https://github.com/amilworks/segmentation
Applies mask (NIFTI format) to volumetric image (TIFF)
https://github.com/amilworks/segmentation
bisque python segmentation
Last synced: 3 months ago
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Applies mask (NIFTI format) to volumetric image (TIFF)
- Host: GitHub
- URL: https://github.com/amilworks/segmentation
- Owner: amilworks
- Created: 2019-01-24T21:22:56.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-27T04:42:38.000Z (over 6 years ago)
- Last Synced: 2025-01-22T08:47:37.175Z (4 months ago)
- Topics: bisque, python, segmentation
- Language: Jupyter Notebook
- Homepage:
- Size: 19.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **The Segmentation Mask Merger 5000**
#### Amil Khan | BisQue UCSB | Version 2.0
***
## Plot a Test Image
This helper function takes in one argument, `img_path`, which is simply the path of the image on your local computer. This step can be stepped if:
- You know what your data looks like
- Trust that everything in this notebook works
- Verified the absolute path of your image
**Arguments:**
- `img_path `: local path to volumetric image data, i.e. `.tif`
```python
def image_visualizer5000(img_path, num_slice=0):
img = skimage.io.imread(img_path, plugin='pil')
print("Successfully Loaded. Image Shape:", img.shape)
filename, file_extension = os.path.splitext(img_path)
io.imshow(img[num_slice])
plt.title("Slice Number: {} of {}".format(num_slice, len(img)), loc='left')
plt.axis('off')
```
```python
image_visualizer5000('/Users/macbook/Downloads/bisque-20190216.005014/T01.tif', num_slice=10)
```
Successfully Loaded. Image Shape: (28, 512, 512)
## Segmentation Function
This function overlays a masked image over the original image
takes two arguments, `NIFTI_img` and `volumetric_img`.
**Arguments:**
- `NIFTI_img `: local path to NIFTI image, i.e. `.nii`
- `volumetric_img `: local path to volumetric image data, i.e. `.tif`
**Output:**
- Overlaid image. Basically, NIFTI image + Volumetric with slight darkening to non-segmented region
```python
def segmentation(NIFTI_img, volumetric_img):
filename, file_extension = os.path.splitext(NIFTI_img)
if file_extension != '.nii':
print("Invalid Filetype --> ",filename+file_extension,"\nPlease check the filepath and/or if you are importing a valid NIFTI image.")
return None
nii_img = nib.load(NIFTI_img)
nii_img = nii_img.get_fdata()
print("Successfully Loaded NIFTI Image", nii_img.shape)
mask = skimage.io.imread(volumetric_img, plugin='pil')
mask = np.swapaxes(mask, axis1=0, axis2=2)
print("Successfully Loaded Mask", mask.shape)
overlay = np.zeros_like(nii_img)
for i in range(nii_img.shape[-1]):
overlay[:,:,i] = np.where((nii_img[:,:,i] >= 2) , mask[:,:,i], nii_img[:,:,i]*mask[:,:,i]+mask[:,:,i]/3)
print('-', end=' ')
print("\nSegmentation Merge Finished!\nOutput Shape:", np.rollaxis(overlay, axis=2).shape)
return np.rollaxis(overlay, axis=2) # So BisQue can read and view image correctly
```
```python
tom = segmentation('/Users/macbook/Downloads/bisque-20190216.005040/all.nii', '/Users/macbook/Downloads/bisque-20190216.005014/T01.tif')
```
Successfully Loaded NIFTI Image (512, 512, 28)
Successfully Loaded Mask (512, 512, 28)
- - - - - - - - - - - - - - - - - - - - - - - - - - - -
Segmentation Merge Finished!
Output Shape: (28, 512, 512)
## Verify the Output Image
```python
plt.imshow(tom[16], cmap='gray')
plt.title('Segmented Image Overlay')
plt.axis('off')
```
(-0.5, 511.5, 511.5, -0.5)
## Output to HDF file
If you are feeling ambitious, you can output the image to an HDF file and visualize it in 2D slices and a 3D volume using [BisQue](https://bisque.ece.ucsb.edu/client_service/). The output file will be created in your current working directory.
**Arguments:**
- `filename `: name of the output hdf file
- `key_name `: key/name of dataset in the HDF file
- `img`: Output image from segmentation function
```python
def img_to_HDF(filename, key_name, img):
f = h5py.File(filename, 'w')
f.create_dataset(key_name, data=img)
f.close()
```
```python
img_to_HDF(filename='SuperTom.h5', key_name='Finished_Product', img=tom)
```
***