diff --git a/pyproject.toml b/pyproject.toml
index 811b86e..51c029d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,7 +19,9 @@ classifiers = [
 dependencies = [
   "fire>=0.7.1",
   "jinja2>=3.1.6",
+  "matplotlib>=3.10.8",
   "pandas>=3.0.2",
+  "scikit-image>=0.26",
 ]
 scripts.ZedProfiler = "ZedProfiler.cli:trigger"
 
diff --git a/src/zedprofiler/featurization/neighbors.py b/src/zedprofiler/featurization/neighbors.py
index 44dac09..3af3c1b 100644
--- a/src/zedprofiler/featurization/neighbors.py
+++ b/src/zedprofiler/featurization/neighbors.py
@@ -1,10 +1,622 @@
-"""Neighbors featurization module scaffold."""
+"""Neighbors featurization module."""
 
-from __future__ import annotations
+from typing import Dict, Tuple, Union
 
-from zedprofiler.exceptions import ZedProfilerError
+import matplotlib.pyplot as plt
+import numpy
+import pandas
+import skimage.measure
 
+from zedprofiler.IO.loading_classes import ObjectLoader
 
-def compute() -> dict[str, list[float]]:
-    """Placeholder for neighbors computation implementation."""
-    raise ZedProfilerError("neighbors.compute is not implemented yet")
+BBoxCoord = Union[int, float]
+BBox3D = Tuple[BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord, BBoxCoord]
+SMALL_SAMPLE_THRESHOLD = 20
+
+
+def neighbors_expand_box(
+    min_coor: Union[int, float],
+    max_coord: Union[int, float],
+    current_min: Union[int, float],
+    current_max: Union[int, float],
+    expand_by: int,
+) -> Tuple[Union[int, float], Union[int, float]]:
+    """
+    Expand the bounding box of the object by a specified distance in each direction.
+
+    Parameters
+    ----------
+    min_coor : Union[int, float]
+        The global minimum coordinate of the image.
+    max_coord : Union[int, float]
+        The global maximum coordinate of the image.
+    current_min : Union[int, float]
+        The current minimum coordinate of the object.
+    current_max : Union[int, float]
+        The current maximum coordinate of the object.
+    expand_by : int
+        The distance by which to expand the bounding box.
+
+    Returns
+    -------
+    Tuple[Union[int, float], Union[int, float]]
+        The new minimum and maximum coordinates of the bounding box.
+    """
+    if current_min - expand_by < min_coor:
+        current_min = min_coor
+    else:
+        current_min -= expand_by
+    if current_max + expand_by > max_coord:
+        current_max = max_coord
+    else:
+        current_max += expand_by
+    return current_min, current_max
+
+
+# crop the image to the bbox of the mask
+def crop_3D_image(
+    image: numpy.ndarray,
+    bbox: BBox3D,
+) -> numpy.ndarray:
+    """
+    Crop the 3D image to the bounding box of the object.
+
+    Parameters
+    ----------
+    image : numpy.ndarray
+        The 3D image to be cropped.
+    bbox : BBox3D
+        The bounding box of the object in the format (z1, y1, x1, z2, y2, x2).
+
+    Returns
+    -------
+    numpy.ndarray
+        The cropped 3D image.
+    """
+    z1, y1, x1, z2, y2, x2 = bbox
+    return image[z1:z2, y1:y2, x1:x2]
+
+
+def compute_neighbors(
+    object_loader: ObjectLoader,
+    distance_threshold: int = 10,
+    anisotropy_factor: int = 10,
+) -> Dict[str, list]:
+    """
+    This function calculates the number of neighbors for each object in a 3D image.
+
+    Parameters
+    ----------
+    object_loader : ObjectLoader
+        The object loader object that contains the image and label image.
+    distance_threshold : int, optional
+        The distance threshold for counting neighbors, by default 10
+    anisotropy_factor : int, optional
+        The anisotropy factor for the image where the anisotropy factor is the
+        ratio of the pixel size in the z direction to the pixel size in the x
+        and y directions, by default 10
+
+    Returns
+    -------
+    Dict[str, list]
+        A dictionary containing the object ID and the number of neighbors for
+        each object.
+    """
+    label_object = object_loader.label_image
+    labels = object_loader.object_ids
+    # set image global min and max coordinates
+    image_global_min_coord_z = 0
+    image_global_min_coord_y = 0
+    image_global_min_coord_x = 0
+    image_global_max_coord_z = label_object.shape[0]
+    image_global_max_coord_y = label_object.shape[1]
+    image_global_max_coord_x = label_object.shape[2]
+
+    neighbors_out_dict = {
+        "object_id": [],
+        "NeighborsCountAdjacent": [],
+        f"NeighborsCountByDistance-{distance_threshold}": [],
+    }
+    for index, label in enumerate(labels):
+        selected_label_object = label_object.copy()
+        selected_label_object[selected_label_object != label] = 0
+        props_label = skimage.measure.regionprops_table(
+            selected_label_object, properties=["bbox"]
+        )
+        # get the number of neighbors for each object
+        distance_x_y = distance_threshold
+        distance_z = numpy.ceil(distance_threshold / anisotropy_factor).astype(int)
+        # find how many other indexes are within a specified distance of the object
+        # first expand the mask image by a specified distance
+        z_min, y_min, x_min, z_max, y_max, x_max = (
+            props_label["bbox-0"][0],
+            props_label["bbox-1"][0],
+            props_label["bbox-2"][0],
+            props_label["bbox-3"][0],
+            props_label["bbox-4"][0],
+            props_label["bbox-5"][0],
+        )
+        original_bbox = (z_min, y_min, x_min, z_max, y_max, x_max)
+
+        new_z_min, new_z_max = neighbors_expand_box(
+            min_coor=image_global_min_coord_z,
+            max_coord=image_global_max_coord_z,
+            current_min=z_min,
+            current_max=z_max,
+            expand_by=distance_z,
+        )
+        new_y_min, new_y_max = neighbors_expand_box(
+            min_coor=image_global_min_coord_y,
+            max_coord=image_global_max_coord_y,
+            current_min=y_min,
+            current_max=y_max,
+            expand_by=distance_x_y,
+        )
+        new_x_min, new_x_max = neighbors_expand_box(
+            min_coor=image_global_min_coord_x,
+            max_coord=image_global_max_coord_x,
+            current_min=x_min,
+            current_max=x_max,
+            expand_by=distance_x_y,
+        )
+        bbox = (new_z_min, new_y_min, new_x_min, new_z_max, new_y_max, new_x_max)
+        croppped_neighbor_image = crop_3D_image(image=label_object, bbox=bbox)
+        self_cropped_neighbor_image = crop_3D_image(
+            image=label_object, bbox=original_bbox
+        )
+        # find all the unique values in the cropped image of the object of interest
+        # this is the number of neighbors in the cropped image
+        n_neighbors_adjacent = (
+            len(
+                numpy.unique(
+                    self_cropped_neighbor_image[self_cropped_neighbor_image > 0]
+                )
+            )
+            - 1
+        )
+
+        # find all the unique values in the expanded cropped image of the
+        # object of interest
+        # this gives the number of neighbors in a n distance of the object
+        n_neighbors_by_distance = (
+            len(numpy.unique(croppped_neighbor_image[croppped_neighbor_image > 0])) - 1
+        )
+        neighbors_out_dict["object_id"].append(label)
+        neighbors_out_dict["NeighborsCountAdjacent"].append(n_neighbors_adjacent)
+        neighbors_out_dict[f"NeighborsCountByDistance-{distance_threshold}"].append(
+            n_neighbors_by_distance
+        )
+
+    return neighbors_out_dict
+
+
+def get_coordinates(
+    nuclei_mask: numpy.ndarray, object_ids: list | None = None
+) -> pandas.DataFrame:
+    """
+    Extract coordinates from a labeled mask.
+
+    Parameters
+    ----------
+    nuclei_mask : ndarray
+        3D labeled mask where each object has a unique ID
+    object_ids : list
+        List of object IDs to extract
+
+    Returns
+    -------
+    coords : pandas.DataFrame
+        DataFrame with columns: object_id, x, y, z
+    """
+    if object_ids is None:
+        object_ids = []
+    coords = {"object_id": [], "x": [], "y": [], "z": []}
+
+    for obj_id in object_ids:
+        object_mask = nuclei_mask.copy()
+        object_mask[object_mask != obj_id] = 0
+        object_mask[object_mask == obj_id] = 1
+        # Get the centroid of the object
+        z, y, x = numpy.where(object_mask == 1)
+        centroid = (numpy.mean(x), numpy.mean(y), numpy.mean(z))
+        coords["object_id"].append(obj_id)
+        coords["x"].append(centroid[0])
+        coords["y"].append(centroid[1])
+        coords["z"].append(centroid[2])
+
+    return pandas.DataFrame(coords)
+
+
+def calculate_centroid(coords: pandas.DataFrame) -> numpy.ndarray:
+    """Calculate the centroid of cell coordinates."""
+    return numpy.mean(coords, axis=0)
+
+
+def euclidean_distance_from_centroid(
+    coords: numpy.ndarray, centroid: numpy.ndarray
+) -> numpy.ndarray:
+    """Calculate Euclidean distance from centroid for each cell."""
+    coords = numpy.asarray(coords, dtype=float)
+    centroid = numpy.asarray(centroid, dtype=float)
+    return numpy.sqrt(numpy.sum((coords - centroid) ** 2, axis=1))
+
+
+def mahalanobis_distance_from_centroid(
+    coords: numpy.ndarray, centroid: numpy.ndarray, min_cells_threshold: int = 50
+) -> numpy.ndarray:
+    """
+    Calculate Mahalanobis distance from centroid for each cell.
+    This accounts for the covariance structure (shape) of the organoid.
+
+    For small sample sizes (<50 cells), uses regularization or falls back to Euclidean.
+
+    Parameters
+    ----------
+    coords : ndarray
+        Cell coordinates (n_cells, 3)
+    centroid : ndarray
+        Centroid coordinates (3,)
+    min_cells_threshold : int
+        Minimum cells needed for reliable Mahalanobis (default: 50)
+
+    Returns
+    -------
+    distances : ndarray
+        Mahalanobis distances for each cell
+    """
+    coords = numpy.asarray(coords, dtype=float)
+    centroid = numpy.asarray(centroid, dtype=float)
+
+    n_cells = len(coords)
+
+    # For very small samples, use Euclidean distance instead
+    if n_cells < SMALL_SAMPLE_THRESHOLD:
+        print(
+            f"  WARNING: Only {n_cells} cells. Using Euclidean distance "
+            f"instead of Mahalanobis."
+        )
+        return euclidean_distance_from_centroid(coords, centroid)
+
+    # Calculate covariance matrix
+    cov_matrix = numpy.cov(coords.T)
+
+    # For small samples (20-50), use strong regularization
+    if n_cells < min_cells_threshold:
+        # Regularization strength inversely proportional to sample size
+        reg_strength = (min_cells_threshold - n_cells) / min_cells_threshold * 0.1
+        cov_matrix += numpy.eye(3) * reg_strength * numpy.trace(cov_matrix) / 3
+        print(
+            f"  WARNING: Only {n_cells} cells. Using regularized covariance "
+            f"(λ={reg_strength:.3f})"
+        )
+    else:
+        # Standard small regularization for numerical stability
+        cov_matrix += numpy.eye(3) * 1e-6
+
+    # Calculate inverse covariance matrix
+    try:
+        inv_cov = numpy.linalg.inv(cov_matrix)
+    except numpy.linalg.LinAlgError:
+        # Fallback to pseudo-inverse if singular
+        print("  WARNING: Singular covariance matrix. Using pseudo-inverse.")
+        inv_cov = numpy.linalg.pinv(cov_matrix)
+
+    # Calculate Mahalanobis distance for each point
+    distances = numpy.array(
+        [
+            numpy.sqrt((coord - centroid).T @ inv_cov @ (coord - centroid))
+            for coord in coords
+        ]
+    )
+
+    return distances
+
+
+def classify_cells_into_shells(
+    coords: pandas.DataFrame or dict,
+    n_shells: int = 5,
+    method: str = "mahalanobis",
+    min_cells_per_shell: int = 3,
+    centroid: numpy.ndarray = None,
+) -> dict:
+    """
+    Classify cells into radial shells based on distance from centroid.
+
+    Automatically adjusts n_shells for small organoids to ensure meaningful statistics.
