diff --git a/noxfile.py b/noxfile.py
index 8f6cae0..025d7c7 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -14,7 +14,7 @@ def tests(session: nox.Session) -> None:
             "pytest-cov",
             "pytest-markdown-docs",
             "six",  # centrosome runtime dep, not declared in its metadata
-            ".",
+            ".[numba]",  # exercise the numba backend + correctness harness in CI
         )
     except Exception:
         session.skip(
diff --git a/pyproject.toml b/pyproject.toml
index a89d1ed..f7b4e31 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,9 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
+numba = [
+    "numba>=0.59",
+]
 test = [
     "pytest>=8.4.2",
     "pytest-cov",
diff --git a/src/cp_measure/_detect.py b/src/cp_measure/_detect.py
new file mode 100644
index 0000000..9dd8f9f
--- /dev/null
+++ b/src/cp_measure/_detect.py
@@ -0,0 +1,14 @@
+"""Backend capability detection.
+
+Detected ONCE at import via ``importlib.util.find_spec`` — availability is
+checked without importing the package or catching ImportErrors. Dispatch reads
+the flag; the resolved backend path is then called directly and unguarded
+(a backend that is flagged present but raises is a real bug and must surface,
+not be papered over by a try/except fallback).
+
+Other backends (jax, ...) add their own flags here as they are wired.
+"""
+
+import importlib.util
+
+HAS_NUMBA: bool = importlib.util.find_spec("numba") is not None
diff --git a/src/cp_measure/bulk.py b/src/cp_measure/bulk.py
index 1f2dc94..60450a8 100644
--- a/src/cp_measure/bulk.py
+++ b/src/cp_measure/bulk.py
@@ -45,6 +45,41 @@
 _3D_FEATURES = ("intensity", "sizeshape", "texture", "granularity")
 
 
+def _numba_registries() -> dict[str, dict[str, Callable]]:
+    """Registries for the 'numba' accelerator.
+
+    Composes the numba implementations (``intensity`` and the ``pearson`` /
+    ``manders_fold`` / ``rwc`` / ``overlap`` colocalization features) with the
+    numpy implementations of every other feature — a single global "numba"
+    selection still yields a full, working feature set, accelerated where a
+    numba backend exists. This is explicit per-function composition, NOT an
+    error-driven fallback.
+
+    Note: ``overlap`` is not in the numpy ``_CORRELATION`` registry, so the numba
+    correlation registry intentionally exposes one feature the numpy one does not
+    (the numba ``overlap`` backend exists and is cheap to surface). Adding
+    ``overlap`` to the numpy ``_CORRELATION`` for symmetry is a separate call.
+    """
+    from cp_measure.core.numba import (
+        get_correlation_manders_fold as _numba_manders_fold,
+        get_correlation_overlap as _numba_overlap,
+        get_correlation_pearson as _numba_pearson,
+        get_correlation_rwc as _numba_rwc,
+        get_intensity as _numba_intensity,
+    )
+
+    return {
+        "core": {**_CORE, "intensity": _numba_intensity},
+        "correlation": {
+            **_CORRELATION,
+            "pearson": _numba_pearson,
+            "manders_fold": _numba_manders_fold,
+            "rwc": _numba_rwc,
+            "overlap": _numba_overlap,
+        },
+    }
+
+
 def _dispatch(name: str) -> dict[str, Callable]:
     from cp_measure import _ACCELERATOR
 
@@ -55,9 +90,14 @@ def _dispatch(name: str) -> dict[str, Callable]:
             f"'jax' accelerator not yet wired for {name} measurements"
         )
     if _ACCELERATOR == "numba":
-        raise NotImplementedError(
-            f"'numba' accelerator not yet wired for {name} measurements"
-        )
+        from cp_measure._detect import HAS_NUMBA
+
+        if not HAS_NUMBA:
+            raise RuntimeError(
+                "accelerator 'numba' selected but numba is not installed; "
+                "you can install it via `pip install cp_measure[numba]`"
+            )
+        return _numba_registries()[name]
     if _ACCELERATOR == "fastest":
         raise NotImplementedError("'fastest' logic not yet implemented")
     raise ValueError(
diff --git a/src/cp_measure/core/numba/__init__.py b/src/cp_measure/core/numba/__init__.py
new file mode 100644
index 0000000..48c00d8
--- /dev/null
+++ b/src/cp_measure/core/numba/__init__.py
@@ -0,0 +1,27 @@
+"""Numba-accelerated backend.
+
+Selected explicitly by import (``from cp_measure.core.numba import get_intensity``)
+or globally via ``cp_measure.set_accelerator("numba")``. Requires the optional
+``numba`` extra; availability is gated by ``cp_measure._detect.HAS_NUMBA``.
+
+This backend accelerates ``intensity`` and the colocalization features
+``pearson``/``manders_fold``/``rwc``/``overlap``; the global "numba" accelerator
+composes them with the numpy implementations of every other feature (see
+``cp_measure.bulk``).
+"""
+
+from cp_measure.core.numba.measurecolocalization import (
+    get_correlation_manders_fold,
+    get_correlation_overlap,
+    get_correlation_pearson,
+    get_correlation_rwc,
+)
+from cp_measure.core.numba.measureobjectintensity import get_intensity
+
+__all__ = [
+    "get_correlation_manders_fold",
+    "get_correlation_overlap",
+    "get_correlation_pearson",
+    "get_correlation_rwc",
+    "get_intensity",
+]
diff --git a/src/cp_measure/core/numba/_colocalization.py b/src/cp_measure/core/numba/_colocalization.py
new file mode 100644
index 0000000..fc896eb
--- /dev/null
+++ b/src/cp_measure/core/numba/_colocalization.py
@@ -0,0 +1,163 @@
+"""Fused per-object colocalization kernel (single-threaded, cached).
+
+One pass-set over the per-object value blocks produced by
+:func:`cp_measure.primitives._segment_numba.flatten_pairs_grouped` yields every
+PR-A colocalization feature — Pearson correlation + slope, Manders M1/M2,
+Overlap + K1/K2, and (optionally) the rank-weighted RWC1/RWC2 — for all objects.
+
+The math is the per-object reduction of
+:mod:`cp_measure.core.measurecolocalization`, where each reference ``_ind`` call
+processes a single object via ``scipy.ndimage`` over an all-ones label
+(``lrange=[1]``, hence the ``[0]`` indexing throughout). Here that becomes a
+plain loop over each object's contiguous block.
+
+Serial by construction: objects are iterated in a single ``for``; no
+``prange``/``nogil``. Parallelism is the job of a future batch layer over images,
+never the kernel.
+"""
+
+import numpy as np
+from numba import njit
+
+
+@njit(cache=True)
+def _dense_rank(vals):
+    """Per-element 0-based dense rank (ties share a rank), plus the max rank.
+
+    Bit-reproduces the reference's ``lexsort`` + unique-diff + ``cumsum``: the
+    rank of an element is the number of DISTINCT smaller values, so it is
+    independent of the tie-break order ``argsort`` happens to pick.
+    """
+    m = vals.shape[0]
+    ranks = np.empty(m, np.int64)
+    order = np.argsort(vals)
+    r = 0
+    ranks[order[0]] = 0
+    for j in range(1, m):
+        if vals[order[j]] != vals[order[j - 1]]:
+            r += 1
+        ranks[order[j]] = r
+    return ranks, r
+
+
+@njit(cache=True)
+def coloc_per_object(g1, g2, offsets, n, thr_frac, compute_rwc):
+    """Per-object colocalization features over grouped value blocks.
+
+    ``g1``/``g2`` hold both channels' intensities with object ``k`` in
+    ``[offsets[k] : offsets[k + 1]]``. ``thr_frac`` is the threshold as a
+    fraction of each object's per-channel max (reference ``thr/100``). The rank
+    sort for RWC is gated by ``compute_rwc`` so the three rank-free features skip
+    it. Returns nine length-``n`` arrays:
+    ``(corr, slope, m1, m2, overlap, k1, k2, rwc1, rwc2)``.
+
+    Threshold-derived features default to ``0.0`` for an object with no pixel
+    above the combined threshold (matching the reference's initialised values);
+    Pearson is threshold-free and is always computed (``NaN`` when undefined).
+    """
+    corr = np.zeros(n)
+    slope = np.zeros(n)
+    m1 = np.zeros(n)
+    m2 = np.zeros(n)
+    overlap = np.zeros(n)
+    k1 = np.zeros(n)
+    k2 = np.zeros(n)
+    rwc1 = np.zeros(n)
+    rwc2 = np.zeros(n)
+
+    for k in range(n):
+        lo = offsets[k]
+        hi = offsets[k + 1]
+        cnt = hi - lo
+        if cnt == 0:
+            corr[k] = np.nan
+            slope[k] = np.nan
+            continue
+
+        # Pass 1: means + per-channel maxima.
