diff --git a/pyproject.toml b/pyproject.toml
index 684b373..ab4e244 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,9 +22,13 @@ dependencies = [
   "bioio-tifffile>=1.3",
   "fire>=0.7.1",
   "jinja2>=3.1.6",
+  "numpy>=2.2.6",
   "pandas>=3.0.2",
+  "scipy>=1.17.1",
+  "tqdm>=4.67.3",
   "pandera>=0.31.1",
   "scikit-image>=0.26",
+
 ]
 scripts.ZedProfiler = "ZedProfiler.cli:trigger"
 
diff --git a/src/zedprofiler/featurization/granularity.py b/src/zedprofiler/featurization/granularity.py
index 958ff14..9d79181 100644
--- a/src/zedprofiler/featurization/granularity.py
+++ b/src/zedprofiler/featurization/granularity.py
@@ -1,10 +1,412 @@
-"""Granularity featurization module scaffold."""
+"""
+Calculate the granularity spectrum of a 3D image.
+"""
 
 from __future__ import annotations
 
-from zedprofiler.exceptions import ZedProfilerError
+from typing import Dict, Optional
 
+import numpy
+import scipy.ndimage
+import skimage.morphology
+import tqdm
 
-def compute() -> dict[str, list[float]]:
-    """Placeholder for granularity computation implementation."""
-    raise ZedProfilerError("granularity.compute is not implemented yet")
+from zedprofiler.IO.loading_classes import ObjectLoader
+
+
+def _fix_scipy_ndimage_result(result: float | list | numpy.ndarray) -> numpy.ndarray:
+    """Convert scipy.ndimage aggregation results to a consistent array.
+
+    Equivalent to centrosome.cpmorphology.fixup_scipy_ndimage_result.
+    scipy.ndimage.mean/sum can return a scalar when there's one label,
+    or a list otherwise. This ensures we always get a numpy array.
+
+    Parameters
+    ----------
+    result : scalar, list, or numpy.ndarray
+        Output from scipy.ndimage.mean or similar.
+
+    Returns
+    -------
+    numpy.ndarray
+        1-D array of results.
+    """
+    if numpy.isscalar(result):
+        return numpy.array([result])
+    return numpy.asarray(result)
+
+
+def _subsample_3d(
+    data: numpy.ndarray,
+    new_shape: numpy.ndarray,
+    subsample_factor: float,
+    order: int = 1,
+) -> numpy.ndarray:
+    """Subsample a 3D array using map_coordinates, matching CellProfiler.
+
+    CellProfiler generates coordinates for the new shape and divides by
+    subsample_factor to map back into the original coordinate space.
+    The same scalar factor is used for all three axes.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        3D array to subsample.
+    new_shape : numpy.ndarray
+        Target shape as a float array (coordinate grid extent).
+    subsample_factor : float
+        The factor used to divide coordinates (same for all axes).
+    order : int
+        Interpolation order (1 for linear, 0 for nearest-neighbor).
+
+    Returns
+    -------
+    numpy.ndarray
+        Subsampled array.
+    """
+    if subsample_factor >= 1.0:
+        return data.copy()
+
+    k, i, j = (
+        numpy.mgrid[0 : new_shape[0], 0 : new_shape[1], 0 : new_shape[2]].astype(float)
+        / subsample_factor
+    )
+    return scipy.ndimage.map_coordinates(data, (k, i, j), order=order)
+
+
+def _upsample_3d(
+    data: numpy.ndarray,
+    subsampled_shape: numpy.ndarray,
+    original_shape: tuple,
+) -> numpy.ndarray:
+    """Upsample a 3D array back to original shape using map_coordinates.
+
+    Matches CellProfiler's approach for restoring reconstructed images
+    to the original label resolution.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        Subsampled 3D array to upsample.
+    subsampled_shape : numpy.ndarray
+        Shape of the subsampled space (float array, preserves CellProfiler
+        precision).
