diff --git a/skimage/feature/__init__.py b/skimage/feature/__init__.py
index 6a933056..c46fde01 100644
--- a/skimage/feature/__init__.py
+++ b/skimage/feature/__init__.py
@@ -26,7 +26,7 @@ __all__ = ['daisy',
            'structure_tensor',
            'structure_tensor_eigvals',
            'hessian_matrix',
-           'hessian_matrx_det',
+           'hessian_matrix_det',
            'hessian_matrix_eigvals',
            'corner_kitchen_rosenfeld',
            'corner_harris',
@@ -45,5 +45,4 @@ __all__ = ['daisy',
            'plot_matches',
            'blob_dog',
            'blob_doh',
-           'blob_log',
-           'hessian_matrix_det']
+           'blob_log']
diff --git a/skimage/feature/_hessian_det_appx.pyx b/skimage/feature/_hessian_det_appx.pyx
index e66c0d53..7f5a403e 100644
--- a/skimage/feature/_hessian_det_appx.pyx
+++ b/skimage/feature/_hessian_det_appx.pyx
@@ -4,8 +4,6 @@
 # cython: wraparound=False
 import numpy as np
 cimport numpy as cnp
-from skimage.transform import integral_image, integrate
-from skimage import util
 
 
 cdef inline Py_ssize_t _clip(Py_ssize_t x, Py_ssize_t low, Py_ssize_t high):
@@ -78,7 +76,7 @@ cdef inline cnp.double_t _integ(
     return ans
 
 
-def _hessian_matrix_det(cnp.double_t[:, ::1] img, float sigma):
+def _hessian_matrix_det(cnp.double_t[:, ::1] img, double sigma):
     """Computes the approximate Hessian Determinant over an image.
 
     This method uses box filters over integral images to compute the
diff --git a/skimage/feature/corner.py b/skimage/feature/corner.py
index 1889d799..e1dd519b 100644
--- a/skimage/feature/corner.py
+++ b/skimage/feature/corner.py
@@ -31,7 +31,7 @@ def _compute_derivatives(image, mode='constant', cval=0):
     imy : ndarray
         Derivative in y-direction.
 
-v    """
+    """
 
     imy = ndimage.sobel(image, axis=0, mode=mode, cval=cval)
     imx = ndimage.sobel(image, axis=1, mode=mode, cval=cval)
@@ -172,7 +172,7 @@ def hessian_matrix(image, sigma=1, mode='constant', cval=0):
     return Hxx, Hxy, Hyy
 
 
-def hessian_matrix_det(image, sigma, integral=True):
+def hessian_matrix_det(image, sigma):
     """Computes the approximate Hessian Determinant over an image.
 
     This method uses box filters over integral images to compute the
@@ -184,11 +184,7 @@ def hessian_matrix_det(image, sigma, integral=True):
         The image over which to compute Hessian Determinant.
     sigma : float
         Standard deviation used for the Gaussian kernel, used for the Hessian
-        matrix
-    integral : bool
-        If `False`, `image` is assumed to be integral and intergral image is
-        not computed. If `True` the integral image is computed for `image`
-        and used for finding the Hessian Determinant.
+        matrix.
 
     Returns
     -------
@@ -211,9 +207,7 @@ def hessian_matrix_det(image, sigma, integral=True):
     """
 
     image = img_as_float(image)
-    if(integral):
-        image = integral_image(image)
-
+    image = integral_image(image)
     return np.array(_hessian_matrix_det(image, sigma))
 
 
diff --git a/skimage/feature/tests/test_corner.py b/skimage/feature/tests/test_corner.py
index dd9f7703..3d59a495 100644
--- a/skimage/feature/tests/test_corner.py
+++ b/skimage/feature/tests/test_corner.py
@@ -93,9 +93,12 @@ def test_hessian_matrix_eigvals():
 
 
 def test_hessian_matrix_det():
-    image = np.ones((5, 5))
-    det = hessian_matrix_det(image, 3, False)
-    assert_array_equal(det, 0)
+    image = np.zeros((5, 5))
+    image[2, 2] = 1
+    det = hessian_matrix_det(image, 5)
+    
+
+    assert_almost_equal(det, 0, decimal = 3)
 
 
 def test_square_image():