+        s1 = 0.0
+        s2 = 0.0
+        mx1 = -np.inf
+        mx2 = -np.inf
+        for i in range(lo, hi):
+            v1 = g1[i]
+            v2 = g2[i]
+            s1 += v1
+            s2 += v2
+            if v1 > mx1:
+                mx1 = v1
+            if v2 > mx2:
+                mx2 = v2
+        mean1 = s1 / cnt
+        mean2 = s2 / cnt
+
+        tff = thr_frac * mx1
+        tss = thr_frac * mx2
+
+        # Per-pixel RWC weights need the object's dense ranks up front.
+        if compute_rwc:
+            r1, r1max = _dense_rank(g1[lo:hi])
+            r2, r2max = _dense_rank(g2[lo:hi])
+            rmax = r1max if r1max > r2max else r2max
+            R = rmax + 1
+
+        # Pass 2: the centred second moments (Pearson r + slope) and the
+        # threshold-gated accumulations are INDEPENDENT given pass 1's means and
+        # maxima, so they share one sweep over the block (each accumulator's
+        # add-order is unchanged -> bit-identical to two separate passes).
+        c11 = 0.0
+        c22 = 0.0
+        c12 = 0.0
+        tot_fi = 0.0
+        tot_si = 0.0
+        sum_fi_c = 0.0
+        sum_si_c = 0.0
+        sum_fi2_c = 0.0
+        sum_si2_c = 0.0
+        sum_fisi_c = 0.0
+        sum_fiw_c = 0.0
+        sum_siw_c = 0.0
+        n_comb = 0
+        for i in range(lo, hi):
+            v1 = g1[i]
+            v2 = g2[i]
+            d1 = v1 - mean1
+            d2 = v2 - mean2
+            c11 += d1 * d1
+            c22 += d2 * d2
+            c12 += d1 * d2
+            above1 = v1 >= tff
+            above2 = v2 >= tss
+            if above1:
+                tot_fi += v1
+            if above2:
+                tot_si += v2
+            if above1 and above2:
+                n_comb += 1
+                sum_fi_c += v1
+                sum_si_c += v2
+                sum_fi2_c += v1 * v1
+                sum_si2_c += v2 * v2
+                sum_fisi_c += v1 * v2
+                if compute_rwc:
+                    j = i - lo
+                    diff = r1[j] - r2[j]
+                    if diff < 0:
+                        diff = -diff
+                    w = (R - diff) / R
+                    sum_fiw_c += v1 * w
+                    sum_siw_c += v2 * w
+        corr[k] = c12 / np.sqrt(c11 * c22)
+        slope[k] = c12 / c11
+
+        if n_comb > 0:
+            m1[k] = sum_fi_c / tot_fi
+            m2[k] = sum_si_c / tot_si
+            overlap[k] = sum_fisi_c / np.sqrt(sum_fi2_c * sum_si2_c)
+            k1[k] = sum_fisi_c / sum_fi2_c
+            k2[k] = sum_fisi_c / sum_si2_c
+            if compute_rwc:
+                rwc1[k] = sum_fiw_c / tot_fi
+                rwc2[k] = sum_siw_c / tot_si
+
+    return corr, slope, m1, m2, overlap, k1, k2, rwc1, rwc2
diff --git a/src/cp_measure/core/numba/measurecolocalization.py b/src/cp_measure/core/numba/measurecolocalization.py
new file mode 100644
index 0000000..17cdeea
--- /dev/null
+++ b/src/cp_measure/core/numba/measurecolocalization.py
@@ -0,0 +1,152 @@
+"""Numba-backed MeasureColocalization (Pearson, Manders, RWC, Overlap).
+
+Drop-in replacements for the like-named functions in
+:mod:`cp_measure.core.measurecolocalization`, producing the identical per-object
+feature dicts. The reference iterates ``labels_to_binmasks`` — materialising an
+``(N, H, W)`` boolean stack — and re-indexes the whole image per object through
+``scipy.ndimage`` (single-label reductions, hence the ``[0]`` everywhere). Here a
+single grouped flatten plus one fused per-object kernel
+(:func:`cp_measure.core.numba._colocalization.coloc_per_object`) replaces all of
+it.
+
+Input is normalised through :func:`cp_measure.primitives.shapes.to_bzyx`, exactly
+like the numba ``intensity``/``zernike`` backends. Colocalization takes a
+``(pixels_1, pixels_2, masks)`` triple, so ``to_bzyx`` is called twice against the
+shared mask and the single ``unwrap`` is reused. Every feature here is a function
+of the per-object value vectors ONLY (no pixel coordinates), so the kernels never
+branch on 2D vs 3D and the ``(1, Y, X)`` vs ``(H, W)`` divergence that affects the
+intensity backend cannot occur.
+
+Pixels are upcast to float64 before reduction. This is strictly more accurate
+than the numpy reference on integer-dtype input — where ``fi*si`` overflows in
+uint8 (Overlap/K can exceed 1) and the Pearson slope's ``lstsq`` runs in float32
+— but means the two backends can differ on genuine integer images. Real (float)
+intensity images are unaffected.
+
+``costes`` lives in a stacked follow-up; it is not part of this module.
+"""
+
+import numpy
+from numpy.typing import NDArray
+
+from cp_measure.core.measurecolocalization import (
+    F_CORRELATION_FORMAT,
+    F_K_FORMAT,
+    F_MANDERS_FORMAT,
+    F_OVERLAP_FORMAT,
+    F_RWC_FORMAT,
+    F_SLOPE_FORMAT,
+)
+from cp_measure.core.numba._colocalization import coloc_per_object
+from cp_measure.primitives._segment_numba import flatten_pairs_grouped
+from cp_measure.primitives.segment import labels_to_offsets
+from cp_measure.primitives.shapes import to_bzyx
+
+
+def _run(masks_zyx, pixels_1_zyx, pixels_2_zyx, thr_frac, compute_rwc):
+    """Flatten one ``(Z, Y, X)`` image triple and run the fused kernel.
+
+    Returns the nine per-object arrays from ``coloc_per_object``, or ``None`` when
+    the image holds no objects (the callers then emit empty feature arrays).
+    """
+    masks = numpy.ascontiguousarray(masks_zyx)
+    if not numpy.issubdtype(masks.dtype, numpy.integer):
+        masks = masks.astype(numpy.intp)
+    lut, n, offsets = labels_to_offsets(masks)
+    if n == 0:
+        return None
+    g1, g2 = flatten_pairs_grouped(
+        masks,
+        numpy.ascontiguousarray(pixels_1_zyx, dtype=numpy.float64),
+        numpy.ascontiguousarray(pixels_2_zyx, dtype=numpy.float64),
+        lut,
+        offsets,
+    )
+    return coloc_per_object(g1, g2, offsets, n, thr_frac, compute_rwc)
+
+
+def _featurize(pixels_1, pixels_2, masks, thr, compute_rwc, build):
+    """Normalise the triple via ``to_bzyx`` and map each image through ``build``.
+
+    ``build(result)`` turns the kernel tuple (or ``None`` for an empty image) into
+    one image's feature dict. ``unwrap`` then collapses a single image to that
+    dict, or returns the list for a batch — matching the numba intensity backend.
+    """
+    frac = thr / 100.0
+    masks_list, pixels_1_list, unwrap = to_bzyx(masks, pixels_1)
+    _, pixels_2_list, _ = to_bzyx(masks, pixels_2)
+    results = [
+        build(_run(m, p1, p2, frac, compute_rwc))
+        for m, p1, p2 in zip(masks_list, pixels_1_list, pixels_2_list)
+    ]
+    return unwrap(results)
+
+
+_EMPTY = numpy.empty(0)
+
+
+def get_correlation_pearson(
+    pixels_1: NDArray[numpy.floating],
+    pixels_2: NDArray[numpy.floating],
+    masks: NDArray[numpy.integer],
+) -> dict[str, NDArray[numpy.floating]]:
+    def build(res):
+        if res is None:
+            return {F_CORRELATION_FORMAT: _EMPTY, F_SLOPE_FORMAT: _EMPTY}
+        corr, slope = res[0], res[1]
+        return {F_CORRELATION_FORMAT: corr, F_SLOPE_FORMAT: slope}
+
+    return _featurize(pixels_1, pixels_2, masks, 15, False, build)
+
+
+def get_correlation_manders_fold(
+    pixels_1: NDArray[numpy.floating],
+    pixels_2: NDArray[numpy.floating],
+    masks: NDArray[numpy.integer],
+    thr: int = 15,
+) -> dict[str, NDArray[numpy.floating]]:
+    def build(res):
+        if res is None:
+            return {f"{F_MANDERS_FORMAT}_1": _EMPTY, f"{F_MANDERS_FORMAT}_2": _EMPTY}
+        m1, m2 = res[2], res[3]
+        return {f"{F_MANDERS_FORMAT}_1": m1, f"{F_MANDERS_FORMAT}_2": m2}
+
+    return _featurize(pixels_1, pixels_2, masks, thr, False, build)
+
+
+def get_correlation_rwc(
+    pixels_1: NDArray[numpy.floating],
+    pixels_2: NDArray[numpy.floating],
+    masks: NDArray[numpy.integer],
+    thr: int = 15,
+) -> dict[str, NDArray[numpy.floating]]:
+    def build(res):
+        if res is None:
+            return {f"{F_RWC_FORMAT}_1": _EMPTY, f"{F_RWC_FORMAT}_2": _EMPTY}
+        rwc1, rwc2 = res[7], res[8]
+        return {f"{F_RWC_FORMAT}_1": rwc1, f"{F_RWC_FORMAT}_2": rwc2}
+
+    return _featurize(pixels_1, pixels_2, masks, thr, True, build)
+
+
+def get_correlation_overlap(
+    pixels_1: NDArray[numpy.floating],
+    pixels_2: NDArray[numpy.floating],
+    masks: NDArray[numpy.integer],
+    thr: int = 15,
+) -> dict[str, NDArray[numpy.floating]]:
+    def build(res):
+        if res is None:
+            return {
+                F_OVERLAP_FORMAT: _EMPTY,
+                f"{F_K_FORMAT}_1": _EMPTY,
+                f"{F_K_FORMAT}_2": _EMPTY,
+            }
+        overlap, k1, k2 = res[4], res[5], res[6]
+        return {
+            F_OVERLAP_FORMAT: overlap,
+            f"{F_K_FORMAT}_1": k1,
+            f"{F_K_FORMAT}_2": k2,
+        }
+
+    return _featurize(pixels_1, pixels_2, masks, thr, False, build)
diff --git a/src/cp_measure/core/numba/measureobjectintensity.py b/src/cp_measure/core/numba/measureobjectintensity.py
new file mode 100644
index 0000000..bd058c5
--- /dev/null
+++ b/src/cp_measure/core/numba/measureobjectintensity.py
@@ -0,0 +1,199 @@
+"""Numba-backed MeasureObjectIntensity.
+
+Drop-in for :func:`cp_measure.core.measureobjectintensity.get_intensity`,
+producing the identical dict of features for 2D and 3D input. The per-label
+reductions — including ``Location_MaxIntensity_*`` — run entirely as fused
+single-pass numba kernels over a flat segment representation
+(:mod:`cp_measure.primitives`). The max position uses a deterministic
+``>=``-last rule that is bit-identical to ``scipy.ndimage.maximum_position`` on
+real (tie-free) data; only exact-value ties can differ (scipy's tie pick is
+quicksort-dependent and not stable across numpy versions).
+"""
+
+import numpy
+import skimage.segmentation
+from numpy.typing import NDArray
+
+from cp_measure.core.measureobjectintensity import (
+    C_LOCATION,
+    INTEGRATED_INTENSITY,
+    INTEGRATED_INTENSITY_EDGE,
+    INTENSITY,
+    LOC_CMI_X,
+    LOC_CMI_Y,
+    LOC_CMI_Z,
+    LOC_MAX_X,
+    LOC_MAX_Y,
+    LOC_MAX_Z,
+    LOWER_QUARTILE_INTENSITY,
+    MAD_INTENSITY,
+    MASS_DISPLACEMENT,
+    MAX_INTENSITY,
+    MAX_INTENSITY_EDGE,
+    MEAN_INTENSITY,
+    MEAN_INTENSITY_EDGE,
+    MEDIAN_INTENSITY,
+    MIN_INTENSITY,
+    MIN_INTENSITY_EDGE,
+    STD_INTENSITY,
+    STD_INTENSITY_EDGE,
+    UPPER_QUARTILE_INTENSITY,
+)
+from cp_measure.primitives.segment import label_to_idx_lut
+from cp_measure.primitives._segment_numba import (
+    flatten_numba,
+    inner_boundary,
+    segment_moments,
+    segment_quantiles,
+    segment_resid_sumsq,
+    segment_stats,
+)
+
+
+def get_intensity(
+    masks: NDArray[numpy.integer],
+    pixels: NDArray[numpy.floating],
+    edge_measurements: bool = True,
+) -> dict[str, NDArray[numpy.floating]]:
+    """masks is a labeled array where 0 are background."""
+    orig_ndim = pixels.ndim
+
+    masked_image = pixels
+    if pixels.ndim == 2:
+        masked_image = pixels.reshape(1, *pixels.shape)
+        if masks.ndim == 2:
+            masks = masks.reshape(1, *masks.shape)
+    elif pixels.ndim == 3 and masks.ndim == 2:  # 3D image, 2D mask
+        masks = masks.reshape(1, *masks.shape)
+
+    lut, nobjects = label_to_idx_lut(masks)
+
+    integrated_intensity = numpy.zeros(nobjects)
+    mean_intensity = numpy.zeros(nobjects)
+    std_intensity = numpy.zeros(nobjects)
+    min_intensity = numpy.zeros(nobjects)
+    max_intensity = numpy.zeros(nobjects)
+    mass_displacement = numpy.zeros(nobjects)
+    lower_quartile_intensity = numpy.zeros(nobjects)
+    median_intensity = numpy.zeros(nobjects)
+    mad_intensity = numpy.zeros(nobjects)
+    upper_quartile_intensity = numpy.zeros(nobjects)
+    cmi_x = numpy.zeros(nobjects)
+    cmi_y = numpy.zeros(nobjects)
+    cmi_z = numpy.zeros(nobjects)
+    max_x = numpy.zeros(nobjects)
+    max_y = numpy.zeros(nobjects)
+    max_z = numpy.zeros(nobjects)
+
+    values, seg0, xc, yc, zc = flatten_numba(
+        numpy.ascontiguousarray(masks),
+        numpy.ascontiguousarray(masked_image),
+        lut,
+    )
+    has_objects = values.size > 0
+
+    if has_objects:
+        (
+            count,
+            sumI,
+            minI,
+            maxI,
+            max_x,
+            max_y,
+            max_z,
+            sx,
+            sy,
+            sz,
+            sxI,
+            syI,
+            szI,
+        ) = segment_moments(values, seg0, xc, yc, zc, nobjects)
+        cnt = count.astype(numpy.float64)
+        with numpy.errstate(invalid="ignore", divide="ignore"):
+            integrated_intensity = sumI
+            mean_intensity = sumI / cnt
+            ss = segment_resid_sumsq(values, seg0, nobjects, mean_intensity)
+            std_intensity = numpy.sqrt(ss / cnt)
+            min_intensity = minI
+            max_intensity = maxI
+
+            cm_x = sx / cnt
+            cm_y = sy / cnt
+            cm_z = sz / cnt
+            cmi_x = sxI / sumI
+            cmi_y = syI / sumI
+            cmi_z = szI / sumI
+            mass_displacement = numpy.sqrt(
+                (cm_x - cmi_x) ** 2 + (cm_y - cmi_y) ** 2 + (cm_z - cmi_z) ** 2
+            )
+
+        (
+            lower_quartile_intensity,
+            median_intensity,
+            upper_quartile_intensity,
+            mad_intensity,
+        ) = segment_quantiles(values, seg0, count, nobjects, 1.0 / orig_ndim)
+
+    if edge_measurements:
+        integrated_intensity_edge = numpy.zeros(nobjects)
+        mean_intensity_edge = numpy.zeros(nobjects)
+        std_intensity_edge = numpy.zeros(nobjects)
+        min_intensity_edge = numpy.zeros(nobjects)
+        max_intensity_edge = numpy.zeros(nobjects)
+
+        # 2D plane (Z==1): numba inner-boundary kernel, bit-identical to skimage
+        # mode="inner" but ~12-27x faster. True 3D keeps skimage (6-neighbourhood).
+        if masks.shape[0] == 1:
+            emask = inner_boundary(numpy.ascontiguousarray(masks[0]))[numpy.newaxis] > 0
+        else:
+            emask = skimage.segmentation.find_boundaries(masks, mode="inner") > 0
+        e_values = masked_image[emask].astype(numpy.float64)
+        e_seg0 = lut[masks[emask]]
+
+        if e_values.size > 0:
+            ecount, esum, emin, emax = segment_stats(e_values, e_seg0, nobjects)
+            edge_obj = ecount > 0
+            ecnt = ecount.astype(numpy.float64)
+            with numpy.errstate(invalid="ignore", divide="ignore"):
+                emean = esum / ecnt
+                ess = segment_resid_sumsq(e_values, e_seg0, nobjects, emean)
+                estd = numpy.sqrt(ess / ecnt)
+            integrated_intensity_edge[edge_obj] = esum[edge_obj]
+            mean_intensity_edge[edge_obj] = emean[edge_obj]
+            std_intensity_edge[edge_obj] = estd[edge_obj]
+            min_intensity_edge[edge_obj] = emin[edge_obj]
+            max_intensity_edge[edge_obj] = emax[edge_obj]
+
+    measurement_names = [
+        (INTENSITY, INTEGRATED_INTENSITY, integrated_intensity),
+        (INTENSITY, MEAN_INTENSITY, mean_intensity),
+        (INTENSITY, STD_INTENSITY, std_intensity),
+        (INTENSITY, MIN_INTENSITY, min_intensity),
+        (INTENSITY, MAX_INTENSITY, max_intensity),
+        (INTENSITY, MASS_DISPLACEMENT, mass_displacement),
+        (INTENSITY, LOWER_QUARTILE_INTENSITY, lower_quartile_intensity),
+        (INTENSITY, MEDIAN_INTENSITY, median_intensity),
+        (INTENSITY, MAD_INTENSITY, mad_intensity),
+        (INTENSITY, UPPER_QUARTILE_INTENSITY, upper_quartile_intensity),
+        (C_LOCATION, LOC_CMI_X, cmi_x),
+        (C_LOCATION, LOC_CMI_Y, cmi_y),
+        (C_LOCATION, LOC_CMI_Z, cmi_z),
+        (C_LOCATION, LOC_MAX_X, max_x),
+        (C_LOCATION, LOC_MAX_Y, max_y),
+        (C_LOCATION, LOC_MAX_Z, max_z),
+    ]
+    if edge_measurements:
+        measurement_names.extend(
+            [
+                (INTENSITY, INTEGRATED_INTENSITY_EDGE, integrated_intensity_edge),
+                (INTENSITY, MEAN_INTENSITY_EDGE, mean_intensity_edge),
+                (INTENSITY, STD_INTENSITY_EDGE, std_intensity_edge),
+                (INTENSITY, MIN_INTENSITY_EDGE, min_intensity_edge),
+                (INTENSITY, MAX_INTENSITY_EDGE, max_intensity_edge),
+            ]
+        )
+
+    return {
+        "{}_{}".format(category, feature_name): measurement
+        for category, feature_name, measurement in measurement_names
+    }
diff --git a/src/cp_measure/primitives/__init__.py b/src/cp_measure/primitives/__init__.py
new file mode 100644
index 0000000..3619525
--- /dev/null
+++ b/src/cp_measure/primitives/__init__.py
@@ -0,0 +1,17 @@
+"""Shared primitive layer.
+
+Backend-agnostic building blocks that the per-backend feature implementations
+(``cp_measure.core`` = numpy, ``cp_measure.core.numba`` = numba, ...) compose.
+
+A labeled image is reduced to a flat *segment* representation (values + 0-based
+segment index + per-axis coordinates) which the segment kernels reduce. All
+spatial structure (2D vs 3D) and any future batch/image axis are encoded in that
+flat segment index, so a single set of segment kernels covers every case without
+a rewrite. The numpy ``label_to_idx_lut`` (in ``segment``) builds the
+label→index lookup; the flattening + reductions themselves live in
+``_segment_numba``.
+
+This is an internal layer with no curated public API: import its building
+blocks directly from the concrete modules (``primitives.segment``,
+``primitives._segment_numba``) rather than re-exporting them here.
+"""
diff --git a/src/cp_measure/primitives/_segment_numba.py b/src/cp_measure/primitives/_segment_numba.py
new file mode 100644
index 0000000..3c884cd
--- /dev/null
+++ b/src/cp_measure/primitives/_segment_numba.py
@@ -0,0 +1,261 @@
+"""Numba segment kernels (single-threaded, cached).
+
+These are the numba implementation of the segment-reduce / segment-quantile
+primitives. They loop over the flat ``(values, seg0, coords)`` arrays produced
+by :mod:`cp_measure.primitives.segment` — no image shape, no batch axis — so one
+kernel set covers 2D, 3D, and (future) batched inputs unchanged.
+
+All kernels are ``@njit(cache=True)`` and serial: no ``prange``/``nogil``.
+Parallelism is the job of the (future) batch layer over images, not the kernel.
+"""
+
+import numpy as np
+from numba import njit
+
+
+@njit(cache=True)
+def flatten_numba(masks, pixels, lut):
+    """Flatten a labeled (Z, Y, X) image to ``(values, seg0, xc, yc, zc)``.
+
+    Two grid scans (count, then fill) replace the numpy ``(masks>0)&isfinite``
+    mask + ``numpy.nonzero`` + fancy-index gathers; coordinates are the loop
+    indices. Background and non-finite pixels are dropped, in C (raster) order.
+    ``masks`` and ``pixels`` must be C-contiguous; ``pixels`` may be any float
+    dtype (kept values are upcast into the float64 ``values`` output).
+    """
+    Z, Y, X = masks.shape
+    M = 0
+    for z in range(Z):
+        for y in range(Y):
+            for x in range(X):
+                if masks[z, y, x] > 0 and np.isfinite(pixels[z, y, x]):
+                    M += 1
+    values = np.empty(M, np.float64)
+    seg0 = np.empty(M, np.int64)
+    xc = np.empty(M, np.float64)
+    yc = np.empty(M, np.float64)
+    zc = np.empty(M, np.float64)
+    i = 0
+    for z in range(Z):
+        for y in range(Y):
+            for x in range(X):
+                L = masks[z, y, x]
+                if L <= 0:
+                    continue
+                v = pixels[z, y, x]
+                if not np.isfinite(v):
+                    continue
+                values[i] = v
+                seg0[i] = lut[L]
+                xc[i] = x
+                yc[i] = y
+                zc[i] = z
+                i += 1
+    return values, seg0, xc, yc, zc
+
+
+@njit(cache=True)
+def segment_moments(values, seg0, xc, yc, zc, n):
+    """One pass over the flat pixels accumulating, per segment:
+
+    count, sum, min, max, the max-pixel coordinates, and the six centroid
+    cross-sums (Sx, Sy, Sz, Sx*I, Sy*I, Sz*I).
+
+    The max position uses ``>=`` so the LAST pixel (in the flat raster order the
+    host builds the arrays in) attaining the maximum wins. On real, continuous
+    data the max is unique, so this is bit-identical to the numpy backend's
+    ``scipy.ndimage.maximum_position``; on exact-value ties it is a deterministic
+    rule rather than scipy's quicksort-dependent (version-unstable) pick.
+    """
+    count = np.zeros(n, np.int64)
+    sumI = np.zeros(n, np.float64)
+    minI = np.full(n, np.inf)
+    maxI = np.full(n, -np.inf)
+    mx = np.zeros(n, np.float64)
+    my = np.zeros(n, np.float64)
+    mz = np.zeros(n, np.float64)
+    sx = np.zeros(n, np.float64)
+    sy = np.zeros(n, np.float64)
+    sz = np.zeros(n, np.float64)
+    sxI = np.zeros(n, np.float64)
+    syI = np.zeros(n, np.float64)
+    szI = np.zeros(n, np.float64)
+    M = values.shape[0]
+    for i in range(M):
+        k = seg0[i]
+        v = values[i]
+        x = xc[i]
+        y = yc[i]
+        z = zc[i]
+        count[k] += 1
+        sumI[k] += v
+        if v < minI[k]:
+            minI[k] = v
+        if v >= maxI[k]:  # >= -> keep LAST max in raster order
+            maxI[k] = v
+            mx[k] = x
+            my[k] = y
+            mz[k] = z
+        sx[k] += x
+        sy[k] += y
+        sz[k] += z
+        sxI[k] += x * v
+        syI[k] += y * v
+        szI[k] += z * v
+    return count, sumI, minI, maxI, mx, my, mz, sx, sy, sz, sxI, syI, szI
+
+
+@njit(cache=True)
+def segment_stats(values, seg0, n):
+    """One pass accumulating per-segment count, sum, min, max only.
+
+    The lightweight reduce for callers (e.g. edge measurements) that need
+    neither the max position nor the centroid cross-sums.