+    original_shape : tuple
+        Target shape to upsample to.
+
+    Returns
+    -------
+    numpy.ndarray
+        Upsampled array at original_shape resolution.
+    """
+    k, i, j = numpy.mgrid[
+        0 : original_shape[0], 0 : original_shape[1], 0 : original_shape[2]
+    ].astype(float)
+    k *= float(subsampled_shape[0] - 1) / float(original_shape[0] - 1)
+    i *= float(subsampled_shape[1] - 1) / float(original_shape[1] - 1)
+    j *= float(subsampled_shape[2] - 1) / float(original_shape[2] - 1)
+    return scipy.ndimage.map_coordinates(data, (k, i, j), order=1)
+
+
+def compute_granularity(  # noqa: C901, PLR0912, PLR0913, PLR0915
+    object_loader: ObjectLoader,
+    radius: int = 10,
+    granular_spectrum_length: int = 16,
+    subsample_size: float = 0.25,
+    image_sample_size: float = 0.25,
+    mask_threshold: float = 0.9,
+    verbose: bool = False,
+    image_mask: Optional[numpy.ndarray] = None,
+) -> Dict[str, list]:
+    """Calculate the granularity spectrum of a 3D image.
+
+    Follows the CellProfiler MeasureGranularity algorithm exactly for 3D:
+    1. Subsample the image uniformly (same factor for Z, Y, X).
+    2. Further subsample for background tophat removal.
+    3. Iteratively erode with ball(1) and reconstruct, measuring
+    signal lost at each scale as image-level and per-object values.
+
+    Parameters
+    ----------
+    object_loader : ObjectLoader
+        Loader containing the image and label arrays.
+    radius : int
+        Radius of the structuring element for background removal.
+        Should correspond to texture radius *after* subsampling.
+    granular_spectrum_length : int
+        Number of granularity scales to measure.
+    subsample_size : float
+        Subsampling factor for the image (0, 1]. Applied uniformly to Z/Y/X.
+    image_sample_size : float
+        Subsampling factor for background reduction (0, 1].
+        Applied relative to the already-subsampled image.
+    mask_threshold : float
+        Threshold for converting interpolated masks back to boolean.
+    verbose : bool
+        Print diagnostic information.
+    image_mask : numpy.ndarray or None
+        Boolean mask matching the image shape. Corresponds to CellProfiler's
+        ``im.mask``. If None (default), all pixels are considered valid
+        (all-True mask), matching the typical CellProfiler behavior for
+        unmasked images.
+
+    Returns
+    -------
+    Dict[str, list]
+        Dictionary with keys 'object_id', 'feature', 'value'.
+        Image-level measurements use object_id=0.
+    """
+    # Validate inputs
+    if subsample_size <= 0 or subsample_size > 1:
+        raise ValueError(f"subsample_size must be in (0, 1], got {subsample_size}")
+    if image_sample_size <= 0 or image_sample_size > 1:
+        raise ValueError(
+            f"image_sample_size must be in (0, 1], got {image_sample_size}"
+        )
+    if radius <= 0:
+        raise ValueError(f"radius must be positive, got {radius}")
+    if granular_spectrum_length <= 0:
+        raise ValueError(
+            f"granular_spectrum_length must be positive, got {granular_spectrum_length}"
+        )
+
+    # Get original data
+    original_pixels = object_loader.image
+    original_labels = object_loader.label_image
+    original_shape = original_pixels.shape
+
+    # Mask: CellProfiler uses im.mask (typically all-True for unmasked images)
+    if image_mask is None:
+        original_mask = numpy.ones(original_shape, dtype=bool)
+    else:
+        original_mask = image_mask.astype(bool)
+
+    # ------------------------------------------------------------------
+    # Step 1: Subsample image and mask (uniform factor for all axes)
+    # CellProfiler: new_shape = shape * subsample_size
+    #   coordinates = mgrid[0:new_shape] / subsample_size
