Metadata-Version: 2.0
Name: mrivis
Version: 0.2
Summary: Tools and scripts for visualization and comparison of 3d MRI scans (T1, T2 etc)
Home-page: https://github.com/raamana/mrivis
Author: Pradeep Reddy Raamana
Author-email: raamana@gmail.com
License: MIT license
Description-Content-Type: UNKNOWN
Keywords: mrivis
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: Click (>=6.0)


# mrivis

Tools for comparison of registration and spatial similarity of 3d MRI scans (T1, T2, PET etc) using checkerboard plots.


![https://img.shields.io/pypi/v/mrivis.svg](https://pypi.python.org/pypi/mrivis)


## Installation

```bash
pip install -U mrivis
```

## Usage:

```python
from mrivis import checkerboard, voxelwise_diff, red_green

path1 = '/Users/Reddy/Desktop/image.nii'
path2 = '/Users/Reddy/Desktop/another.nii'

checkerboard(path1, path2, patch_size=5) # square patches

checkerboard(path1, path2, rescale_intensity_range=(0, 256) )

checkerboard(path1, path2, patch_size=10,
             num_rows=1, num_cols=3) # 1 row per dimension, with 3 columns

checkerboard(path1, path2, patch_size=[10, 20], # rectangular patches
             num_rows=2, # 2 rows per dimension (6 rows in total)
             num_cols=5) # 5 panels per row

voxelwise_diff(path1, path2)
voxelwise_diff(path1, path2, abs_value=False)

color_mix(path1, path2, alpha_channels=(1, 1))
red_green(path1, path2, alpha_channels=(0.7, 1))

```

## Various options available

![flyer](docs/flyer_option_matrix.png)


## Sample outputs

When the two scans are mismatched:

![vis_all3](docs/zoomed_in/vis_all3.png)

When the mismatch is low (here a smoothed image is comapred to its original),
you can see the differences in intensity (due to smoothing),
but you can also see that they are both spatially aligned accurately:

![flyer2_low_mismatch](docs/flyer2_low_mismatch.png)

With really low patch-sizes (e.g. 1, which is voxel-wise alternation), you can see the alignment even better:

![vis_voxelwise_axial](docs/zoomed_in/vis_voxelwise_axial.png)

When there is mismatch, you can clearly see it (patch size 15 voxels square):

![vis_all3_mismatch_ps15](docs/zoomed_in/vis_all3_mismatch_ps15.png)

Let's make the patches a bit bigger (patch size 25 voxels square):

![vis_all3_mismatch_ps25_axial](docs/zoomed_in/vis_all3_mismatch_ps25_axial.png)
![vis_all3_mismatch_ps25_sagittal](docs/zoomed_in/vis_all3_mismatch_ps25_sagittal.png)

Let's make the patches a even bigger (50 voxels square):

![vis_all3_mismatch_ps50](docs/zoomed_in/vis_all3_mismatch_ps50.png)

Let's use a **rectangular** patch (10 voxels high and 30 voxels wide):

![vis_all3_mismatch__rect_ps10_30_sagittal](docs/zoomed_in/vis_all3_mismatch__rect_ps10_30_sagittal.png)
![vis_all3_mismatch__rect_ps10_30_axial](docs/zoomed_in/vis_all3_mismatch__rect_ps10_30_axial.png)

If they were identical (no mismatch at all), you won't see any edges or borders:

![identical](docs/zoomed_in/vis_all3_identical.png)

Full layout with 6x6 pangels can be seen in [this folder](docs/comprehensive).

## More example outputs

Many more example outputs, for different combinations of parameters, can be seen in this [example outputs folder in docs](docs/example_outputs).








=======
History
=======

0.1.0 (2017-12-05)
------------------

* First release on PyPI.