+    """
+    count = np.zeros(n, np.int64)
+    sumI = np.zeros(n, np.float64)
+    minI = np.full(n, np.inf)
+    maxI = np.full(n, -np.inf)
+    M = values.shape[0]
+    for i in range(M):
+        k = seg0[i]
+        v = values[i]
+        count[k] += 1
+        sumI[k] += v
+        if v < minI[k]:
+            minI[k] = v
+        if v > maxI[k]:
+            maxI[k] = v
+    return count, sumI, minI, maxI
+
+
+@njit(cache=True)
+def inner_boundary(masks2d):
+    """Labeled inner boundary of a 2D label plane (0 = not a boundary pixel).
+
+    A foreground pixel is a boundary pixel iff any in-bounds 4-neighbour has a
+    different label; out-of-bounds neighbours are ignored. Bit-identical to
+    ``skimage.segmentation.find_boundaries(mode="inner")`` on a single 2D plane,
+    ~12-27x faster (one pass, no morphology). ``masks2d`` must be C-contiguous.
+    """
+    H, W = masks2d.shape
+    out = np.zeros((H, W), masks2d.dtype)
+    for r in range(H):
+        for c in range(W):
+            L = masks2d[r, c]
+            if L <= 0:
+                continue
+            if (
+                (r > 0 and masks2d[r - 1, c] != L)
+                or (r < H - 1 and masks2d[r + 1, c] != L)
+                or (c > 0 and masks2d[r, c - 1] != L)
+                or (c < W - 1 and masks2d[r, c + 1] != L)
+            ):
+                out[r, c] = L
+    return out
+
+
+@njit(cache=True)
+def segment_resid_sumsq(values, seg0, n, mean):
+    """Second pass: per-segment sum of squared residuals (for population std)."""
+    ss = np.zeros(n, np.float64)
+    M = values.shape[0]
+    for i in range(M):
+        k = seg0[i]
+        d = values[i] - mean[k]
+        ss[k] += d * d
+    return ss
+
+
+@njit(cache=True)
+def _interp(sorted_seg, n, frac):
+    """Linear interpolation at position ``n * frac`` within a sorted segment.
+
+    Matches the reference's ``cumsum(areas)``-offset interpolation: the local
+    position is ``count * frac``; clamp the upper neighbour at the last element.
+    """
+    pos = n * frac
+    lo = int(pos)
+    if lo > n - 1:
+        lo = n - 1
+    hi = lo + 1
+    if hi > n - 1:
+        hi = n - 1
+    f = pos - lo
+    return sorted_seg[lo] * (1.0 - f) + sorted_seg[hi] * f
+
+
+@njit(cache=True)
+def segment_quantiles(values, seg0, counts, n, mad_frac):
+    """Per-segment quartiles/median + MAD via scatter-into-segments + sort.
+
+    Builds CSR offsets from ``counts``, scatters values into one flat buffer,
+    sorts each segment slice in place, and interpolates. MAD reuses the sorted
+    absolute deviations from the median at ``mad_frac`` (= 1 / original ndim).
+    """
+    starts = np.zeros(n, np.int64)
+    acc = 0
+    for k in range(n):
+        starts[k] = acc
+        acc += counts[k]
+    total = acc
+
+    buf = np.empty(total, np.float64)
+    cursor = starts.copy()
+    M = values.shape[0]
+    for i in range(M):
+        k = seg0[i]
+        buf[cursor[k]] = values[i]
+        cursor[k] += 1
+
+    lq = np.zeros(n, np.float64)
+    med = np.zeros(n, np.float64)
+    uq = np.zeros(n, np.float64)
+    mad = np.zeros(n, np.float64)
+    for k in range(n):
+        cnt = counts[k]
+        if cnt == 0:
+            continue
+        s = starts[k]
+        seg = buf[s : s + cnt]
+        seg.sort()
+        lq[k] = _interp(seg, cnt, 0.25)
+        med[k] = _interp(seg, cnt, 0.5)
+        uq[k] = _interp(seg, cnt, 0.75)
+        ad = np.abs(seg - med[k])
+        ad.sort()
+        mad[k] = _interp(ad, cnt, mad_frac)
+    return lq, med, uq, mad
+
+
+@njit(cache=True)
+def flatten_pairs_grouped(masks, pix1, pix2, lut, offsets):
+    """Flatten two co-registered ``(Z, Y, X)`` channels to per-object blocks.
+
+    Returns ``(g1, g2)`` where object ``k`` owns the contiguous slices
+    ``g1[offsets[k] : offsets[k + 1]]`` and ``g2[...]``. Both channels are
+    gathered at every ``masks > 0`` pixel in one raster scan, so ``g1`` and
+    ``g2`` stay aligned. Unlike :func:`flatten_numba`, non-finite pixels are
+    KEPT — this mirrors the colocalization reference's ``pixels[mask]``
+    extraction, which applies no finiteness filter.
+
+    A SINGLE scatter scan: ``offsets`` (the CSR block bounds, length ``n + 1``)
+    is precomputed by :func:`cp_measure.primitives.segment.labels_to_offsets`
+    from a ``bincount``, so the count scan this kernel used to do is gone.
+    ``masks`` must be C-contiguous integer; ``lut`` the ``label -> 0..n-1`` map.
+    """
+    Z, Y, X = masks.shape
+    g1 = np.empty(offsets[-1], np.float64)
+    g2 = np.empty(offsets[-1], np.float64)
+    fill = offsets[:-1].copy()
+    for z in range(Z):
+        for y in range(Y):
+            for x in range(X):
+                L = masks[z, y, x]
+                if L > 0:
+                    k = lut[L]
+                    p = fill[k]
+                    g1[p] = pix1[z, y, x]
+                    g2[p] = pix2[z, y, x]
+                    fill[k] = p + 1
+    return g1, g2
diff --git a/src/cp_measure/primitives/segment.py b/src/cp_measure/primitives/segment.py
new file mode 100644
index 0000000..bd2cc0a
--- /dev/null
+++ b/src/cp_measure/primitives/segment.py
@@ -0,0 +1,66 @@
+"""Host-side segment helpers (numpy, backend-agnostic).
+
+A labeled image is reduced to a flat *segment* form — ``values[M]`` intensities,
+``seg0[M]`` 0-based segment indices, and ``xc/yc/zc[M]`` per-pixel coordinates —
+which the segment kernels then reduce. The flattening itself lives in the numba
+layer (:func:`cp_measure.primitives._segment_numba.flatten_numba`); this module
+holds only the numpy label→index lookup that feeds it.
+"""
+
+import numpy
+import scipy.ndimage
+from numpy.typing import NDArray
+
+
+def label_to_idx_lut(
+    masks: NDArray[numpy.integer],
+) -> tuple[NDArray[numpy.int64], int]:
+    """Build a ``label -> 0..n-1`` lookup over the sorted positive labels.
+
+    Returns ``(lut, n)`` where ``lut[label]`` is the segment index (and ``-1``
+    for absent labels / background) and ``n`` is the object count. Output arrays
+    are indexed by this segment rank, which equals ``label - 1`` when labels are
+    the contiguous ``1..n`` that cp_measure expects.
+
+    Present labels are enumerated with ``scipy.ndimage.find_objects`` (one pass)
+    rather than ``numpy.unique`` (a full-image sort): same ascending label set,
+    identical LUT, ~3-5x faster on large/3D masks.
+    """
+    if not numpy.issubdtype(masks.dtype, numpy.integer):
+        masks = masks.astype(numpy.intp, copy=False)
+    bboxes = scipy.ndimage.find_objects(masks)
+    labels = numpy.array(
+        [i + 1 for i, sl in enumerate(bboxes) if sl is not None], dtype=numpy.int64
+    )
+    n = int(labels.size)
+    max_label = int(labels[-1]) if n else 0
+    lut = numpy.full(max_label + 1, -1, dtype=numpy.int64)
+    lut[labels] = numpy.arange(n, dtype=numpy.int64)
+    return lut, n
+
+
+def labels_to_offsets(
+    masks: NDArray[numpy.integer],
+) -> tuple[NDArray[numpy.int64], int, NDArray[numpy.int64]]:
+    """Build ``(lut, n, offsets)`` from a single ``np.bincount`` pass.
+
+    Like :func:`label_to_idx_lut` but also returns the CSR ``offsets`` (length
+    ``n + 1``) giving each object's pixel block in the grouped flat order, so the
+    grouped flatten can scatter in a SINGLE scan instead of count-then-scatter.
+
+    One ``bincount`` over the raster (~2x faster here than ``find_objects`` plus a
+    separate count scan) gives per-label pixel counts directly; ``cumsum`` of the
+    present-label counts is ``offsets``. ``lut`` and segment ordering (ascending
+    present labels) are identical to :func:`label_to_idx_lut`.