+    # ------------------------------------------------------------------
+    new_shape = numpy.array(original_shape, dtype=float)
+
+    if subsample_size < 1.0:
+        new_shape = new_shape * subsample_size
+
+        pixels = _subsample_3d(
+            original_pixels,
+            new_shape,
+            subsample_factor=subsample_size,
+            order=1,
+        )
+        mask = (
+            _subsample_3d(
+                original_mask.astype(float),
+                new_shape,
+                subsample_factor=subsample_size,
+                order=1,
+            )
+            > mask_threshold
+        )
+
+        if verbose:
+            print(
+                f"Subsampled image: {original_shape} -> {pixels.shape} "
+                f"(factor={subsample_size})"
+            )
+    else:
+        pixels = original_pixels.copy()
+        mask = original_mask.copy()
+
+    # ------------------------------------------------------------------
+    # Step 2: Background removal via tophat filter
+    #
+    # CellProfiler 3D BUG (replicated for compatibility):
+    #   The 3D branch uses new_shape for grid bounds and subsample_size
+    #   for coordinate division, instead of back_shape and
+    #   image_sample_size as the 2D branch does. This means:
+    #   - back_pixels has the SAME shape as pixels (not smaller)
+    #   - Many coordinates are out of bounds → map_coordinates returns 0
+    #   We replicate this exactly to match CellProfiler output.
+    # ------------------------------------------------------------------
+    if image_sample_size < 1.0:
+        back_shape = new_shape * image_sample_size
+
+        # CellProfiler 3D: mgrid[0:new_shape] / subsample_size
+        # (NOT mgrid[0:back_shape] / image_sample_size as 2D does)
+        k, i, j = (
+            numpy.mgrid[0 : new_shape[0], 0 : new_shape[1], 0 : new_shape[2]].astype(
+                float
+            )
+            / subsample_size
+        )
+        back_pixels = scipy.ndimage.map_coordinates(pixels, (k, i, j), order=1)
+        back_mask = (
+            scipy.ndimage.map_coordinates(mask.astype(float), (k, i, j))
+            > mask_threshold
+        )
+
+        if verbose:
+            print(
+                f"Background subsampled: pixels {pixels.shape} -> "
+                f"back_pixels {back_pixels.shape} "
+                f"(image_sample_size={image_sample_size})"
+            )
+    else:
+        back_pixels = pixels
+        back_mask = mask
+        back_shape = new_shape
+
+    # Tophat filter: masked erosion + masked dilation
+    footprint_bg = skimage.morphology.ball(radius, dtype=bool)
+
+    back_pixels_masked = numpy.zeros_like(back_pixels)
+    back_pixels_masked[back_mask] = back_pixels[back_mask]
+    back_pixels = skimage.morphology.erosion(back_pixels_masked, footprint=footprint_bg)
+
+    back_pixels_masked = numpy.zeros_like(back_pixels)
+    back_pixels_masked[back_mask] = back_pixels[back_mask]
+    back_pixels = skimage.morphology.dilation(
+        back_pixels_masked, footprint=footprint_bg
+    )
+
+    # Upsample background back to subsampled image size
+    if image_sample_size < 1.0:
+        # CellProfiler 3D: mgrid[0:new_shape] with coords scaled by
+        # (back_shape - 1) / (new_shape - 1)
+        k, i, j = numpy.mgrid[
+            0 : new_shape[0], 0 : new_shape[1], 0 : new_shape[2]
+        ].astype(float)
+        k *= float(back_shape[0] - 1) / float(new_shape[0] - 1)
+        i *= float(back_shape[1] - 1) / float(new_shape[1] - 1)
+        j *= float(back_shape[2] - 1) / float(new_shape[2] - 1)
+        back_pixels = scipy.ndimage.map_coordinates(back_pixels, (k, i, j), order=1)
+
+    # Subtract background
+    pixels = pixels - back_pixels
+    pixels[pixels < 0] = 0
+
+    if verbose:
+        print("Background removed via tophat filter.")
