diff --git a/skimage/morphology/convex_hull.py b/skimage/morphology/convex_hull.py
index 08ff0e04..d024ca02 100644
--- a/skimage/morphology/convex_hull.py
+++ b/skimage/morphology/convex_hull.py
@@ -1,9 +1,11 @@
-__all__ = ['convex_hull_image']
+__all__ = ['convex_hull_image', 'convex_hull_object']
 
 import numpy as np
 from ._pnpoly import grid_points_inside_poly
 from ._convex_hull import possible_hull
-
+from .selem import square
+from skimage.morphology import label, dilation
+from skimage.util import img_as_ubyte, img_as_bool
 
 def convex_hull_image(image):
     """Compute the convex hull image of a binary image.
@@ -14,21 +16,19 @@ def convex_hull_image(image):
     Parameters
     ----------
     image : ndarray
-        Binary input image.  This array is cast to bool before processing.
+        Binary input image. This array is cast to bool before processing.
 
     Returns
     -------
-    hull : ndarray of uint8
-        Binary image with pixels in convex hull set to 255.
+    hull : ndarray of bool
+        Binary image with pixels in convex hull set to True.
 
     References
     ----------
     .. [1] http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/
-
     """
 
     image = image.astype(bool)
-
     # Here we do an optimisation by choosing only pixels that are
     # the starting or ending pixel of a row or column.  This vastly
     # limits the number of coordinates to examine for the virtual
@@ -38,10 +38,10 @@ def convex_hull_image(image):
 
     # Add a vertex for the middle of each pixel edge
     coords_corners = np.empty((N * 4, 2))
-    for i, (x_offset, y_offset) in enumerate(zip((0, 0, -0.5, 0.5),
+    for i, (x_offset, y_offset) in enumerate(zip((0, 0, -0.5, 0.5), 
                                                  (-0.5, 0.5, 0, 0))):
         coords_corners[i * N:(i + 1) * N] = coords + [x_offset, y_offset]
-
+  
     coords = coords_corners
 
     try:
@@ -64,3 +64,35 @@ def convex_hull_image(image):
     mask = grid_points_inside_poly(image.shape[:2], v)
 
     return mask
+
+def convex_hull_object(image):
+    """Compute the convex hull image of individual objects in a binary image.
+
+    The convex hull is the set of pixels included in the smallest convex
+    polygon that surround all white pixels in the input image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Binary input image.
+
+    Returns
+    -------
+    hull : ndarray of bool
+        Binary image with pixels in convex hull set to True.
+    """
+
+    # Add 1 to the output of label() so as to make the 
+    # background value 0 rather than -1
+    labeled_im = label(image, neighbors=8, background=0) + 1
+    convex_obj = np.zeros(image.shape, dtype=bool)
+    mask = np.zeros(image.shape, dtype=np.uint8)
+    convex_img = np.zeros(image.shape, dtype=bool)
+    
+    for i in range(1, labeled_im.max()+1):
+        mask[:] = i
+        mask = img_as_ubyte(np.logical_not(np.bitwise_xor(labeled_im, mask)))
+        convex_obj = convex_hull_image(mask)
+        convex_img = np.logical_or(convex_img, convex_obj)
+
+    return convex_img