diff --git a/TPTBox/core/nii_wrapper.py b/TPTBox/core/nii_wrapper.py
index 81e8b70..3481259 100755
--- a/TPTBox/core/nii_wrapper.py
+++ b/TPTBox/core/nii_wrapper.py
@@ -965,6 +965,23 @@ def normalize_to_range_(self, min_value: int = 0, max_value: int = 1500, verbose
             self.set_dtype_(self_dtype)
         log.print(f"Shifted from range {mi, ma} to range {self.min(), self.max()}", verbose=verbose)
 
+    def get_histogram(self, bins=256, hrange=None, density=False, c_val:float|None=None):
+        """Returns the histogram of the image array.
+
+        Args:
+            bins (int, optional): Number of bins for the histogram. Defaults to 256.
+            range (tuple, optional): Range of values to consider for the histogram. Defaults to None.
+            density (bool, optional): If True, the result is the probability density function at the bin, normalized such that the integral over the range is 1. Defaults to False.
+            c_val (float|None, optional): The value below which all values are set to c_val. Defaults to None.
+
+        Returns:
+            tuple: A tuple containing the histogram values and the bin edges.
+        """
+        arr = self.get_array()
+        if c_val is not None:
+            arr[arr <= c_val] = c_val
+        return np.histogram(arr, bins=bins, range=hrange, density=density)
+
     def match_histograms(self, reference:Image_Reference,c_val = 0,inplace=False):
         assert not self.seg
         ref_nii = to_nii(reference)
@@ -1268,6 +1285,7 @@ def filter_connected_components(self, labels: int |list[int]|None=None,min_volum
         """
         assert self.seg, "This only works on segmentations"
         arr = np_filter_connected_components(self.get_seg_array(), largest_k_components=max_count_component,label_ref=labels,connectivity=connectivity,return_original_labels=keep_label,min_volume=min_volume,max_volume=max_volume,removed_to_label=removed_to_label,)
+        assert arr.shape == self.shape, f"Shape mismatch: {arr.shape} != {self.shape}"
         if keep_label and labels is not None:
             if isinstance(labels,int):
                 labels = [labels]
diff --git a/TPTBox/core/np_utils.py b/TPTBox/core/np_utils.py
index 51bd178..9816f98 100755
--- a/TPTBox/core/np_utils.py
+++ b/TPTBox/core/np_utils.py
@@ -357,7 +357,7 @@ def np_dilate_msk(
             data = out.copy()
             data[i != data] = 0
             if use_crop:
-                lcrop = np_bbox_binary(data, px_dist=2, raise_error=False)
+                lcrop = np_bbox_binary(data, px_dist=2 + n_pixel, raise_error=False)
                 data = data[lcrop]
             msk_ibe_data = _binary_dilation(data, struct=struct)
 
diff --git a/TPTBox/core/poi.py b/TPTBox/core/poi.py
index e321daf..71cf2b3 100755
--- a/TPTBox/core/poi.py
+++ b/TPTBox/core/poi.py
@@ -314,6 +314,7 @@ def apply_crop(self: Self, o_shift: tuple[slice, slice, slice] | Sequence[slice]
         """
         origin: COORDINATE = None  # type: ignore
         shape = None  # type: ignore
+        o_shift = tuple(o if o.start is not None else slice(0, None) for o in o_shift)
         try:
 
             def shift(x, y, z):
@@ -1066,7 +1067,7 @@ def calc_centroids_from_two_masks(
     org_shape = subreg_msk.shape
     # crop to mask to speed up the segmentation
     crop = vert_msk.compute_crop()
-    crop = subreg_msk.compute_crop(maximum_size=crop)
+    # crop = subreg_msk.compute_crop(maximum_size=crop)
     # crop = (slice(0, subreg_msk.shape[0]), slice(0, subreg_msk.shape[1]), slice(0, subreg_msk.shape[2]))
 
     vert_msk = vert_msk.apply_crop(crop)