+
+    Parameters
+    ----------
+    coords : pandas.DataFrame or dict
+        Cell coordinates with /keys: object_id, x, y, z
+    n_shells : int
+        Number of concentric shells to create (will be adjusted if needed)
+    method : str
+        'euclidean' or 'mahalanobis'
+    min_cells_per_shell : int
+        Minimum average cells per shell (default: 3)
+    centroid : numpy.ndarray, optional
+        Pre-calculated centroid (if None, will be calculated from coords)
+
+    Returns
+    -------
+    results : dict
+        Dictionary containing:
+        - 'ShellAssignments': Shell number for each cell (0 = innermost)
+        - 'DistancesFromCenter': Distance from centroid for each cell
+        - 'DistancesFromExterior': Distance from exterior for each cell
+        - 'NormalizedDistancesFromCenter': Normalized distances (0-1)
+    """
+    # Handle both DataFrame and dict input
+    if isinstance(coords, pandas.DataFrame):
+        object_ids = coords["object_id"].to_numpy()
+        coords_array = coords[["x", "y", "z"]].to_numpy()
+    else:
+        object_ids = numpy.array(coords["object_id"])
+        coords_array = numpy.column_stack([coords["x"], coords["y"], coords["z"]])
+    if len(coords_array) == 0:
+        results = {
+            "object_id": [],
+            "ShellAssignments": [],
+            "DistancesFromCenter": [],
+            "DistancesFromExterior": [],
+            "NormalizedDistancesFromCenter": [],
+            "MaxShellsUsed": [],
+        }
+        centroid = None
+        return results, centroid
+    n_cells = len(coords_array)
+    if centroid is None:
+        centroid = calculate_centroid(coords_array)
+
+    # Adjust number of shells for small organoids
+    max_shells = max(2, n_cells // min_cells_per_shell)
+    if n_shells > max_shells:
+        print(
+            f"  WARNING: {n_cells} cells with {n_shells} shells = "
+            f"{n_cells / n_shells:.1f} cells/shell"
+        )
+        print(f"           Reducing to {max_shells} shells for statistical reliability")
+        n_shells = max_shells
+
+    # Calculate distances based on method
+    if method == "mahalanobis":
+        distances = mahalanobis_distance_from_centroid(coords_array, centroid)
+    else:  # euclidean
+        distances = euclidean_distance_from_centroid(coords_array, centroid)
+
+    # Normalize distances to 0-1 range
+    max_distance = numpy.percentile(
+        distances, 95
+    )  # Use 95 percentile to avoid outliers
+    # max_distance = numpy.max(distances)
+    max_distance = numpy.max(distances)
+    if max_distance == 0:
+        # All cells are at the same location; assign all to shell 0
+        normalized_distances = numpy.zeros_like(distances)
+    else:
+        normalized_distances = distances / max_distance
+
+    # Assign shells (0 = innermost, n_shells-1 = outermost)
+    shell_assignments = numpy.minimum(
+        numpy.floor(normalized_distances * n_shells).astype(int),
+        n_shells - 1,
+    )
+
+    # Calculate distance from exterior (inverse of distance from center)
+    distance_from_exterior = max_distance - distances
+
+    results = {
+        "object_id": object_ids,
+        "ShellAssignments": shell_assignments,
+        "DistancesFromCenter": distances,
+        "DistancesFromExterior": distance_from_exterior,
+        "NormalizedDistancesFromCenter": normalized_distances,
+        "ShellsUsed": n_shells,
+    }
+
+    return results, centroid
+
+
+def create_results_dataframe(results: dict) -> pandas.DataFrame:
+    """
+    Create a pandas DataFrame with all cell information.
+
+    Parameters
+    ----------
+    results : dict
+        Results from classify_cells_into_shells
+
+    Returns
+    -------
+    df : pandas.DataFrame
+        DataFrame with cell information
+    """
+    # Handle both DataFrame and dict input
+    if isinstance(results, dict):
+        df = pandas.DataFrame.from_dict(results)
+    else:
+        raise ValueError(
+            "Input must be a results dictionary from classify_cells_into_shells."
+        )
+
+    return df
+
+
+def visualize_organoid_shells(
+    coords: pandas.DataFrame,
+    classification_results: dict,
+    title: str = "Organoid Shell Classification",
+    centroid: numpy.ndarray = None,
+) -> plt.figure:
+    """
+    Create 3D visualization of organoid with shell coloring.
+
+    Parameters
+    ----------
+    coords : pandas.DataFrame or dict
+        Cell coordinates with columns/keys: object_id, x, y, z
+    classification_results : dict
+        Results from classify_cells_into_shells
+    title : str
+        Plot title
+    """
+    # Handle both DataFrame and dict input
+    if isinstance(coords, pandas.DataFrame):
+        x_coords = coords["x"].to_numpy()
+        y_coords = coords["y"].to_numpy()
+        z_coords = coords["z"].to_numpy()
+    else:
+        x_coords = numpy.array(coords["x"])
+        y_coords = numpy.array(coords["y"])
+        z_coords = numpy.array(coords["z"])
+
+    fig = plt.figure(figsize=(14, 6))
+
+    # 3D scatter plot
+    ax1 = fig.add_subplot(121, projection="3d")
+
+    shell_assignments = classification_results["ShellAssignments"]
+    n_shells = classification_results.get(
+        "ShellsUsed", len(numpy.unique(shell_assignments))
+    )
+
+    # Red to blue color gradient
+    colors = plt.cm.RdYlBu_r(numpy.linspace(0, 1, n_shells))
+
+    for shell in range(n_shells):
+        mask = shell_assignments == shell
+        if numpy.sum(mask) > 0:  # Only plot if shell has cells
+            ax1.scatter(
+                x_coords[mask],
+                y_coords[mask],
+                z_coords[mask],
+                c=[colors[shell]],
+                label=f"Shell {shell + 1} (n={numpy.sum(mask)})",
+                s=50,
+                alpha=0.7,
+                edgecolors="black",
+                linewidths=0.5,
+            )
+
+    if centroid is not None:
+        ax1.scatter(
+            *centroid,
+            c="black",
+            s=200,
+            marker="*",
+            label="Centroid",
+            edgecolors="white",
+            linewidths=2,
+        )
+
+    ax1.set_xlabel("X")
+    ax1.set_ylabel("Y")
+    ax1.set_zlabel("Z")
+    ax1.set_title(title)
+    ax1.legend(loc="upper right", fontsize=8)
+
+    # Shell distribution histogram
+    ax2 = fig.add_subplot(122)
+    shell_counts = [numpy.sum(shell_assignments == i) for i in range(n_shells)]
+    bars = ax2.bar(
+        range(1, n_shells + 1), shell_counts, color=colors, alpha=0.7, edgecolor="black"
+    )
+    ax2.set_xlabel("Shell Number")
+    ax2.set_ylabel("Number of Cells")
+    ax2.set_title("Cell Distribution Across Shells")
+    ax2.set_xticks(range(1, n_shells + 1))
+
+    # Add percentage labels on bars
+    total_cells = len(x_coords)
+    for i, (bar, count) in enumerate(zip(bars, shell_counts)):
+        height = bar.get_height()
+        percentage = (count / total_cells) * 100
+        ax2.text(
+            bar.get_x() + bar.get_width() / 2.0,
+            height,
+            f"{count}\n({percentage:.1f}%)",
+            ha="center",
+            va="bottom",
+            fontsize=9,
+        )
+
+    # Add horizontal line for average
+    avg_per_shell = total_cells / n_shells
+    ax2.axhline(
+        y=avg_per_shell,
+        color="red",
+        linestyle="--",
+        alpha=0.5,
+        label=f"Average ({avg_per_shell:.1f})",
+    )
+    ax2.legend()
+
+    plt.tight_layout()
+    return fig
+
+
+def plot_distance_distributions(
+    classification_results: dict, n_shells: int | None = None
+) -> plt.figure:
+    """
+    Plot distance distributions for each shell.
