[BUG] Potential wrong edge_mask calculation and out-of-range indexing issues with QuickShear defacing

### Description of the potential `edge_mask` calculation issue

@[sezginer](https://github.com/sezginerr) and I might have found an issue in `brainles_preprocessing/defacing/quickshear/nipy_quickshear.py` where the `edge_mask` computation can become incorrect when the brain area in the brain masks touch the array boundary.

In particular, the current implementation of `edge_mask` uses `np.roll` to compare shifted versions of the mask:

https://github.com/BrainLesion/preprocessing/blob/6878ce198dce5454ddef9ae10953f4906b8ba1de/brainles_preprocessing/defacing/quickshear/nipy_quickshear.py#L86-L93

When the brain mask touches an array edge, `np.roll` introduces **wrap-around**: voxels rolled off from one side, appear on the opposite side. This creates an artificial edge on the opposite boundary. As a result, `edgemask` contains spurious edges and can propagate incorrect geometry into subsequent steps.

An example case is visualized in the top row of the first figure below. In RPS orientation, **posterior** part of the brain mask touches the **posterior array boundary**. During `edge_mask` calculation, 'fake' edge voxels appear on the **anterior** side even though the real boundary contact is **posterior**. And these 'fake' edges propagate through `convex_hull`, and slope calculation steps. Second figure zooms in to 'fake' edge area.


### Description of the potential out-of-range indexing during `defaced_mask` calculation issue

Additionally (related, but not necessarily dependent), a separate error can occur during the calculation of the defacing mask, if the computed defacing plane intersects the array edge **posterior to the brain** (e.g., as in the top row right plot of the first figure above).

Based on the `ys` calculation with `mask_RPS.shape[2]` and the for loop iterating over the positive `ys` indices, `x` values might go beyond `defaced_mask_RPS.shape[1]` range and cause index out-of-range errors for such special cases.
https://github.com/BrainLesion/preprocessing/blob/6878ce198dce5454ddef9ae10953f4906b8ba1de/brainles_preprocessing/defacing/quickshear/nipy_quickshear.py#L162-L166

In our case, the incorrect `edgemask` triggered this later crash, but we believe the out-of-range indexing can happen independently depending on geometry of brain masks and plane placement (although we acknowledge this should be a very rare case with regular brain segmentations).

### To Reproduce
Steps to reproduce the behavior:
1. Download this brain segmentation mask: [native__t1-der-cor-nc_brain_mask.nii.gz](https://github.com/user-attachments/files/24545433/native__t1-der-cor-nc_brain_mask.nii.gz)
2. Install 'BrainLes-Preprocessing' as suggested
3. Run defacing with the example code below:
```python
from brainles_preprocessing.defacing import QuickshearDefacer

defacer = QuickshearDefacer(
buffer=10.0,
force_atlas_registration=False, # We don't need atlas registration
)

defacer.deface(
input_image_path=Path(your_path_to_native__t1-der-cor-nc_brain_mask),
mask_image_path=Path(your_path_to_output_defacing_mask)
)
```
4. You should see an error message similar to this:
```sh
Traceback (most recent call last):
File "/home/user/deface_test.py", line 112, in
defacer.deface(
File "/home/user/preprocessing/brainles_preprocessing/defacing/quickshear/quickshear.py", line 68, in deface
mask = run_quickshear(bet_img=bet_img, buffer=self.buffer)
File "/home/user/preprocessing/brainles_preprocessing/defacing/quickshear/nipy_quickshear.py", line 166, in run_quickshear
defaced_mask_RPS[:, x, :y] = 0
IndexError: index 180 is out of bounds for axis 1 with size 180
```
5. As intermediate values such as `edgemask` are implicit, you would need to extract those arrays manually.

### Expected behavior
Second row of the first figure above shows our expected behavior. To get these results:
1. We replace the `np.roll` implementation in `edge_mask` calculation with convolution as below:
```python
kernel = np.array([
[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]
])
edgemask = ndimage.convolve(brain, kernel, mode='constant', cval=0)
```

### operating system and version
Debian GNU/Linux 11 (bullseye)

### Python environment and version?
miniforge3 environment with Python 3.10.19

### version of brainles_preprocessing
brainles_preprocessing==0.6.8
numpy==2.2.6