docs: finalize change of extension

Author: EscapedGibbon

docs/tutorials/extracting-metadata.md

@@ -4,7 +4,7 @@ In this tutorial we will discuss metadata extraction with an image that we alrea
 
 ![Particles](./images/roiAnalysis/particles.jpg)
 
-[Metadata](../glossary#metadata 'glossary link on metadata') represents information about various aspects of an image itself. It can be something basic such as the date when an image was taken, or something more specific like the name of the camera that the image was taken by. You can extract metadata tags that can provide additional information about an image by using this command:
+[Metadata](../glossary.md#metadata 'glossary link on metadata') represents information about various aspects of an image itself. It can be something basic such as the date when an image was taken, or something more specific like the name of the camera that the image was taken by. You can extract metadata tags that can provide additional information about an image by using this command:
 
 ```ts
 const meta = image.meta;

docs/tutorials/image-segmentation-with-watershed.md

@@ -104,7 +104,7 @@ First thing that you possibly need to do is to remove [image noise](https://en.w
 
 ImageJS has several kinds of blurring:
 
-- [blur filter](../features/filters/blur.mdx'internal link on blur')
+- [blur filter](../features/filters/blur.mdx 'internal link on blur')
 
 - [gaussian blur filter](../features/filters/gaussian-blur.mdx 'internal link on gaussian blur')
 

docs/tutorials/image-stack-analysis.md

@@ -61,7 +61,7 @@ const stack = decodeStack(buffer);
 ```
 
 :::warning
-`Stack` class works only with images that share same properties. Particularly, values for [bit depth](../glossary#bit-depth 'internal link on bit depth'), [color model](../glossary#color-model 'internal link on color model'), width and height must be the same.
+`Stack` class works only with images that share same properties. Particularly, values for [bit depth](../glossary#bit-depth 'internal link on bit depth'), [color model](../glossary.md#color-model 'internal link on color model'), width and height must be the same.
 :::
 
 ## Find the image with maximum values:

docs/useful-tips/blurring-techniques-and-their-differences.md

@@ -4,7 +4,7 @@ If you looked at some of our tutorials, you might have noticed that we apply a b
 
 ## Blur
 
-To be precise blur is a general term that refers to a reduction in the sharpness or clarity of an image. It also works to reduce some of the noise, such as [gaussian noise](https://en.wikipedia.org/wiki/Gaussian_noise#:~:text=In%20signal%20processing%20theory%2C%20Gaussian,can%20take%20are%20Gaussian%2Ddistributed. 'wikipedia link on gaussian noise') for example. In ImageJS blur is actually a box blur or mean blur. It is a filter that uses convolution matrix to calculate an average among the surrounding pixels which are within the transformation matrix ([kernel](../glossary#kernel 'glossary link on kernel')) and then applies this value.
+To be precise blur is a general term that refers to a reduction in the sharpness or clarity of an image. It also works to reduce some of the noise, such as [gaussian noise](https://en.wikipedia.org/wiki/Gaussian_noise#:~:text=In%20signal%20processing%20theory%2C%20Gaussian,can%20take%20are%20Gaussian%2Ddistributed. 'wikipedia link on gaussian noise') for example. In ImageJS blur is actually a box blur or mean blur. It is a filter that uses convolution matrix to calculate an average among the surrounding pixels which are within the transformation matrix ([kernel](../glossary.md#kernel 'glossary link on kernel')) and then applies this value.
 
 ![Convolution process](./images/blurring/2D_Convolution_Animation.gif)
 
@@ -23,7 +23,7 @@ The idea is that the closer you are to the pixel in check, the more weight it wi
 
 The main parameter of gaussian blur is called "sigma" and it is responsible for the width of the gaussian bell curve, therefore it controls the overall smoothness of the end result.
 
-Gaussian blur is a good preparatory tool for edge detection. Edge detection's algorithms are sensitive to noise and small details so blur smoothens them. For instance here is the example of a [Canny Edge detector](../features/morphology/canny-edge-detector 'internal link on canny edge detector') with and without gaussian blur:
+Gaussian blur is a good preparatory tool for edge detection. Edge detection's algorithms are sensitive to noise and small details so blur smoothens them. For instance here is the example of a [Canny Edge detector](../features/morphology/canny-edge-detector.mdx 'internal link on canny edge detector') with and without gaussian blur:
 
 ![Edge detection with gaussian](./images/blurring/edgesWithBlurs.png)