+
+    # ------------------------------------------------------------------
+    # Step 3: Object initialization
+    # CellProfiler computes per-object start_mean from the ORIGINAL image
+    # (im.pixel_data) using the full-resolution label image, with labels
+    # masked by im.mask: labels[~im.mask] = 0.
+    # ------------------------------------------------------------------
+    object_measurements = {
+        "object_id": [],
+        "feature": [],
+        "value": [],
+    }
+
+    label_range = numpy.unique(original_labels[original_labels > 0])
+    nobjects = len(label_range)
+
+    if nobjects > 0:
+        label_range = numpy.arange(1, nobjects + 1)
+
+        # CellProfiler: self.labels[~im.mask] = 0
+        masked_labels = original_labels.copy()
+        masked_labels[~original_mask] = 0
+
+        per_object_current_mean = _fix_scipy_ndimage_result(
+            scipy.ndimage.mean(original_pixels, masked_labels, label_range)
+        )
+        per_object_start_mean = numpy.maximum(
+            per_object_current_mean, numpy.finfo(float).eps
+        )
+    else:
+        label_range = numpy.array([], dtype=int)
+        masked_labels = original_labels
+        per_object_current_mean = numpy.array([])
+        per_object_start_mean = numpy.array([])
+
+    # ------------------------------------------------------------------
+    # Step 4: Granular spectrum loop
+    # CellProfiler computes startmean AFTER background subtraction but
+    # BEFORE zeroing pixels outside mask (zeroing is implicit via indexing).
+    # ------------------------------------------------------------------
+    startmean = numpy.mean(pixels[mask])
+    ero = pixels.copy()
+    # Mask the test image so masked pixels have no effect during reconstruction
+    ero[~mask] = 0
+    currentmean = startmean
+    startmean = max(startmean, numpy.finfo(float).eps)
+
+    # CellProfiler uses ball(1) for the iterative erosion/reconstruction loop
+    footprint = skimage.morphology.ball(1, dtype=bool)
+
+    if verbose:
+        print(
+            f"Image startmean: {startmean:.6f}, "
+            f"Processing {nobjects} objects, "
+            f"Spectrum length: {granular_spectrum_length}"
+        )
+
+    for scale in tqdm.tqdm(
+        range(1, granular_spectrum_length + 1),
+        desc="Granularity measurement",
+        position=1,
+        leave=False,
+    ):
+        prevmean = currentmean
+
+        # Masked erosion
+        ero_masked = numpy.zeros_like(ero)
+        ero_masked[mask] = ero[mask]
+        ero = skimage.morphology.erosion(ero_masked, footprint=footprint)
+
+        # Reconstruction
+        rec = skimage.morphology.reconstruction(ero, pixels, footprint=footprint)
+
+        # Image-level granularity
+        currentmean = numpy.mean(rec[mask])
+        gs = (prevmean - currentmean) * 100 / startmean
+
+        if verbose and scale == 1:
+            print(f"Scale 1 - gs: {gs:.4f}, currentmean: {currentmean:.6f}")
+
+        # ----------------------------------------------------------
+        # Per-object granularity: upsample rec to original shape,
+        # then compute per-label means using masked_labels.