+    """
+    if not numpy.issubdtype(masks.dtype, numpy.integer):
+        masks = masks.astype(numpy.intp, copy=False)
+    counts = numpy.bincount(masks.ravel())
+    present = numpy.nonzero(counts[1:])[0] + 1  # positive labels actually present
+    n = int(present.size)
+    max_label = int(present[-1]) if n else 0
+    lut = numpy.full(max_label + 1, -1, dtype=numpy.int64)
+    lut[present] = numpy.arange(n, dtype=numpy.int64)
+    offsets = numpy.zeros(n + 1, dtype=numpy.int64)
+    offsets[1:] = numpy.cumsum(counts[present])
+    return lut, n, offsets
diff --git a/src/cp_measure/primitives/shapes.py b/src/cp_measure/primitives/shapes.py
new file mode 100644
index 0000000..0c94059
--- /dev/null
+++ b/src/cp_measure/primitives/shapes.py
@@ -0,0 +1,83 @@
+"""Canonical ``(B, Z, Y, X)`` input normalisation (numpy, backend-agnostic).
+
+Every optimised feature works on a batch of volumes. Rather than a single dense
+``(B, Z, Y, X)`` array (which cannot hold ragged, differently-sized images), the
+batch is represented as a **list of B ``(Z, Y, X)`` arrays** — equal-size and
+ragged batches share one code path, and a single image is just ``B == 1``.
+
+Normalisation rules (the only public entry, :func:`to_bzyx`):
+
+================================ ============================ ========
+input                            yields                       batch?
+================================ ============================ ========
+2D ``(H, W)`` ndarray            ``[(1, H, W)]``              no
+3D ``(Z, Y, X)`` ndarray         ``[(Z, Y, X)]`` (one volume) no
+4D ``(B, Z, Y, X)`` ndarray      ``[(Z, Y, X)] * B``          yes
+list/tuple of 2D/3D arrays       one ``(Z, Y, X)`` per item   yes
+================================ ============================ ========
+
+A 3D ndarray is therefore ALWAYS one volume, never a batch — this preserves the
+existing single-volume semantics. To pass a batch of 2D images as an array, use
+``(B, 1, H, W)``; for ragged sizes, pass a list. ``unwrap`` then re-shapes the
+per-image results back to a single dict (single input) or the list (batch).
+
+This is a pure batch normaliser: ``masks`` and ``pixels`` must share the same
+batch/ndim structure. It does NOT broadcast a lower-dimensional mask over a
+volume (e.g. a 2D mask applied to a 3D stack) — per-element ndim handling is the
+caller's job (each backend dispatches its own 2D vs 3D path).
+"""
+
+import numpy
+from numpy.typing import NDArray
+
+
+def _to_zyx(arr: NDArray) -> NDArray:
+    """Promote a single 2D/3D image to ``(Z, Y, X)`` (2D gets a unit Z axis)."""
+    a = numpy.asarray(arr)
+    if a.ndim == 2:
+        return a[numpy.newaxis]
+    if a.ndim == 3:
+        return a
+    raise ValueError(f"expected a 2D or 3D image, got ndim={a.ndim}")
+
+
+def to_bzyx(masks, pixels):
+    """Normalise ``(masks, pixels)`` to the canonical batch-of-volumes form.
+
+    Returns ``(masks_zyx, pixels_zyx, unwrap)`` where ``masks_zyx`` and
+    ``pixels_zyx`` are length-``B`` lists of ``(Z, Y, X)`` arrays (one per image),
+    and ``unwrap(results)`` maps a length-``B`` list of per-image results back to
+    a single result (non-batch input) or the list itself (batch input).
+    """
+    masks_is_seq = isinstance(masks, (list, tuple))
+    pixels_is_seq = isinstance(pixels, (list, tuple))
+    if masks_is_seq != pixels_is_seq:
+        raise ValueError("masks and pixels must both be sequences, or both arrays")
+
+    if masks_is_seq:
+        masks_zyx = [_to_zyx(m) for m in masks]
+        pixels_zyx = [_to_zyx(p) for p in pixels]
+        is_batch = True
+    else:
+        m = numpy.asarray(masks)
+        p = numpy.asarray(pixels)
+        if (m.ndim == 4) != (p.ndim == 4):
+            raise ValueError("masks and pixels must both be 4D for a stacked batch")
+        if m.ndim == 4:
+            masks_zyx = list(m)
+            pixels_zyx = list(p)
+            is_batch = True
+        else:
+            masks_zyx = [_to_zyx(m)]
+            pixels_zyx = [_to_zyx(p)]
+            is_batch = False
+
+    if len(masks_zyx) != len(pixels_zyx):
+        raise ValueError(
+            f"batch size mismatch: {len(masks_zyx)} masks vs {len(pixels_zyx)} images"
+        )
+
+    def unwrap(results):
+        return results if is_batch else results[0]
+
+    return masks_zyx, pixels_zyx, unwrap
diff --git a/test/test_backend_correctness.py b/test/test_backend_correctness.py
new file mode 100644
index 0000000..a5eea1e
--- /dev/null
+++ b/test/test_backend_correctness.py
@@ -0,0 +1,100 @@
+"""Backend correctness harness: numba `intensity` must match the numpy backend.
+
+Compares ``cp_measure.core.numba.get_intensity`` against the reference
+``cp_measure.core.get_intensity`` key-by-key on continuous random pixels (no
+exact ties, so ``Location_MaxIntensity_*`` is unambiguous), for 2D and 3D input
+with edge measurements on and off. Also checks the dispatch wiring: under
+``set_accelerator("numba")`` the core registry composes the numba intensity with
+the numpy implementations of every other feature, and an absent numba backend
+raises rather than silently falling back.
+"""
+
+import numpy as np
+import pytest
+from conftest import (
+    DEPTH_3D,
+    SIZE_2D,
+    SIZE_3D,
+    _stamp_objects_2d,
+    _stamp_objects_3d,
+    get_rng,
+)
+
+import cp_measure
+import cp_measure._detect
+import cp_measure.bulk
+from cp_measure._detect import HAS_NUMBA
+from cp_measure.core.measureobjectintensity import get_intensity as intensity_numpy
+
+requires_numba = pytest.mark.skipif(not HAS_NUMBA, reason="numba not installed")
+
+
+def _mask_pixels_2d():
+    mask = np.zeros((SIZE_2D, SIZE_2D), dtype=np.int32)
+    _stamp_objects_2d(mask, n_objects=3)
+    return mask, get_rng().random((SIZE_2D, SIZE_2D))
+
+
+def _mask_pixels_3d():
+    mask = np.zeros((DEPTH_3D, SIZE_3D, SIZE_3D), dtype=np.int32)
+    _stamp_objects_3d(mask, n_objects=2)
+    return mask, get_rng().random((DEPTH_3D, SIZE_3D, SIZE_3D))
+
+
+def _assert_dicts_match(ref, got):
+    assert set(got) == set(ref), set(got).symmetric_difference(ref)
+    for key in ref:
+        np.testing.assert_allclose(
+            got[key], ref[key], rtol=1e-6, atol=1e-8, err_msg=f"feature {key!r}"
+        )
+
+
+@requires_numba
+@pytest.mark.parametrize("edge", [True, False], ids=["edge", "noedge"])
+@pytest.mark.parametrize("dim", ["2d", "3d"])
+def test_numba_intensity_matches_numpy(dim, edge):
+    from cp_measure.core.numba import get_intensity as intensity_numba
+
+    mask, pixels = _mask_pixels_2d() if dim == "2d" else _mask_pixels_3d()
+    ref = intensity_numpy(mask, pixels, edge_measurements=edge)
+    got = intensity_numba(mask, pixels, edge_measurements=edge)
+    _assert_dicts_match(ref, got)
+
+
+@requires_numba
+def test_set_accelerator_numba_composes_with_numpy():
+    cp_measure.set_accelerator("numba")
+    try:
+        core = cp_measure.bulk.get_core_measurements()
+        assert core["intensity"].__module__ == (
+            "cp_measure.core.numba.measureobjectintensity"
+        )
+        # The colocalization features route to the numba backend too.
+        corr = cp_measure.bulk.get_correlation_measurements()
+        for feature in ("pearson", "manders_fold", "rwc", "overlap"):
+            assert corr[feature].__module__ == (
+                "cp_measure.core.numba.measurecolocalization"
+            ), feature
+        # costes stays on numpy until its follow-up lands.
+        assert corr["costes"].__module__ == "cp_measure.core.measurecolocalization"
+        # Every other feature stays on the numpy backend.
+        assert core["sizeshape"].__module__ == "cp_measure.core.measureobjectsizeshape"
+        assert core["texture"].__module__ == "cp_measure.core.measuretexture"
+    finally:
+        cp_measure.set_accelerator(None)
+
+    restored = cp_measure.bulk.get_core_measurements()
+    assert restored["intensity"].__module__ == "cp_measure.core.measureobjectintensity"
+    # overlap is numba-only; the default registry does not expose it.
