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fill_voids

★ 30 Open GitHub ↗

Fill holes in binary 2D & 3D images fast.

// repository documentation

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# Fill Voids
```python
# PYTHON
import fill_voids

img = ... # 2d or 3d binary image 
filled_image = fill_voids.fill(img, in_place=False) # in_place allows editing of original image
filled_image, N = fill_voids.fill(img, return_fill_count=True) # returns number of voxels filled in
```
```cpp 
// C++ 
#include "fill_voids.hpp"

size_t sx, sy, sz;
sx = sy = sz = 512;

uint8_t* labels = ...; // 512x512x512 binary image

// modifies labels as a side effect, returns number of voxels filled in
size_t fill_ct = fill_voids::binary_fill_holes<uint8_t>(labels, sx, sy, sz); // 3D

// let labels now represent a 512x512 2D image
size_t fill_ct = fill_voids::binary_fill_holes<uint8_t>(labels, sx, sy); // 2D
```
<p style="font-style: italics;" align="center">
<img height=384 src="https://raw.githubusercontent.com/seung-lab/fill_voids/master/comparison.png" alt="Filling five labels using SciPy binary_fill_holes vs fill_voids from a 512x512x512 densely labeled connectomics segmentation. (black) fill_voids 1.1.0 (blue) fill_voids 1.1.0 with `in_place=True` (red) scipy 1.4.1" /><br>
Fig. 1: Filling five labels using SciPy binary_fill_holes vs fill_voids from a 512x512x512 densely labeled connectomics segmentation. (black) fill_voids 1.1.0 (blue) fill_voids 1.1.0 with `in_place=True` (red) scipy 1.4.1. In this test, fill_voids (`in_place=False`) is significantly faster than scipy with lower memory usage. 
</p>

This library contains both 2D and 3D void filling algorithms, similar in function to [`scipy.ndimage.morphology.binary_fill_holes`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.binary_fill_holes.html), but with an eye towards higher performance. The SciPy hole filling algorithm uses slow serial dilations.

The current version of this library uses a scan line flood fill of the background labels and then labels everything not filled as foreground.

### pip Installation

```bash
pip install fill-voids
```

If there's no binary for your platform and you have a C++ compiler try:

```bash 
sudo apt-get install python3-dev # This is for Ubuntu, but whatever is appropriate for you
pip install numpy
pip install fill-voids --no-binary :all:
```

### Current Algorithm

1. Raster scan and mark every foreground voxel `2` for pre-existing foreground.
2. Raster scan each face of the current image and the first time a black pixel (`0`) is encountered after either starting or enountering a foreground pixel, add that location to a stack.
3. Flood fill (six connected) with the visited background color (`1`) in sequence from each location in the stack that is not already foreground.
4. Write out a binary image the same size as the input mapped as buffer != 1 (i.e. 0 or 2). This means non-visited holes and foreground will be marked as `1` for foreground and the visited background will be marked as `0`.

We improve performance significantly by using libdivide to make computing x,y,z coordinates from array index faster, by scanning right and left to take advantage of machine memory speed, by only placing a neighbor on the stack when we've either just started a scan or just passed a foreground pixel while scanning.

### Multi-Label Concept

For multi-label void filling, see https://github.com/seung-lab/fastmorph/

fill_voids

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