+        # ----------------------------------------------------------
+        if nobjects > 0:
+            if subsample_size < 1.0:
+                rec_full = _upsample_3d(
+                    rec,
+                    subsampled_shape=new_shape,
+                    original_shape=original_shape,
+                )
+            else:
+                rec_full = rec
+
+            # Single-pass per-object mean via scipy.ndimage.mean
+            new_object_means = _fix_scipy_ndimage_result(
+                scipy.ndimage.mean(rec_full, masked_labels, label_range)
+            )
+
+            # Granular spectrum: (prev - new) * 100 / start, per object
+            gss = (
+                (per_object_current_mean - new_object_means)
+                * 100
+                / per_object_start_mean
+            )
+
+            per_object_current_mean = new_object_means
+
+            # Record measurements for each object
+            for idx in range(len(label_range)):
+                object_measurements["object_id"].append(int(label_range[idx]))
+                object_measurements["feature"].append(scale)
+                object_measurements["value"].append(float(gss[idx]))
+
+    if verbose:
+        n_total = len(object_measurements["object_id"])
+        non_zero = sum(1 for v in object_measurements["value"] if v > 0)
+        print(f"Total measurements: {n_total}")
+        print(f"Non-zero measurements: {non_zero}")
+        if non_zero > 0:
+            vals = [v for v in object_measurements["value"] if v > 0]
+            print(f"Mean granularity: {numpy.mean(vals):.2f}")
+
+    return object_measurements
diff --git a/tests/featurization/test_granularity.py b/tests/featurization/test_granularity.py
new file mode 100644
index 0000000..a1d944e
--- /dev/null
+++ b/tests/featurization/test_granularity.py
@@ -0,0 +1,151 @@
+import re
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+from _pytest.capture import CaptureFixture
+
+from zedprofiler.featurization.granularity import (
+    _fix_scipy_ndimage_result,
+    _subsample_3d,
+    _upsample_3d,
+    compute_granularity,
+)
+
+EXPECTED_THREE_SCALES = 3
+EXPECTED_TWO_SCALES = 2
+
+
+def _make_loader(image: np.ndarray, labels: np.ndarray) -> SimpleNamespace:
+    return SimpleNamespace(image=image, label_image=labels)
+
+
+def test_fix_scipy_ndimage_result_handles_scalar_and_sequence() -> None:
+    scalar_result = _fix_scipy_ndimage_result(3.14)
+    sequence_result = _fix_scipy_ndimage_result([1.0, 2.0])
+
+    np.testing.assert_array_equal(scalar_result, np.array([3.14]))
+    np.testing.assert_array_equal(sequence_result, np.array([1.0, 2.0]))
+
+
+def test_subsample_3d_returns_copy_when_factor_is_one() -> None:
+    data = np.arange(4 * 4 * 4, dtype=float).reshape(4, 4, 4)
+    out = _subsample_3d(data, np.array(data.shape, dtype=float), subsample_factor=1.0)
+
+    np.testing.assert_array_equal(out, data)
+    assert out is not data
+
+
+def test_subsample_3d_reduces_shape_for_fractional_factor() -> None:
+    data = np.arange(4 * 4 * 4, dtype=float).reshape(4, 4, 4)
+    new_shape = np.array(data.shape, dtype=float) * 0.5
+
+    out = _subsample_3d(data, new_shape, subsample_factor=0.5)
+
+    assert out.shape == (2, 2, 2)
+
+
+def test_upsample_3d_restores_requested_shape() -> None:
+    data = np.arange(2 * 2 * 2, dtype=float).reshape(2, 2, 2)
+
+    out = _upsample_3d(
+        data=data,
+        subsampled_shape=np.array([2.0, 2.0, 2.0]),
+        original_shape=(4, 4, 4),
+    )
+
+    assert out.shape == (4, 4, 4)
+
+
+@pytest.mark.parametrize(
+    ("kwargs", "error_text"),
+    [
+        ({"subsample_size": 0.0}, "subsample_size must be in (0, 1]"),
+        ({"image_sample_size": 0.0}, "image_sample_size must be in (0, 1]"),
+        ({"radius": 0}, "radius must be positive"),
+        ({"granular_spectrum_length": 0}, "granular_spectrum_length must be positive"),