+    assert "overlap" not in cp_measure.bulk.get_correlation_measurements()
+
+
+def test_set_accelerator_numba_absent_raises(monkeypatch):
+    """When find_spec reports numba absent, selecting it raises (no silent fallback)."""
+    monkeypatch.setattr(cp_measure._detect, "HAS_NUMBA", False)
+    cp_measure.set_accelerator("numba")
+    try:
+        with pytest.raises(RuntimeError, match="numba is not installed"):
+            cp_measure.bulk.get_core_measurements()
+    finally:
+        cp_measure.set_accelerator(None)
diff --git a/test/test_coloc_backend.py b/test/test_coloc_backend.py
new file mode 100644
index 0000000..6b80b3c
--- /dev/null
+++ b/test/test_coloc_backend.py
@@ -0,0 +1,118 @@
+"""Backend correctness: numba colocalization must match the numpy backend.
+
+Compares the numba ``pearson``/``manders_fold``/``rwc``/``overlap`` against the
+reference ``cp_measure.core.measurecolocalization`` key-by-key. Two value regimes
+run: continuous random floats (generic path) and integer-VALUED floats in a small
+range (forces RWC dense-rank ties). 2D and 3D, single image and batch (4D array +
+ragged list) all run, since every bzyx path must hold.
+
+Pixels stay float64 deliberately. Feeding a genuine integer dtype to the *numpy*
+reference triggers two baseline artifacts the numba backend does not reproduce
+(it upcasts to float64): ``fi*si`` overflows in uint8 (so Overlap/K can exceed 1),
+and the Pearson slope's ``lstsq`` design matrix is uint8 → a float32 result. The
+numba float64 path is strictly more accurate; matching those artifacts is not a
+goal. (costes, the follow-up, will exercise true integer dtypes for its scale.)
+"""
+
+import numpy as np
+import pytest
+from conftest import (
+    DEPTH_3D,
+    SIZE_2D,
+    SIZE_3D,
+    _stamp_objects_2d,
+    _stamp_objects_3d,
+    get_rng,
+)
+
+import cp_measure.core.measurecolocalization as ref
+from cp_measure._detect import HAS_NUMBA
+
+requires_numba = pytest.mark.skipif(not HAS_NUMBA, reason="numba not installed")
+
+FUNCS = ["pearson", "manders_fold", "rwc", "overlap"]
+
+
+def _numba_fn(name):
+    import cp_measure.core.numba.measurecolocalization as nb
+
+    return getattr(nb, f"get_correlation_{name}")
+
+
+def _pixels(shape, regime):
+    rng = get_rng()
+    if regime == "cont":
+        return rng.random(shape), rng.random(shape)
+    # Integer-valued floats in a small range -> many RWC rank ties, no dtype artifact.
+    return (
+        rng.integers(0, 8, shape).astype(np.float64),
+        rng.integers(0, 8, shape).astype(np.float64),
+    )
+
+
+def _data_2d(regime="cont"):
+    masks = np.zeros((SIZE_2D, SIZE_2D), np.int32)
+    _stamp_objects_2d(masks, n_objects=3)
+    p1, p2 = _pixels((SIZE_2D, SIZE_2D), regime)
+    return masks, p1, p2
+
+
+def _data_3d(regime="cont"):
+    masks = np.zeros((DEPTH_3D, SIZE_3D, SIZE_3D), np.int32)
+    _stamp_objects_3d(masks, n_objects=2)
+    p1, p2 = _pixels((DEPTH_3D, SIZE_3D, SIZE_3D), regime)
+    return masks, p1, p2
+
+
+def _assert_match(got, expected):
+    assert set(got) == set(expected), set(got).symmetric_difference(expected)
+    for key in expected:
+        np.testing.assert_allclose(
+            got[key], expected[key], rtol=1e-6, atol=1e-8, err_msg=f"feature {key!r}"
+        )
+
+
+@requires_numba
+@pytest.mark.parametrize("name", FUNCS)
+@pytest.mark.parametrize("regime", ["cont", "ties"])
+def test_matches_numpy_2d(name, regime):
+    masks, p1, p2 = _data_2d(regime)
+    expected = getattr(ref, f"get_correlation_{name}")(p1, p2, masks)
+    got = _numba_fn(name)(p1, p2, masks)
+    _assert_match(got, expected)
+
+
+@requires_numba
+@pytest.mark.parametrize("name", FUNCS)
+def test_matches_numpy_3d(name):
+    masks, p1, p2 = _data_3d(float)
+    expected = getattr(ref, f"get_correlation_{name}")(p1, p2, masks)
+    got = _numba_fn(name)(p1, p2, masks)
+    _assert_match(got, expected)
+
+
+@requires_numba
+@pytest.mark.parametrize("name", FUNCS)
+def test_batch_list_matches_per_image(name):
+    imgs = [_data_2d(float), _data_3d(float)]
+    masks = [m for m, _, _ in imgs]
+    p1 = [a for _, a, _ in imgs]
+    p2 = [b for _, _, b in imgs]
+    got = _numba_fn(name)(p1, p2, masks)
+    assert isinstance(got, list) and len(got) == 2
+    for (m, a, b), per_image in zip(imgs, got):
+        _assert_match(per_image, getattr(ref, f"get_correlation_{name}")(a, b, m))
+
+
+@requires_numba
+@pytest.mark.parametrize("name", FUNCS)
+def test_4d_batch_matches_per_image(name):
+    m0, a0, b0 = _data_2d("cont")
+    m1, a1, b1 = _data_2d("ties")
+    masks = np.stack([m0[np.newaxis], m1[np.newaxis]])  # (2, 1, H, W)
+    p1 = np.stack([a0[np.newaxis], a1[np.newaxis]])
+    p2 = np.stack([b0[np.newaxis], b1[np.newaxis]])
+    got = _numba_fn(name)(p1, p2, masks)
+    assert isinstance(got, list) and len(got) == 2
+    for per_image, (m, a, b) in zip(got, [(m0, a0, b0), (m1, a1, b1)]):
+        _assert_match(per_image, getattr(ref, f"get_correlation_{name}")(a, b, m))
diff --git a/test/test_coloc_kernels.py b/test/test_coloc_kernels.py
new file mode 100644
index 0000000..0239979
--- /dev/null
+++ b/test/test_coloc_kernels.py
@@ -0,0 +1,77 @@
+"""Unit tests for the colocalization numba primitives."""
+
+import numpy as np
+import pytest
+import scipy.stats
+
+from cp_measure._detect import HAS_NUMBA
+
+requires_numba = pytest.mark.skipif(not HAS_NUMBA, reason="numba not installed")
+
+
+@requires_numba
+def test_flatten_pairs_grouped_blocks():
+    from cp_measure.primitives._segment_numba import flatten_pairs_grouped
+    from cp_measure.primitives.segment import labels_to_offsets
+
+    masks = np.array([[0, 1, 1], [2, 2, 0], [2, 0, 1]], np.int64)
+    p1 = np.arange(9, dtype=np.float64).reshape(3, 3)
+    p2 = (np.arange(9, dtype=np.float64) * 10).reshape(3, 3)
+    lut, n, offsets = labels_to_offsets(masks[np.newaxis])
+    g1, g2 = flatten_pairs_grouped(
+        masks[np.newaxis], p1[np.newaxis], p2[np.newaxis], lut, offsets
+    )
+
+    assert n == 2
+    assert list(offsets) == [0, 3, 6]  # label 1 has 3 px, label 2 has 3 px
+    # Each object's block is exactly the (channel-aligned) masked pixels.
+    assert sorted(g1[0:3]) == sorted(p1[masks == 1])
+    assert sorted(g2[3:6]) == sorted(p2[masks == 2])
+    # g1/g2 stay co-registered: g2 == g1 * 10 elementwise everywhere.
+    np.testing.assert_array_equal(g2, g1 * 10)
+
+
+@requires_numba
+def test_flatten_pairs_grouped_keeps_nonfinite():
+    """Reference extracts pixels[mask] with no finiteness filter — match it."""
+    from cp_measure.primitives._segment_numba import flatten_pairs_grouped
+    from cp_measure.primitives.segment import labels_to_offsets
+
+    masks = np.array([[1, 1, 1]], np.int64)
+    p1 = np.array([[1.0, np.nan, 3.0]])
+    p2 = np.array([[1.0, 2.0, np.inf]])
+    lut, n, offsets = labels_to_offsets(masks[np.newaxis])
+    g1, g2 = flatten_pairs_grouped(
+        masks[np.newaxis], p1[np.newaxis], p2[np.newaxis], lut, offsets
+    )
+    assert offsets[-1] == 3  # all three pixels kept
+    assert np.isnan(g1).sum() == 1 and np.isinf(g2).sum() == 1
+
+
+@pytest.mark.parametrize("labels", [[0, 1, 2, 3], [0, 2, 5, 5, 2], [0, 0, 0]])
+def test_labels_to_offsets_agrees_with_lut(labels):
+    """bincount-based (lut, n) == find_objects-based; offsets are the CSR counts."""
+    from cp_measure.primitives.segment import label_to_idx_lut, labels_to_offsets
+
+    masks = np.array(labels, np.int64).reshape(1, 1, -1)
+    lut_ref, n_ref = label_to_idx_lut(masks)
+    lut, n, offsets = labels_to_offsets(masks)
+    assert n == n_ref
+    np.testing.assert_array_equal(lut, lut_ref)
+    assert offsets[0] == 0 and offsets[-1] == int((masks > 0).sum())
+    np.testing.assert_array_equal(np.diff(offsets), np.bincount(lut[masks[masks > 0]]))
+
+
+@requires_numba
+@pytest.mark.parametrize("seed", [0, 1, 2])
+def test_dense_rank_matches_scipy(seed):
+    """0-based dense rank == scipy.stats.rankdata(method='dense') - 1, with ties."""
+    from cp_measure.core.numba._colocalization import _dense_rank
+
+    rng = np.random.default_rng(seed)
+    # Small integer range forces ties.
+    vals = rng.integers(0, 5, size=40).astype(np.float64)
+    ranks, rmax = _dense_rank(vals)
+    expected = scipy.stats.rankdata(vals, method="dense") - 1
+    np.testing.assert_array_equal(ranks, expected)
+    assert rmax == expected.max()
diff --git a/test/test_primitives_shapes.py b/test/test_primitives_shapes.py
new file mode 100644
index 0000000..0584e41
--- /dev/null
+++ b/test/test_primitives_shapes.py
@@ -0,0 +1,80 @@
+"""Tests for the canonical (B,Z,Y,X) input normalisation helper."""
+
+import numpy
+import pytest
+
+from cp_measure.primitives.shapes import to_bzyx
+
+
+def _img(shape, seed):
+    rng = numpy.random.default_rng(seed)
+    return rng.random(shape)
+
+
+def test_2d_is_single_unit_z():
+    m, p = numpy.ones((4, 5), int), _img((4, 5), 0)
+    masks, pixels, unwrap = to_bzyx(m, p)
+    assert len(masks) == len(pixels) == 1
+    assert masks[0].shape == (1, 4, 5)
+    assert pixels[0].shape == (1, 4, 5)
+    # single input -> unwrap returns the lone result, not a list
+    assert unwrap(["only"]) == "only"
+
+
+def test_3d_is_single_volume_not_batch():
+    m, p = numpy.ones((3, 4, 5), int), _img((3, 4, 5), 1)
+    masks, pixels, unwrap = to_bzyx(m, p)
+    assert len(masks) == 1
+    assert masks[0].shape == (3, 4, 5)  # one volume, Z preserved
+    assert unwrap(["d"]) == "d"
+
+
+def test_4d_is_batch():
+    m, p = numpy.ones((2, 3, 4, 5), int), _img((2, 3, 4, 5), 2)
+    masks, pixels, unwrap = to_bzyx(m, p)
+    assert len(masks) == len(pixels) == 2
+    assert all(a.shape == (3, 4, 5) for a in masks)
+    out = unwrap([{"a": 1}, {"a": 2}])
+    assert isinstance(out, list) and len(out) == 2  # batch -> list
+
+
+def test_list_of_2d_is_batch_unit_z():
+    imgs = [_img((4, 5), i) for i in range(3)]
+    masks = [numpy.ones((4, 5), int) for _ in range(3)]
+    m, p, unwrap = to_bzyx(masks, imgs)
+    assert len(m) == 3
+    assert all(a.shape == (1, 4, 5) for a in m)
+    assert isinstance(unwrap([1, 2, 3]), list)
+
+
+def test_list_ragged_sizes_ok():
+    imgs = [_img((4, 5), 0), _img((7, 3), 1)]
+    masks = [numpy.ones((4, 5), int), numpy.ones((7, 3), int)]
+    m, p, _ = to_bzyx(masks, imgs)
+    assert m[0].shape == (1, 4, 5)
+    assert m[1].shape == (1, 7, 3)
+
+
+def test_list_of_3d_volumes_batch():
+    vols = [_img((2, 4, 5), 0), _img((3, 4, 5), 1)]
+    masks = [numpy.ones((2, 4, 5), int), numpy.ones((3, 4, 5), int)]
+    m, _, _ = to_bzyx(masks, vols)
+    assert m[0].shape == (2, 4, 5) and m[1].shape == (3, 4, 5)
+
+
+@pytest.mark.parametrize(
+    "masks, pixels, match",
+    [
+        (
+            [numpy.ones((4, 5), int)],
+            [_img((4, 5), 0), _img((4, 5), 1)],
+            "batch size mismatch",
+        ),
+        ([numpy.ones((4, 5), int)], _img((4, 5), 0), "both be sequences"),
+        (numpy.ones((2, 3, 4, 5), int), _img((3, 4, 5), 0), "both be 4D"),
+        (numpy.ones((5,), int), numpy.ones((5,), float), "2D or 3D"),
+    ],
+)
+def test_to_bzyx_raises(masks, pixels, match):
+    with pytest.raises(ValueError, match=match):
+        to_bzyx(masks, pixels)
diff --git a/uv.lock b/uv.lock
index da478cd..9b66ae0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -537,6 +537,9 @@ benchmark = [
     { name = "tifffile", version = "2025.5.10", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "tifffile", version = "2025.10.16", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
+numba = [
+    { name = "numba" },
+]
 test = [
     { name = "pytest" },
     { name = "pytest-cov" },
@@ -558,6 +561,7 @@ dev = [
 requires-dist = [
     { name = "centrosome", specifier = ">=1.3.3" },
     { name = "mahotas", specifier = ">=1.4.13,<2.0.0" },
+    { name = "numba", marker = "extra == 'numba'", specifier = ">=0.59" },
     { name = "numpy", specifier = ">=1.22.1" },
     { name = "pyarrow", marker = "extra == 'benchmark'", specifier = ">=14.0" },
     { name = "pytest", marker = "extra == 'test'", specifier = ">=8.4.2" },
@@ -567,7 +571,7 @@ requires-dist = [
     { name = "scipy", specifier = ">=1.9.1" },
     { name = "tifffile", marker = "extra == 'benchmark'", specifier = ">=2025.5.10" },
 ]
-provides-extras = ["test", "benchmark"]
+provides-extras = ["numba", "test", "benchmark"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -1324,6 +1328,38 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/aa/47/7d70414bcdbb3bc1f458a8d10558f00bbfdb24e5a11740fc8197e12c3255/librt-0.9.0-cp314-cp314t-win_arm64.whl", hash = "sha256:a4b25c6c25cac5d0d9d6d6da855195b254e0021e513e0249f0e3b444dc6e0e61", size = 50009, upload-time = "2026-04-09T16:06:07.995Z" },
 ]
 
+[[package]]
+name = "llvmlite"
+version = "0.47.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/88/a8952b6d5c21e74cbf158515b779666f692846502623e9e3c39d8e8ba25f/llvmlite-0.47.0.tar.gz", hash = "sha256:62031ce968ec74e95092184d4b0e857e444f8fdff0b8f9213707699570c33ccc", size = 193614, upload-time = "2026-03-31T18:29:53.497Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/f5/a1bde3aa8c43524b0acaf3f72fb3d80a32dd29dbb42d7dc434f84584cdcc/llvmlite-0.47.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:41270b0b1310717f717cf6f2a9c68d3c43bd7905c33f003825aebc361d0d1b17", size = 37232772, upload-time = "2026-03-31T18:28:12.198Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/fb/76d88fc05ee1f9c1a6efe39eb493c4a727e5d1690412469017cd23bcb776/llvmlite-0.47.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f9d118bc1dd7623e0e65ca9ac485ec6dd543c3b77bc9928ddc45ebd34e1e30a7", size = 56275179, upload-time = "2026-03-31T18:28:15.725Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/08/29da7f36217abd56a0c389ef9a18bea47960826e691ced1a36c92c6ce93c/llvmlite-0.47.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9ea5cfb04a6ab5b18e46be72b41b015975ba5980c4ddb41f1975b83e19031063", size = 55128632, upload-time = "2026-03-31T18:28:19.946Z" },
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