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https://github.com/pmneila/morphsnakes

Morphological snakes for image segmentation and tracking
https://github.com/pmneila/morphsnakes

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Morphological snakes for image segmentation and tracking

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README 

        
# Morphological Snakes



[![Build Status](https://travis-ci.org/pmneila/morphsnakes.svg?branch=master)](https://travis-ci.org/pmneila/morphsnakes)

[![codecov](https://codecov.io/gh/pmneila/morphsnakes/branch/master/graph/badge.svg)](https://codecov.io/gh/pmneila/morphsnakes)



**morphsnakes** is an implementation of the *Morphological Snakes* for image segmentation.

**morphsnakes** supports 2D images and 3D volumes.



![anim_nodule](examples/anim_nodule.gif)

![anim_dendrite](examples/anim_dendrite.gif)



*Morphological Snakes* [1][2] are a family of methods for image segmentation.

Their behavior is similar to that of *Active Contours* like *Geodesic

Active Contours* [3] or *Active Contours without Edges* [4]. However,

traditional approaches for *Active Contours* require solving PDEs over

floating points arrays, which is slow and might have numerical stability issues.

Instead of PDEs, *Morphological Snakes* use morphological operators

-such as dilation or erosion- over a binary array. This makes

*Morphological Snakes* faster and numerically more stable than their

traditional counterparts.



Two *Morphological Snakes* methods are available in **morphsnakes**:



* *Morphological Geodesic Active Contours*, or **MorphGAC**, implemented in the

  function ``morphological_geodesic_active_contour``,

* *Morphological Active Contours without Edges*, or **MorphACWE**, implemented in

  the function ``morphological_chan_vese``.



## Installation



### Option 1: Copy `morphsnakes.py` to your project



All the required code is contained in [`morphsnakes.py`](morphsnakes.py). You can

copy this file into your own project with

```

wget https://raw.githubusercontent.com/pmneila/morphsnakes/master/morphsnakes.py

```



### Option 2: `pip install`



It is also possible to install **morphsnakes** with `pip`

```

pip install morphsnakes

```



## Getting started



The file [`examples.py`](examples.py) contains multiple examples of **MorphGAC**

and **MorphACWE**. You can take any example from that file as a starting point for

your project.



## What are the differences between **MorphGAC** and **MorphACWE**?



### MorphACWE



**MorphACWE** works well when pixel values of the inside

and the outside regions of the object to segment have different averages.

Unlike **MorphGAC**, **MorphACWE** does not require that the contours of the

object are well defined, and it can work over the original image without any

preprocessing. 



**MorphACWE** is much easier to setup and use than **MorphGAC**, and much more

robust to noise. You should try using **MorphACWE** first, and only switch

to **MorphGAC** when it is clear to you that **MorphACWE** is not suitable

for the kind of images you are working with.



![anim_lakes](examples/anim_lakes.gif)

![anim_europe](examples/anim_europe.gif)



### MorphGAC



**MorphGAC** is suitable for images with visible contours, even when these

contours might be noisy, cluttered, or partially unclear. It requires, however,

that the image is preprocessed to highlight the contours. This can be done

using the function ``inverse_gaussian_gradient``, although you might want

to define your own version. The quality of the **MorphGAC** segmentation

depends greatly on this preprocessing step.



![anim_starfish](examples/anim_starfish.gif)

![anim_nodule](examples/anim_nodule.gif)



## References



[1]: *A Morphological Approach to Curvature-based Evolution of Curves and

    Surfaces*, Pablo Márquez-Neila, Luis Baumela and Luis Álvarez. In IEEE

    Transactions on Pattern Analysis and Machine Intelligence (PAMI),

    2014, DOI 10.1109/TPAMI.2013.106



[2]: *Morphological Snakes*. Luis Álvarez, Luis Baumela, Pablo Márquez-Neila.

   In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2010 (CVPR10).



[3]: *Geodesic Active Contours*, Vicent Caselles, Ron Kimmel and Guillermo

    Sapiro. In International Journal of Computer Vision (IJCV), 1997,

    DOI:10.1023/A:1007979827043



[4]: *Active Contours without Edges*, Tony Chan and Luminita Vese. In IEEE

    Transactions on Image Processing, 2001, DOI:10.1109/83.902291