+    ],
+)
+def test_compute_granularity_validates_inputs(
+    kwargs: dict[str, float | int],
+    error_text: str,
+) -> None:
+    image = np.ones((4, 4, 4), dtype=float)
+    labels = np.ones((4, 4, 4), dtype=int)
+    loader = _make_loader(image, labels)
+
+    with pytest.raises(ValueError, match=re.escape(error_text)):
+        compute_granularity(loader, **kwargs)
+
+
+def test_compute_granularity_returns_empty_measurements_when_no_objects() -> None:
+    image = np.ones((6, 6, 6), dtype=float)
+    labels = np.zeros((6, 6, 6), dtype=int)
+    loader = _make_loader(image, labels)
+
+    result = compute_granularity(
+        loader,
+        radius=1,
+        granular_spectrum_length=2,
+        subsample_size=1.0,
+        image_sample_size=1.0,
+        verbose=False,
+    )
+
+    assert result == {"object_id": [], "feature": [], "value": []}
+
+
+def test_compute_granularity_generates_measurements_for_objects() -> None:
+    image = np.zeros((7, 7, 7), dtype=float)
+    image[2:5, 2:5, 2:5] = 20.0
+    labels = np.zeros((7, 7, 7), dtype=int)
+    labels[2:5, 2:5, 2:5] = 1
+    loader = _make_loader(image, labels)
+
+    result = compute_granularity(
+        loader,
+        radius=1,
+        granular_spectrum_length=3,
+        subsample_size=1.0,
+        image_sample_size=1.0,
+        verbose=False,
+    )
+
+    assert result["object_id"] == [1, 1, 1]
+    assert result["feature"] == [1, 2, 3]
+    # Expected per-scale granularity percentages for the single object
+    assert result["value"] == [100.0, 0.0, 0.0]
+
+
+def test_compute_granularity_with_subsampling_mask_and_verbose(
+    capsys: CaptureFixture[str],
+) -> None:
+    image = np.zeros((8, 8, 8), dtype=float)
+    image[2:6, 2:6, 2:6] = 30.0
+    labels = np.zeros((8, 8, 8), dtype=int)
+    labels[2:6, 2:6, 2:6] = 1
+    mask = np.ones((8, 8, 8), dtype=bool)
+    mask[0, :, :] = False
+    loader = _make_loader(image, labels)
+
+    result = compute_granularity(
+        loader,
+        radius=1,
+        granular_spectrum_length=2,
+        subsample_size=0.75,
+        image_sample_size=0.5,
+        mask_threshold=0.5,
+        verbose=True,
+        image_mask=mask,
+    )
+
+    captured = capsys.readouterr()
+
+    assert "Subsampled image" in captured.out
+    assert "Background removed via tophat filter." in captured.out
+    assert result["object_id"] == [1, 1]
+    assert result["feature"] == [1, 2]
+    # Expected per-scale granularity percentages (floating values)
+    assert result["value"] == pytest.approx(
+        [57.91469603714501, 42.085303962855], rel=1e-12
+    )
diff --git a/uv.lock b/uv.lock
index 64da325..5075075 100644
--- a/uv.lock
+++ b/uv.lock
@@ -693,32 +693,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
 ]
 
-[[package]]
-name = "imagecodecs"
-version = "2026.3.6"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
-]
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+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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 version = "5.14.3"
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     { name = "fire" },
     { name = "jinja2" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14'" },
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     { name = "pandas" },
-    { name = "pandera" },
     { name = "scikit-image" },
+    { name = "scipy" },
+    { name = "tqdm" },
 ]
 
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     { name = "fire", specifier = ">=0.7.1" },
     { name = "jinja2", specifier = ">=3.1.6" },
+    { name = "numpy", specifier = ">=2.2.6" },
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-    { name = "pandera", specifier = ">=0.31.1" },
     { name = "scikit-image", specifier = ">=0.26.0" },
+    { name = "scipy", specifier = ">=1.17.1" },
+    { name = "tqdm", specifier = ">=4.67.3" },
 ]
 
 [package.metadata.requires-dev]