based on the provided input image and mask.
+        """
+        # Just as in the defacer, masking value is a global value defined 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 specific modality
+        self.masking_value = masking_value
+
     @abstractmethod
     def extract(
         self,
@@ -63,8 +80,17 @@ def apply_mask(
         if input_data.shape != mask_data.shape:
             raise ValueError("Input image and mask must have the same dimensions.")
 
-        # Mask and save it
-        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
+        )
 
         try:
             write_image(
@@ -78,6 +104,15 @@ def apply_mask(
 
 
 class HDBetExtractor(BrainExtractor):
+    def __init__(self, masking_value: Optional[Union[int, float]] = None):
+        """
+        Brain extraction HDBet implementation.
+
+        Args:
+            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__(masking_value=masking_value)
+
     def extract(
         self,
         input_image_path: Union[str, Path],
diff --git a/brainles_preprocessing/brain_extraction/synthstrip.py b/brainles_preprocessing/brain_extraction/synthstrip.py
index 0ea3135..eed97d6 100644
--- a/brainles_preprocessing/brain_extraction/synthstrip.py
+++ b/brainles_preprocessing/brain_extraction/synthstrip.py
@@ -21,7 +21,9 @@
 
 class SynthStripExtractor(BrainExtractor):
 
-    def __init__(self, border: int = 1):
+    def __init__(
+        self, border: int = 1, masking_value: Optional[Union[int, float]] = None
+    ):
         """
         Brain extraction using SynthStrip with preprocessing conforming to model requirements.
 
@@ -31,9 +33,10 @@ def __init__(self, border: int = 1):
 
         Args:
             border (int): Mask border threshold in mm. Defaults to 1.
-        """
+            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.border = border
 
     def _setup_model(self, device: torch.device) -> StripModel: