Plot for documentation

Add plot of runtime and memory usage for local thickness and structure tensor. Time and memory usage are respectively computed using the commands for profiling in Jupyter Notebook %time and %memit

qim3d/processing/local_thickness_.py

@@ -17,6 +17,13 @@ def local_thickness(
 ) -> np.ndarray:
     """Wrapper for the local thickness function from the [local thickness package](https://github.com/vedranaa/local-thickness)
 
+    The "Fast Local Thickness" by Vedrana Andersen Dahl and Anders Bjorholm Dahl from the Technical University of Denmark is a efficient algorithm for computing local thickness in 2D and 3D images. 
+    Their method significantly reduces computation time compared to traditional algorithms by utilizing iterative dilation with small structuring elements, rather than the large ones typically used. 
+    This approach allows the local thickness to be determined much faster, making it feasible for high-resolution volumetric data that are common in contemporary 3D microscopy. 
+    
+    Testing against conventional methods and other Python-based tools like PoreSpy shows that the new algorithm is both accurate and faster, offering significant improvements in processing time for large datasets.
+
+
     Args:
         image (np.ndarray): 2D or 3D NumPy array representing the image/volume.
             If binary, it will be passed directly to the local thickness function.
@@ -30,7 +37,7 @@ def local_thickness(
 
     Returns:
         local_thickness (np.ndarray): 2D or 3D NumPy array representing the local thickness of the input image/volume.
-    
+
     Example:
         ```python
         import qim3d
@@ -48,6 +55,11 @@ def local_thickness(
         ```
         ![local thickness 2d](assets/screenshots/local_thickness_2d.png)
 
+    !!! info "Runtime and memory usage of the local thickness method for different volume sizes"
+        ![local thickness estimate time and mem](assets/screenshots/Local_thickness_time_mem_estimation.png)
+
+        Performance computed on Intel(R) Xeon(R) Gold 6226 CPU @ 2.70GHz.
+
     !!! quote "Reference"
         Dahl, V. A., & Dahl, A. B. (2023, June). Fast Local Thickness. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
         <https://doi.org/10.1109/cvprw59228.2023.00456>
@@ -63,11 +75,9 @@ def local_thickness(
         month=jun }
 
         ```
-    
-    !!! info "Runtime and memory usage of the local thickness method for different volume sizes"
-        ![local thickness estimate time and mem](assets/screenshots/Local_thickness_time_mem_estimation.png)
-        Performance computed on Intel(R) Xeon(R) Gold 6226 CPU @ 2.70GHz.
-       
+
+
+
     """
 
     # Check if input is binary