diff --git a/src/cedalion/imagereco/forward_model.py b/src/cedalion/imagereco/forward_model.py
index 0d926b0d..a8aee6df 100644
--- a/src/cedalion/imagereco/forward_model.py
+++ b/src/cedalion/imagereco/forward_model.py
@@ -627,6 +627,7 @@ def __init__(
         assert head_model.crs == geo3d.points.crs
 
         self.head_model = head_model
+        self.measurement_list = measurement_list
 
         self.optode_pos = geo3d[
             geo3d.type.isin([cdc.PointType.SOURCE, cdc.PointType.DETECTOR])
@@ -645,9 +646,10 @@ def __init__(
 
         self.optode_pos = self.optode_pos.pint.dequantify()
         self.optode_dir = self.optode_dir.pint.dequantify()
-
+        
         self.tissue_properties = get_tissue_properties(
-            self.head_model.segmentation_masks
+            self.head_model.segmentation_masks,
+            self.measurement_list.wavelength.unique()
         )
 
         self.volume = self.head_model.segmentation_masks.sum("segmentation_type")
diff --git a/src/cedalion/imagereco/tissue_properties.py b/src/cedalion/imagereco/tissue_properties.py
index 042571c1..59f6b84c 100644
--- a/src/cedalion/imagereco/tissue_properties.py
+++ b/src/cedalion/imagereco/tissue_properties.py
@@ -82,26 +82,27 @@ class TissueType(Enum):
 # FIXME allow for wavelength dependencies
 
 
-def get_tissue_properties(segmentation_masks: xr.DataArray) -> np.ndarray:
+def get_tissue_properties(segmentation_masks: xr.DataArray, wavelengths: list) -> np.ndarray:
     """Return tissue properties for the given segmentation mask."""
     ntissues = segmentation_masks.sizes["segmentation_type"] + 1
-    tissue_props = np.zeros((ntissues, 4))
-    tissue_props[0, :] = [0.0, 0.0, 1.0, 1.0]  # background
-
-    for st in segmentation_masks.segmentation_type.values:
-        m = segmentation_masks.sel(segmentation_type=st).values
-        int_labels = np.unique(m[m > 0])
-        if len(int_labels) == 0:
-            warn("Segmentation type %s is empty." % st)
-            continue
-        int_label = int_labels.item()
-
-        if (tissue_type := TISSUE_LABELS.get(st, None)) is None:
-            raise ValueError(f"unknown tissue type '{st}'")
-
-        tissue_props[int_label, 0] = TISSUE_PROPS_ABSORPTION[tissue_type]
-        tissue_props[int_label, 1] = TISSUE_PROPS_SCATTERING[tissue_type]
-        tissue_props[int_label, 2] = TISSUE_PROPS_ANISOTROPY[tissue_type]
-        tissue_props[int_label, 3] = TISSUE_PROPS_REFRACTION[tissue_type]
+    n_wavelength = len(wavelengths)
+    tissue_props = np.zeros((ntissues, 4, n_wavelength)) #FIXME add dimension for multiple wavelengths
+   
+    for i_wl in range(n_wavelength):
+        tissue_props[0, :,i_wl] = [0.0, 0.0, 1.0, 1.0]  # background
+    
+        for st in segmentation_masks.segmentation_type.values:
+            m = segmentation_masks.sel(segmentation_type=st).values
+            int_label = np.unique(m[m > 0]).item()
+    
+            if (tissue_type := TISSUE_LABELS.get(st, None)) is None:
+                raise ValueError(f"unknown tissue type '{st}'")
+            
+            #FIXME made it so the same properties were assigned to each wavelength
+            tissue_props[int_label, 0, i_wl] = TISSUE_PROPS_ABSORPTION[tissue_type]
+            tissue_props[int_label, 1, i_wl] = TISSUE_PROPS_SCATTERING[tissue_type]
+            tissue_props[int_label, 2, i_wl] = TISSUE_PROPS_ANISOTROPY[tissue_type]
+            tissue_props[int_label, 3, i_wl] = TISSUE_PROPS_REFRACTION[tissue_type]
 
     return tissue_props
+