Update README.md

README.md

@@ -50,14 +50,117 @@ Further details about installing ANTsPy or building it from source can be found
 
 ## Quickstart
 
-Here is an example of reading in an image, using various utility functions such as resampling and masking, then performing three-class Atropos segmentation.
+Here is a basic overview of some of the things you can do with ANTsPy. The main functionality includes reading / writing images, basic and advanced image operations, segmentation, registration, and visualization.
+
+### Reading and writing images
+
+You can read and write images of any format.
 
 ```python
 import ants
-img   = ants.image_read(ants.get_data("r16"))
-img   = ants.resample_image(img, (64,64), 1, 0 )
-mask  = ants.get_mask(img)
-segs1 = ants.atropos(a=img, m='[0.2,1x1]', c='[2,0]', i='kmeans[3]', x=mask)
+img = ants.image_read('path/to/image.nii.gz')
+ants.image_write(img, 'path/to/image.nii.gz')
+```
+
+Printing to the console provides a great deal of metadata information about the image.
+
+```python
+print(img)
+```
+
+```
+ANTsImage
+         Pixel Type : float (float32)
+         Components : 1
+         Dimensions : (256, 256)
+         Spacing    : (1.0, 1.0)
+         Origin     : (0.0, 0.0)
+         Direction  : [1. 0. 0. 1.]
+```
+
+### Image operations
+
+#### Basic
+
+Images can be operated on similar to numpy arrays - e.g., all math operations will work as expected.
+
+```python
+img = ants.image_read(ants.get_data('r16'))
+img2 = ants.image_read(ants.get_data('r64'))
+
+img + img2
+img - img2
+img * img2
+img / img2
+img ** 2
+```
+
+#### Indexing 
+
+You can also index images as you would a numpy array. Where possible, indexing an image will return an image with metadata intact.
+
+```python
+img = ants.image_read(ants.get_data('mni')) # 3D image
+
+img[:20,:20,:20] # 3D image
+img[:,:,20] # 2D image
+img[:,20,20] # 1D array
+img[20,20,20] # sigle value
+
+# setting works as well
+img[:20,:20,:20] = 10
+```
+
+#### Advanced
+
+There is a large collection of advanced image operations that can be performed on images. 
+
+```python
+img = ants.image_read(ants.get_data('mni')) # 3D image
+img = ants.smooth_image(img, 2)
+img = ants.resample_image(img, (3,3,3))
+img = ants.pad_image(img, pad_width=(4,4,4))
+```
+
+And if you ever need to convert to or from numpy, it is straight-forward to do so.
+
+```python
+img = ants.image_read(ants.get_data('mni')) # 3D image
+arr = img.numpy()
+arr += 2
+img2 = ants.from_numpy(arr)
+```
+
+### Segmentation
+
+Atropos is an example of a powerful three-class segmentation algorithm provided to you.
+
+```python
+img = ants.image_read(ants.get_data("r16"))
+mask = ants.get_mask(img)
+result = ants.atropos(a=img, m='[0.2,1x1]', c='[2,0]', i='kmeans[3]', x=mask)
+```
+
+### Registration
+
+The full registration functionality of ANTs is available via the `ants.registration` function.
+
+```python
+fixed_image = ants.image_read(ants.get_ants_data('r16')).resample_image((60,60), 1, 0)
+moving_image = ants.image_read(ants.get_ants_data('r64')).resample_image((60,60), 1, 0)
+mytx = ants.registration(fixed_image, moving_image, type_of_transform = 'SyN' )
+```
+
+### Plotting
+
+A diverse set of functions are available to flexibly visualize images, optionally with discrete or continuous overlays. The `ants.plot` function will meet most needs.
+
+```python
+img = ants.image_read(ants.get_data("mni")) # 3D image
+ants.plot(img)
+
+# with overlay
+ants.plot(img, overlay = img > img.mean())
 ```
 
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