CropForegroundd on ROI with text

Describe the bug

CropForegroundd works very well for mammography images that contain only the breast and background. However, some mammograms also contain foreground text or markers (e.g., laterality labels, acquisition text, scanner annotations). Since these annotations are non-zero pixels, they are detected as foreground by select_fn, causing the computed bounding box to include them and resulting in a larger crop than desired.

To Reproduce

input:

import numpy as np
from monai.transforms import CropForegroundd

transform = CropForegroundd(
    keys=["image"],
    source_key="image",
)

output = transform({"image": input_array})
cropped = output["image"]
cropped = np.transpose(cropped, (1, 2, 0))

Output returns same as input with text label.

Expected behavior

Maybe only return the breast region.

Start at the CropForegroundd transform entry point and reproduce the provided NumPy example with the mammogram containing text or markers. Determine how select_fn produces the foreground bounding box and clarify the intended rule for excluding annotations while retaining the breast region. Done means the behavior is covered by a regression test and the crop no longer expands to include the unwanted text.