diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 24167abe75..8b1a77ea4c 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -324,6 +324,8 @@ Intensity
 
 `SavitzkyGolaySmooth`
 """""""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/SavitzkyGolaySmooth.png
+    :alt: example of SavitzkyGolaySmooth
 .. autoclass:: SavitzkyGolaySmooth
     :members:
     :special-members: __call__
@@ -408,6 +410,8 @@ Intensity
 
 `RandRicianNoise`
 """""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/RandRicianNoise.png
+    :alt: example of RandRicianNoise
 .. autoclass:: RandRicianNoise
     :members:
     :special-members: __call__
@@ -1173,6 +1177,14 @@ Intensity (Dict)
     :members:
     :special-members: __call__
 
+`RandRicianNoised`
+""""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/RandRicianNoised.png
+    :alt: example of RandRicianNoised
+.. autoclass:: RandRicianNoised
+    :members:
+    :special-members: __call__
+
 `ScaleIntensityRangePercentilesd`
 """""""""""""""""""""""""""""""""
 .. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/ScaleIntensityRangePercentilesd.png
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index b0553ff8c7..35f7930df0 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -873,7 +873,7 @@ def generate_spatial_bounding_box(
     margin: Union[Sequence[int], int] = 0,
 ) -> Tuple[List[int], List[int]]:
     """
-    generate the spatial bounding box of foreground in the image with start-end positions.
+    generate the spatial bounding box of foreground in the image with start-end positions (inclusive).
     Users can define arbitrary function to select expected foreground from the whole image or specified channels.
     And it can also add margin to every dim of the bounding box.
     The output format of the coordinates is:
diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py
index 59d359639b..7dc2fbae6d 100644
--- a/monai/transforms/utils_create_transform_ims.py
+++ b/monai/transforms/utils_create_transform_ims.py
@@ -102,9 +102,11 @@
     RandGibbsNoise,
     RandHistogramShift,
     RandKSpaceSpikeNoise,
+    RandRicianNoise,
     RandScaleIntensity,
     RandShiftIntensity,
     RandStdShiftIntensity,
+    SavitzkyGolaySmooth,
     ScaleIntensityRange,
     ScaleIntensityRangePercentiles,
     ShiftIntensity,
@@ -130,6 +132,7 @@
     RandGibbsNoised,
     RandHistogramShiftd,
     RandKSpaceSpikeNoised,
+    RandRicianNoised,
     RandScaleIntensityd,
     RandShiftIntensityd,
     RandStdShiftIntensityd,
@@ -520,6 +523,9 @@ def create_transform_im(
         dict(keys=CommonKeys.IMAGE, global_prob=1, prob=1, common_sampling=True, intensity_range=(13, 15)),
         data,
     )
+    create_transform_im(RandRicianNoise, dict(prob=1.0, mean=1, std=0.5), data)
+    create_transform_im(RandRicianNoised, dict(keys=CommonKeys.IMAGE, prob=1.0, mean=1, std=0.5), data)
+    create_transform_im(SavitzkyGolaySmooth, dict(window_length=5, order=1), data)
     create_transform_im(GibbsNoise, dict(alpha=0.8), data)
     create_transform_im(GibbsNoised, dict(keys=CommonKeys.IMAGE, alpha=0.8), data)
     create_transform_im(RandGibbsNoise, dict(prob=1.0, alpha=(0.6, 0.8)), data)
@@ -640,8 +646,8 @@ def create_transform_im(
     create_transform_im(RandScaleCropd, dict(keys=keys, roi_scale=0.4), data)
     create_transform_im(CenterScaleCrop, dict(roi_scale=0.4), data)
     create_transform_im(CenterScaleCropd, dict(keys=keys, roi_scale=0.4), data)
-    create_transform_im(AsDiscrete, dict(to_onehot=2, threshold=10), data, is_post=True, colorbar=True)
-    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=2, threshold=10), data, is_post=True)
+    create_transform_im(AsDiscrete, dict(to_onehot=None, threshold=10), data, is_post=True, colorbar=True)
+    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=None, threshold=10), data, is_post=True)
     create_transform_im(LabelFilter, dict(applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True)
     create_transform_im(
         LabelFilterd, dict(keys=CommonKeys.LABEL, applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True