Repository: Project-MONAI/MONAI

Issue:
Missing example images for transform `SavitzkyGolaySmooth` and `RandRicianNoise`
**Is your feature request related to a problem? Please describe.**
I checked the document of intensity transforms, found that we didn't add example images for transform `SavitzkyGolaySmooth` and `RandRicianNoise`:
https://github.com/Project-MONAI/MONAI/blob/dev/docs/source/transforms.rst#RandRicianNoise
https://github.com/Project-MONAI/MONAI/blob/dev/docs/source/transforms.rst#SavitzkyGolaySmooth

Would be nice to add them.



Discussion:

Resolving patch:
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
--- 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
--- 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


Test patch: