--- a
+++ b/ants/label/label_image_centroids.py
@@ -0,0 +1,94 @@
+
+
+__all__ = ['label_image_centroids']
+
+import numpy as np
+
+from ants.decorators import image_method
+
+
+@image_method
+def label_image_centroids(image, physical=False, convex=True, verbose=False):
+    """
+    Converts a label image to coordinates summarizing their positions
+
+    ANTsR function: `labelImageCentroids`
+
+    Arguments
+    ---------
+    image : ANTsImage
+        image of integer labels
+
+    physical : boolean
+        whether you want physical space coordinates or not
+
+    convex : boolean
+        if True, return centroid
+        if False return point with min average distance to other points with same label
+
+    Returns
+    -------
+    dictionary w/ following key-value pairs:
+        `labels` : 1D-ndarray
+            array of label values
+
+        `vertices` : pd.DataFrame
+            coordinates of label centroids
+
+    Example
+    -------
+    >>> import ants
+    >>> import numpy as np
+    >>> image = ants.from_numpy(np.asarray([[[0,2],[1,3]],[[4,6],[5,7]]]).astype('float32'))
+    >>> labels = ants.label_image_centroids(image)
+    """
+    d = image.shape
+    if len(d) != 3:
+        raise ValueError('image must be 3 dimensions')
+
+    xcoords = np.asarray(np.arange(d[0]).tolist()*(d[1]*d[2]))
+    ycoords = np.asarray(np.repeat(np.arange(d[1]),d[0]).tolist()*d[2])
+    zcoords = np.asarray(np.repeat(np.arange(d[1]), d[0]*d[2]))
+
+    labels = image.numpy()
+    mylabels = np.sort(np.unique(labels[labels > 0])).astype('int')
+    n_labels = len(mylabels)
+    xc = np.zeros(n_labels)
+    yc = np.zeros(n_labels)
+    zc = np.zeros(n_labels)
+
+    if convex:
+        for lab_idx, label_intensity in enumerate(mylabels):
+            idx = (labels == label_intensity).flatten()
+            xc[lab_idx] = np.mean(xcoords[idx])
+            yc[lab_idx] = np.mean(ycoords[idx])
+            zc[lab_idx] = np.mean(zcoords[idx])
+    else:
+        for lab_idx, label_intensity in enumerate(mylabels):
+            idx = (labels == label_intensity).flatten()
+            xci = xcoords[idx]
+            yci = ycoords[idx]
+            zci = zcoords[idx]
+            dist = np.zeros(len(xci))
+
+            for j in range(len(xci)):
+                dist[j] = np.mean(np.sqrt((xci[j] - xci)**2 + (yci[j] - yci)**2 + (zci[j] - zci)**2))
+
+            mid = np.where(dist==np.min(dist))
+            xc[lab_idx] = xci[mid]
+            yc[lab_idx] = yci[mid]
+            zc[lab_idx] = zci[mid]
+
+    centroids = np.vstack([xc,yc,zc]).T
+
+    #if physical:
+    #    centroids = ants.transform_index_to_physical_point(image, centroids)
+
+    return {
+        'labels': mylabels,
+        'vertices': centroids
+    }
+
+
+
+