diff --git a/README.md b/README.md
index 67e4d2d..8173a33 100644
--- a/README.md
+++ b/README.md
@@ -6,19 +6,24 @@ The following is a simple pure python implementation of forward addative registr
 
 # Simple example  : 
 
-import LKForwardAddativeImageReg as LKReg 
+```python
+from LKReg import LKForwardAddativeImageReg as LKReg
 import cv2
-
-#load images 
+ ```
+##### Load images 
+```python
 fixed = cv2.cvtColor(cv2.imread('fixed.png'), cv2.COLOR_BGR2GRAY)
 moving = cv2.cvtColor(cv2.imread('moving.png'), cv2.COLOR_BGR2GRAY)
-
-#perform registration
+```
+##### Perform registration
+```python
 tforms, deltas  = LKReg.iterativeReg(fixed, moving, niter = 150, npyramids = 2)
-
-#show before and after
+```
+##### Show before and after
+```python
 LKReg.imshowpair(fixed,moving,tforms[0], figureTitle = 'before')
 LKReg.imshowpair(fixed,moving,tforms[-1], figureTitle = 'after')
+```
 
 # In depth  :
 This module allows for fast, subpixel accurate, robust registration. It essentially performs stochatic gradient decent using a first order taylor polynomial aproximation of the gradient. This is very similar to whats found in cv2 / matlab imregister. The ECC ( enhanced correlation coefficient ) algorithm is also implemented which adds robustness and reduces the numbers of needed itterations. This registration can use pyramiding ( or not - npyramids = 1 ) to deal with relatively large transformations. The registration is homography only ( no affine, translation, similarity ...etc options ( yet )).