+
+    Parameters
+    ----------
+    classification_results : dict
+        Results from classify_cells_into_shells
+    n_shells : int, optional
+        Number of shells (will use ShellsUsed from results if not provided)
+    """
+    if n_shells is None:
+        n_shells = classification_results.get(
+            "ShellsUsed",
+            len(numpy.unique(classification_results["ShellAssignments"])),
+        )
+
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+    shell_assignments = classification_results["ShellAssignments"]
+    distances_from_center = classification_results["DistancesFromCenter"]
+    distances_from_exterior = classification_results["DistancesFromExterior"]
+
+    colors = plt.cm.RdYlBu_r(numpy.linspace(0, 1, n_shells))
+
+    # Distance from center
+    for shell in range(n_shells):
+        mask = shell_assignments == shell
+        if numpy.sum(mask) > 0:
+            axes[0].hist(
+                distances_from_center[mask],
+                bins=20,
+                alpha=0.5,
+                color=colors[shell],
+                label=f"Shell {shell + 1}",
+                edgecolor="black",
+            )
+
+    axes[0].set_xlabel("Distance from Center")
+    axes[0].set_ylabel("Number of Cells")
+    axes[0].set_title("Distance from Center Distribution")
+    axes[0].legend()
+
+    # Distance from exterior
+    for shell in range(n_shells):
+        mask = shell_assignments == shell
+        if numpy.sum(mask) > 0:
+            axes[1].hist(
+                distances_from_exterior[mask],
+                bins=20,
+                alpha=0.5,
+                color=colors[shell],
+                label=f"Shell {shell + 1}",
+                edgecolor="black",
+            )
+
+    axes[1].set_xlabel("Distance from Exterior")
+    axes[1].set_ylabel("Number of Cells")
+    axes[1].set_title("Distance from Exterior Distribution")
+    axes[1].legend()
+
+    plt.tight_layout()
+    return fig
diff --git a/tests/featurization/test_neighbors.py b/tests/featurization/test_neighbors.py
new file mode 100644
index 0000000..1dd1c66
--- /dev/null
+++ b/tests/featurization/test_neighbors.py
@@ -0,0 +1,257 @@
+import sys
+import types
+
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+
+from zedprofiler.featurization import neighbors as neighbors_module
+
+matplotlib.use("Agg", force=True)
+
+
+EXPECTED_SHELLS_USED = 2
+EXPECTED_OBJECT_COUNT = 6
+EXPECTED_SECOND_OBJECT_ID = 2
+
+if "image_analysis_3D" not in sys.modules:
+    image_analysis_3D = types.ModuleType("image_analysis_3D")
+    image_analysis_3D.__path__ = []  # type: ignore[attr-defined]
+    sys.modules["image_analysis_3D"] = image_analysis_3D
+
+if "image_analysis_3D.featurization_utils" not in sys.modules:
+    featurization_utils = types.ModuleType("image_analysis_3D.featurization_utils")
+    featurization_utils.__path__ = []  # type: ignore[attr-defined]
+    sys.modules["image_analysis_3D.featurization_utils"] = featurization_utils
+
+if "image_analysis_3D.featurization_utils.loading_classes" not in sys.modules:
+    loading_classes = types.ModuleType(
+        "image_analysis_3D.featurization_utils.loading_classes"
+    )
+
+    class ObjectLoader:
+        pass
+
+    loading_classes.ObjectLoader = ObjectLoader
+    sys.modules["image_analysis_3D.featurization_utils.loading_classes"] = (
+        loading_classes
+    )
+
+
+def test_neighbors_expand_box_clamps_to_global_bounds() -> None:
+    result = neighbors_module.neighbors_expand_box(
+        min_coor=0,
+        max_coord=10,
+        current_min=2,
+        current_max=8,
+        expand_by=3,
+    )
+    assert result == (0, 10)
+
+
+def test_neighbors_expand_box_expands_without_clamping() -> None:
+    result = neighbors_module.neighbors_expand_box(
+        min_coor=0,
+        max_coord=10,
+        current_min=2,
+        current_max=8,
+        expand_by=1,
+    )
+    assert result == (1, 9)
+
+
+def test_crop_3d_image_returns_expected_subvolume() -> None:
+    image = np.arange(3 * 4 * 5).reshape(3, 4, 5)
