semvec/pypi-packages huggingface dataset:
intensipy 0.1.0
Normalize intensity values in 3D images.
License: BSD

# intensipy
Normalize intensity values in 3D image stacks.

# Current Methods

## 1. [Intensify3D](https://github.com/nadavyayon/Intensify3D)

Python implementation of the Intensify3D algorithm originally developed by [Yoyan et al](https://www.nature.com/articles/s41598-018-22489-1). There are some minor adjustments:

  1. Semi-quantile normalization is the only Z-normalization method currently implemented.
  2. Pixels that are quantile normalized are optionally smoothed using they Savitzky-Galoy method outlined in the original paper. In practice this was necessary to reduce artefact noise.
  3. Tissue detection is not currently supported.
  4. By default, contrast stretching is performed by `skimage.exposure.rescale_intensity()`. To perform contrast stretching as implemented by the original *Intensify3D*, set `stretch_method='intensify3d'` 
  5. If no maximum background intensity threshold `t` is provided, `t` will be estimated for each slice using Otsu's method.

### Original Paper Results
![Original](https://raw.githubusercontent.com/nadavyayon/Intensify3D/master/Examples/Montage2-01.jpg)

### Intensipy Results
![Artificial Data](https://media.githubusercontent.com/media/dakota-hawkins/intensipy/master/images/artificial_results.png)

### Z-normalization Example
![Confocal Embryo Image](https://github.com/dakota-hawkins/intensipy/blob/master/images/embryo_example.png)

### Average Intensity Comparison 
![Scatterplot](https://media.githubusercontent.com/media/dakota-hawkins/intensipy/master/images/average_intensity.png)

# Installation

Clone the repository and from the terminal run:

```pip install .```

# Example

```python
import numpy as np
import matplotlib.pyplot as plt

from intensipy import Intensify

# decreasing average intensity as z increases.
img_stack = 1 / np.arange(1, 6)[:, np.newaxis, np.newaxis]\
          * np.random.randint(0, 255, (5, 512, 512))                           

for each in img_stack: 
    plt.imshow(each, vmin=img_stack.min(), vmax=img_stack.max(), cmap='gray') 
    plt.show()

model = Intensify()
out = model.normalize(img_stack)

for each in out: 
    plt.imshow(each, vmin=out.min(), vmax=out.max(), cmap='gray') 
    plt.show()
```

# References

1.Yayon, N. et al. Intensify3D: Normalizing signal intensity in large heterogenic image stacks. Scientific Reports 8, 4311 (2018).