diff --git a/segmentation.py b/segmentation.py
index 26f70ec452036f359fc2a5ea73ae0bebb2e53062..8d1ca44277d8b48ddababffd5090dc4d3544c207 100644
--- a/segmentation.py
+++ b/segmentation.py
@@ -52,6 +52,7 @@ class Segmentation(object):
                                 'lowThresh': 0.2,
                                 'activateUpThresh': False,
                                 'upThresh': 0.5,
+                                'invertThresh': False,
                                 'maxholebrightness': 0.5,
 #                                'erodeconvexdefects': 0,
                                 'minparticlearea': 20,
@@ -73,9 +74,10 @@ class Segmentation(object):
         parlist = [Parameter("contrastCurve", np.ndarray, self.detectParams['contrastCurve'], helptext="Curve contrast"),
                    Parameter("activateContrastCurve", np.bool, self.detectParams['activateContrastCurve'], helptext="activate Contrast curve", show=True, linkedParameter='contrastCurve'),
                    Parameter("blurRadius", int, self.detectParams['blurRadius'], 3, 99, 1, 2, helptext="Blur radius", show=True),
-                   Parameter("activateLowThresh", np.bool, self.detectParams['activateThresh2'], helptext="activate lower threshold", show=False, linkedParameter='lowThresh'),
+                   Parameter("invertThresh", np.bool, self.detectParams['invertThresh'], helptext="Invert the current threshold", show=False),
+                   Parameter("activateLowThresh", np.bool, self.detectParams['activateLowThresh'], helptext="activate lower threshold", show=False, linkedParameter='lowThresh'),
                    Parameter("lowThresh", float, self.detectParams['lowThresh'], .01, .9, 2, .02, helptext="Lower threshold", show=True),
-                   Parameter("activateUpThresh", np.bool, self.detectParams['activateThresh2'], helptext="activate upper threshold", show=False, linkedParameter='upThresh'),
+                   Parameter("activateUpThresh", np.bool, self.detectParams['activateUpThresh'], helptext="activate upper threshold", show=False, linkedParameter='upThresh'),
                    Parameter("upThresh", float, self.detectParams['upThresh'], .01, 1.0, 2, .02, helptext="Upper threshold", show=True),
                    Parameter("maxholebrightness", float, self.detectParams['maxholebrightness'], 0, 1, 2, 0.02, helptext="Close holes brighter than..", show = True),
 #                   Parameter("erodeconvexdefects", int, self.detectParams['erodeconvexdefects'], 0, 20, helptext="Erode convex defects", show=True),     #TODO: Consider removing it entirely. It is usually not used...
@@ -320,6 +322,8 @@ class Segmentation(object):
         # thresholding
         if self.activateLowThresh and not self.activateUpThresh:
             thresh = cv2.threshold(blur, int(255*self.lowThresh), 255, cv2.THRESH_BINARY)[1]
+            if self.invertThresh:
+                thresh = 255-thresh
             if return_step=="lowThresh": return thresh, 0
             print("lower threshold")
             if self.cancelcomputation:
@@ -329,7 +333,8 @@ class Segmentation(object):
             lowerLimit, upperLimit = np.round(self.lowThresh*255), np.round(self.upThresh*255)
             thresh = np.zeros_like(blur)
             thresh[np.where(np.logical_and(blur >= lowerLimit, blur <= upperLimit))] = 255
-            
+            if self.invertThresh:
+                thresh = 255-thresh
             if return_step=="lowThresh" or return_step=="upThresh": return thresh, 0
             print("between threshold")
             if self.cancelcomputation:
@@ -338,6 +343,8 @@ class Segmentation(object):
         elif not self.activateLowThresh and self.activateUpThresh:
             thresh = np.zeros_like(blur)
             thresh[np.where(blur <= np.round(self.upThresh*255))] = 255
+            if self.invertThresh:
+                thresh = 255-thresh
             if return_step=="upThresh": return thresh, 0
             print("upper threshold")
             if self.cancelcomputation:
@@ -346,8 +353,7 @@ class Segmentation(object):
             if self.parent is not None:
                 self.parent.raiseWarning('No thresholding method selected!\nAborted detection..')
             print('NO THRESHOLDING SELECTED!')
-            return None, None, None
-            
+            return blur, 0
         
         #close holes darkter than self.max_brightness
         self.closeBrightHoles(thresh, blur, self.maxholebrightness)