+    cropped = neighbors_module.crop_3D_image(image=image, bbox=(1, 1, 1, 3, 4, 5))
+    assert cropped.shape == (2, 3, 4)
+    np.testing.assert_array_equal(cropped, image[1:3, 1:4, 1:5])
+
+
+def test_compute_neighbors_counts_adjacent_and_distance_neighbors() -> None:
+    label_image = np.zeros((1, 1, 4), dtype=int)
+    label_image[0, 0, 0] = 1
+    label_image[0, 0, 1] = 2
+    label_image[0, 0, 3] = 3
+
+    object_loader = types.SimpleNamespace(
+        label_image=label_image,
+        object_ids=[1, 2, 3],
+    )
+
+    result = neighbors_module.compute_neighbors(
+        object_loader=object_loader,
+        distance_threshold=1,
+        anisotropy_factor=1,
+    )
+
+    assert result["object_id"] == [1, 2, 3]
+    assert result["NeighborsCountAdjacent"] == [0, 0, 0]
+    assert result["NeighborsCountByDistance-1"] == [1, 1, 0]
+
+
+def test_get_coordinates_returns_centroids_for_selected_objects() -> None:
+    nuclei_mask = np.zeros((2, 2, 2), dtype=int)
+    nuclei_mask[0, 0, 0] = 1
+    nuclei_mask[1, 1, 1] = 2
+
+    coords = neighbors_module.get_coordinates(nuclei_mask, object_ids=[1, 2])
+
+    assert list(coords.columns) == ["object_id", "x", "y", "z"]
+    assert coords.shape == (2, 4)
+    assert coords.loc[coords["object_id"] == 1, ["x", "y", "z"]].iloc[0].tolist() == [
+        0.0,
+        0.0,
+        0.0,
+    ]
+    assert coords.loc[
+        coords["object_id"] == EXPECTED_SECOND_OBJECT_ID, ["x", "y", "z"]
+    ].iloc[0].tolist() == [
+        1.0,
+        1.0,
+        1.0,
+    ]
+
+
+def test_calculate_centroid_uses_column_mean() -> None:
+    coords = np.array([[0.0, 0.0, 0.0], [2.0, 4.0, 6.0]])
+    centroid = neighbors_module.calculate_centroid(coords)
+    np.testing.assert_allclose(centroid, np.array([1.0, 2.0, 3.0]))
+
+
+def test_euclidean_distance_from_centroid_matches_expected_values() -> None:
+    coords = np.array([[1.0, 1.0, 1.0], [4.0, 5.0, 6.0]])
+    centroid = np.array([1.0, 1.0, 1.0])
+
+    distances = neighbors_module.euclidean_distance_from_centroid(coords, centroid)
+
+    np.testing.assert_allclose(distances, np.array([0.0, np.sqrt(50.0)]))
+
+
+def test_mahalanobis_distance_falls_back_to_euclidean_for_small_samples() -> None:
+    coords = np.array([[0.0, 0.0, 0.0], [3.0, 4.0, 0.0]])
+    centroid = np.array([0.0, 0.0, 0.0])
+
+    mahalanobis = neighbors_module.mahalanobis_distance_from_centroid(coords, centroid)
+    euclidean = neighbors_module.euclidean_distance_from_centroid(coords, centroid)
+
+    np.testing.assert_allclose(mahalanobis, euclidean)
+
+
+def test_mahalanobis_distance_uses_pseudo_inverse_for_singular_covariance() -> None:
+    coords = np.zeros((20, 3))
+    centroid = np.zeros(3)
+
+    distances = neighbors_module.mahalanobis_distance_from_centroid(coords, centroid)
+
+    np.testing.assert_allclose(distances, np.zeros(20))
+
+
+def test_classify_cells_into_shells_handles_empty_input() -> None:
+    results, centroid = neighbors_module.classify_cells_into_shells(
+        coords={"object_id": [], "x": [], "y": [], "z": []}
+    )
+
+    assert centroid is None
+    assert results == {
+        "object_id": [],
+        "ShellAssignments": [],
+        "DistancesFromCenter": [],
+        "DistancesFromExterior": [],
+        "NormalizedDistancesFromCenter": [],
+        "MaxShellsUsed": [],
+    }
+
+
+def test_classify_cells_into_shells_adjusts_shell_count_and_returns_results() -> None:
+    coords = pd.DataFrame(
+        {
+            "object_id": [1, 2, 3, 4, 5, 6],
+            "x": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
+            "y": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            "z": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+        }
+    )
+
+    results, centroid = neighbors_module.classify_cells_into_shells(
+        coords=coords,
+        n_shells=5,
+        method="euclidean",
+        min_cells_per_shell=3,
+    )
+
+    assert centroid.shape == (3,)
+    assert results["ShellsUsed"] == EXPECTED_SHELLS_USED
+    assert len(results["object_id"]) == EXPECTED_OBJECT_COUNT
