diff --git a/doc/source/user_guide/transforming_image_data.txt b/doc/source/user_guide/transforming_image_data.txt
index d247db3b..1e26b138 100644
--- a/doc/source/user_guide/transforming_image_data.txt
+++ b/doc/source/user_guide/transforming_image_data.txt
@@ -17,7 +17,7 @@ Color images can be represented using different `color spaces
 <http://en.wikipedia.org/wiki/Color_space>`_. One of the most common
 color spaces is the `RGB space
 <http://en.wikipedia.org/wiki/RGB_color_model>`_, where an image has
-red, blue and green channels. However, other color models are widely
+red, green and blue channels. However, other color models are widely
 used, such as the `HSV color model
 <http://en.wikipedia.org/wiki/HSL_and_HSV>`_ (for hue, saturation and
 value), where hue can be changed independently of saturation or value, or
@@ -54,8 +54,8 @@ Converting an RGB image to a grayscale image is realized with
     >>> img = data.astronaut()
     >>> img_gray = rgb2gray(img)
 
-:func:`rgb2gray` uses a non-uniform weigthing of color channels, because of the
-different sensivity of the human eye to different colors. ::
+:func:`rgb2gray` uses a non-uniform weighting of color channels, because of the
+different sensitivity of the human eye to different colors. ::
 
     >>> red_pixel = np.array([[[255, 0, 0]]], dtype=np.uint8)
     >>> color.rgb2gray(red_pixel)
@@ -95,14 +95,15 @@ Contrast and exposure
 
 .. currentmodule:: skimage.exposure
 
-Image values can take values determined by the `dtype` of the image (see
-:ref:`data_types`), such as 0 to 255 for `uint8` images or [-1, 1] for
-floating point images. However, most images either have a narrower range
-of values (because of poor contrast), or have most pixel values
-concentrated in a subrange. :mod:`skimage.exposure` provides functions
-that modify the distribution of pixels values of an image.  
+Image pixels can take values determined by the ``dtype`` of the image
+(see :ref:`data_types`), such as 0 to 255 for ``uint8`` images or [-1, 1]
+for floating-point images. However, most images either have a narrower
+range of values (because of poor contrast), or have most pixel values
+concentrated in a subrange of the accessible values.
+:mod:`skimage.exposure` provides functions that modify the distribution
+of pixels values of an image.  
 
-A first class of methods compute a nonlinear function of the luminance,
+A first class of methods compute a nonlinear function of the intensity,
 which is always the same no matter the pixel values of a specific image.
 Such methods are often used for correcting a known non-linearity of
 sensors, or receptors such as the human eye. A known example is the
@@ -167,13 +168,3 @@ gradients across the image. See the example
   * :ref:`example_plot_equalize.py`
 
 
-Image filtering
----------------
-
-.. currentmodule:: skimage.filters
-
-Denoising and restoration
--------------------------
-
-Mathematical morphology
------------------------