diff --git a/monai/data/dataset_summary.py b/monai/data/dataset_summary.py
index 5b9e32afca..93d7d7043e 100644
--- a/monai/data/dataset_summary.py
+++ b/monai/data/dataset_summary.py
@@ -159,6 +159,8 @@ def calculate_statistics(self, foreground_threshold: int = 0):
             label, *_ = convert_data_type(data=label, output_type=torch.Tensor)
 
             image_foreground = image[torch.where(label > foreground_threshold)]
+            if image_foreground.numel() == 0:
+                continue
 
             voxel_max.append(image_foreground.max().item())
             voxel_min.append(image_foreground.min().item())
@@ -166,6 +168,11 @@ def calculate_statistics(self, foreground_threshold: int = 0):
             voxel_sum += image_foreground.sum()
             voxel_square_sum += torch.square(image_foreground).sum()
 
+        if voxel_ct == 0:
+            raise ValueError(
+                f"no foreground voxels found in any sample with foreground_threshold={foreground_threshold}; "
+                "set foreground_threshold=-1 to compute statistics over whole images."
+            )
         self.data_max, self.data_min = max(voxel_max), min(voxel_min)
         self.data_mean = (voxel_sum / voxel_ct).item()
         self.data_std = (torch.sqrt(voxel_square_sum / voxel_ct - self.data_mean**2)).item()
@@ -204,11 +211,17 @@ def calculate_percentiles(
             label, *_ = convert_data_type(data=label, output_type=torch.Tensor)
 
             intensities = image[torch.where(label > foreground_threshold)].tolist()
-            if sampling_flag:
-                intensities = intensities[::interval]
-            all_intensities.append(intensities)
+            if intensities:
+                if sampling_flag:
+                    intensities = intensities[::interval]
+                all_intensities.append(intensities)
 
         all_intensities = list(chain(*all_intensities))
+        if not all_intensities:
+            raise ValueError(
+                f"no foreground voxels found in any sample with foreground_threshold={foreground_threshold}; "
+                "set foreground_threshold=-1 to compute statistics over whole images."
+            )
         self.data_min_percentile, self.data_max_percentile = np.percentile(
             all_intensities, [min_percentile, max_percentile]
         )
diff --git a/tests/data/test_dataset_summary.py b/tests/data/test_dataset_summary.py
index 21cc53de90..0735ae0a79 100644
--- a/tests/data/test_dataset_summary.py
+++ b/tests/data/test_dataset_summary.py
@@ -18,6 +18,7 @@
 
 import nibabel as nib
 import numpy as np
+import torch
 
 from monai.data import Dataset, DatasetSummary, create_test_image_3d
 from monai.transforms import LoadImaged
@@ -99,6 +100,35 @@ def test_anisotropic_spacing(self):
             target_spacing = calculator.get_target_spacing(anisotropic_threshold=4.0, percentile=20.0)
             np.testing.assert_allclose(target_spacing, (1.0, 1.0, 1.8))
 
+    def test_mixed_foreground_and_background(self):
+        data = [
+            {"image": torch.rand(1, 4, 4), "label": torch.ones(1, 4, 4)},
+            {"image": torch.rand(1, 4, 4), "label": torch.zeros(1, 4, 4)},
+        ]
+        image = torch.cat([d["image"] for d in data])
+        label = torch.cat([d["label"] for d in data])
+        expected = image[torch.where(label > 0)]
+
+        calculator = DatasetSummary(data, num_workers=0)
+        calculator.calculate_statistics()
+        np.testing.assert_allclose(calculator.data_mean, expected.mean().item(), rtol=1e-5, atol=1e-5)
+        np.testing.assert_allclose(calculator.data_std, expected.std(correction=0).item(), rtol=1e-5, atol=1e-5)
+        np.testing.assert_allclose(calculator.data_max, expected.max().item(), rtol=1e-5, atol=1e-5)
+        np.testing.assert_allclose(calculator.data_min, expected.min().item(), rtol=1e-5, atol=1e-5)
+
+        calculator.calculate_percentiles(sampling_flag=False)
+        np.testing.assert_allclose(calculator.data_min_percentile, np.percentile(expected, 0.5), rtol=1e-5, atol=1e-5)
+        np.testing.assert_allclose(calculator.data_max_percentile, np.percentile(expected, 99.5), rtol=1e-5, atol=1e-5)
+        np.testing.assert_allclose(calculator.data_median, np.median(expected), rtol=1e-5, atol=1e-5)
+
+    def test_all_background(self):
+        data = [{"image": torch.rand(1, 4, 4), "label": torch.zeros(1, 4, 4)}]
+        calculator = DatasetSummary(data, num_workers=0)
+        with self.assertRaisesRegex(ValueError, "foreground_threshold"):
+            calculator.calculate_statistics()
+        with self.assertRaisesRegex(ValueError, "foreground_threshold"):
+            calculator.calculate_percentiles()
+
 
 if __name__ == "__main__":
     unittest.main()