+    assert len(results["ShellAssignments"]) == EXPECTED_OBJECT_COUNT
+    assert len(results["DistancesFromCenter"]) == EXPECTED_OBJECT_COUNT
+    assert len(results["DistancesFromExterior"]) == EXPECTED_OBJECT_COUNT
+    assert len(results["NormalizedDistancesFromCenter"]) == EXPECTED_OBJECT_COUNT
+
+
+def test_create_results_dataframe_builds_dataframe_from_results_dict() -> None:
+    results = {
+        "object_id": np.array([1, 2]),
+        "ShellAssignments": np.array([0, 1]),
+        "DistancesFromCenter": np.array([0.5, 1.5]),
+        "DistancesFromExterior": np.array([1.0, 0.0]),
+        "NormalizedDistancesFromCenter": np.array([0.25, 1.0]),
+        "ShellsUsed": 2,
+    }
+
+    df = neighbors_module.create_results_dataframe(results)
+
+    assert list(df.columns) == [
+        "object_id",
+        "ShellAssignments",
+        "DistancesFromCenter",
+        "DistancesFromExterior",
+        "NormalizedDistancesFromCenter",
+        "ShellsUsed",
+    ]
+    assert df.shape == (2, 6)
+
+
+def test_create_results_dataframe_rejects_non_dict_input() -> None:
+    with pytest.raises(ValueError, match="Input must be a results dictionary"):
+        neighbors_module.create_results_dataframe([1, 2, 3])
+
+
+def test_visualize_organoid_shells_returns_figure() -> None:
+    coords = pd.DataFrame(
+        {
+            "object_id": [1, 2, 3],
+            "x": [0.0, 1.0, 2.0],
+            "y": [0.0, 1.0, 2.0],
+            "z": [0.0, 1.0, 2.0],
+        }
+    )
+    classification_results = {
+        "ShellAssignments": np.array([0, 1, 1]),
+        "ShellsUsed": 2,
+    }
+
+    fig = neighbors_module.visualize_organoid_shells(
+        coords=coords,
+        classification_results=classification_results,
+        centroid=np.array([1.0, 1.0, 1.0]),
+    )
+
+    expected_axes = 2
+    assert len(fig.axes) == expected_axes
+    fig.canvas.draw()
+
+
+def test_plot_distance_distributions_returns_figure() -> None:
+    classification_results = {
+        "ShellAssignments": np.array([0, 0, 1, 1]),
+        "DistancesFromCenter": np.array([0.1, 0.2, 0.8, 0.9]),
+        "DistancesFromExterior": np.array([0.9, 0.8, 0.2, 0.1]),
+        "ShellsUsed": 2,
+    }
+
+    fig = neighbors_module.plot_distance_distributions(classification_results)
+
+    expected_axes = 2
+    assert len(fig.axes) == expected_axes
+    fig.canvas.draw()
diff --git a/uv.lock b/uv.lock
index e5ad51b..c44879a 100644
--- a/uv.lock
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 ]
 
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 version = "1.8.3"
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+version = "2026.4.11"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
+]
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+
 [[package]]
 name = "tinycss2"
 version = "1.4.0"
@@ -2023,7 +2454,9 @@ source = { editable = "." }
 dependencies = [
     { name = "fire" },
     { name = "jinja2" },
+    { name = "matplotlib" },
     { name = "pandas" },
+    { name = "scikit-image" },
 ]
 
 [package.dev-dependencies]
@@ -2050,20 +2483,22 @@ notebooks = [
 requires-dist = [
     { name = "fire", specifier = ">=0.7.1" },
     { name = "jinja2", specifier = ">=3.1.6" },
+    { name = "matplotlib", specifier = ">=3.10.8" },
     { name = "pandas", specifier = ">=3.0.2" },
+    { name = "scikit-image", specifier = ">=0.26.0" },
 ]
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "poethepoet", specifier = ">=0.44.0" },
+    { name = "poethepoet", specifier = ">=0.44" },
     { name = "pytest", specifier = ">=9.0.3" },
     { name = "pytest-cov", specifier = ">=5" },
     { name = "ruff", specifier = ">=0.15.10" },
 ]
 docs = [
-    { name = "myst-nb", specifier = ">=1.4.0" },
-    { name = "pydata-sphinx-theme", specifier = ">=0.17.0" },
-    { name = "sphinx", specifier = ">=9.1.0" },
+    { name = "myst-nb", specifier = ">=1.4" },
+    { name = "pydata-sphinx-theme", specifier = ">=0.17" },
+    { name = "sphinx", specifier = ">=9.1" },
 ]
 notebooks = [
     { name = "black", specifier = ">=26.3.1" },