diff --git a/skimage/feature/_template.pyx b/skimage/feature/_template.pyx
index 58d48524..938e3a18 100644
--- a/skimage/feature/_template.pyx
+++ b/skimage/feature/_template.pyx
@@ -42,12 +42,17 @@ from skimage._shared.transform cimport integrate
 @cython.boundscheck(False)
 def match_template(np.ndarray[float, ndim=2, mode="c"] image,
                    np.ndarray[float, ndim=2, mode="c"] template):
+
     cdef np.ndarray[float, ndim=2, mode="c"] corr
     cdef np.ndarray[float, ndim=2, mode="c"] image_sat
     cdef np.ndarray[float, ndim=2, mode="c"] image_sqr_sat
     cdef float template_mean = np.mean(template)
     cdef float template_ssd
     cdef float inv_area
+    cdef ssize_t r, c, r_end, c_end
+    cdef ssize_t template_rows = template.shape[0]
+    cdef ssize_t template_cols = template.shape[1]
+    cdef float den, window_sqr_sum, window_mean_sqr, window_sum
 
     image_sat = integral.integral_image(image)
     image_sqr_sat = integral.integral_image(image**2)
@@ -63,24 +68,24 @@ def match_template(np.ndarray[float, ndim=2, mode="c"] image,
                                             mode="valid"),
                                 dtype=np.float32)
 
-    cdef int i, j
-    cdef float den, window_sqr_sum, window_mean_sqr, window_sum,
-    # move window through convolution results, normalizing in the process
-    for i in range(corr.shape[0]):
-        for j in range(corr.shape[1]):
-            # subtract 1 because `i_end` and `j_end` are used for indexing into
-            # summed-area table, instead of slicing windows of the image.
-            i_end = i + template.shape[0] - 1
-            j_end = j + template.shape[1] - 1
 
-            window_sum = integrate(image_sat, i, j, i_end, j_end)
+    # move window through convolution results, normalizing in the process
+    for r in range(corr.shape[0]):
+        for c in range(corr.shape[1]):
+            # subtract 1 because `i_end` and `c_end` are used for indexing into
+            # summed-area table, instead of slicing windows of the image.
+            r_end = r + template_rows - 1
+            c_end = c + template_cols - 1
+
+            window_sum = integrate(image_sat, r, c, r_end, c_end)
             window_mean_sqr = window_sum * window_sum * inv_area
-            window_sqr_sum = integrate(image_sqr_sat, i, j, i_end, j_end)
+            window_sqr_sum = integrate(image_sqr_sat, r, c, r_end, c_end)
             if window_sqr_sum <= window_mean_sqr:
-                corr[i, j] = 0
+                corr[r, c] = 0
                 continue
 
             den = sqrt((window_sqr_sum - window_mean_sqr) * template_ssd)
-            corr[i, j] /= den
+            corr[r, c] /= den
+
     return corr
 
diff --git a/skimage/feature/_texture.pyx b/skimage/feature/_texture.pyx
index 70a446bb..e2e3bb35 100644
--- a/skimage/feature/_texture.pyx
+++ b/skimage/feature/_texture.pyx
@@ -39,7 +39,7 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2,
     """
     cdef:
         np.int32_t a_inx, d_idx
-        np.int32_t r, c, rows, cols, row, col
+        ssize_t r, c, rows, cols, row, col
         np.int32_t i, j
 
     rows = image.shape[0]
@@ -52,8 +52,8 @@ def _glcm_loop(np.ndarray[dtype=np.uint8_t, ndim=2,
                     i = image[r, c]
 
                     # compute the location of the offset pixel
-                    row = r + <int>(sin(angle) * distance + 0.5)
-                    col = c + <int>(cos(angle) * distance + 0.5);
+                    row = r + <ssize_t>(sin(angle) * distance + 0.5)
+                    col = c + <ssize_t>(cos(angle) * distance + 0.5);
 
                     # make sure the offset is within bounds
                     if row >= 0 and row < rows and \
@@ -123,11 +123,11 @@ def _local_binary_pattern(np.ndarray[double, ndim=2] image,
     output_shape = (image.shape[0], image.shape[1])
     cdef np.ndarray[double, ndim=2] output = np.zeros(output_shape, np.double)
 
-    cdef int rows = image.shape[0]
-    cdef int cols = image.shape[1]
+    cdef ssize_t rows = image.shape[0]
+    cdef ssize_t cols = image.shape[1]
 
     cdef double lbp
-    cdef int r, c, changes, i
+    cdef ssize_t r, c, changes, i
     for r in range(image.shape[0]):
         for c in range(image.shape[1]):
             for i in range(P):
diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx
index 2f001ea4..34913450 100644
--- a/skimage/feature/corner_cy.pyx
+++ b/skimage/feature/corner_cy.pyx
@@ -10,7 +10,7 @@ from skimage.color import rgb2grey
 from skimage.util import img_as_float
 
 
-def corner_moravec(image, int window_size=1):
+def corner_moravec(image, ssize_t window_size=1):
     """Compute Moravec corner measure response image.
 
     This is one of the simplest corner detectors and is comparatively fast but
@@ -56,8 +56,8 @@ def corner_moravec(image, int window_size=1):
            [ 0.,  0.,  0.,  0.,  0.,  0.,  0.]])
     """
 
-    cdef int rows = image.shape[0]
-    cdef int cols = image.shape[1]
+    cdef ssize_t rows = image.shape[0]
+    cdef ssize_t cols = image.shape[1]
 
     cdef cnp.ndarray[dtype=cnp.double_t, ndim=2, mode='c'] cimage, out
 
@@ -71,7 +71,7 @@ def corner_moravec(image, int window_size=1):
     cdef double* out_data = <double*>out.data
 
     cdef double msum, min_msum
-    cdef int r, c, br, bc, mr, mc, a, b
+    cdef ssize_t r, c, br, bc, mr, mc, a, b
     for r in range(2 * window_size, rows - 2 * window_size):
         for c in range(2 * window_size, cols - 2 * window_size):
             min_msum = DBL_MAX