Change shapes image

episodes/filters-and-thresholding.md

@@ -187,19 +187,20 @@ from skimage.util import random_noise, img_as_ubyte
 from packaging import version
 
 image = np.full((100, 100), 10, dtype="uint8")
-image[10:50, 20:80] = 80
+image[10:50, 20:80] = 200
 image[disk((75, 75), 15)] = 140
 rr, cc = polygon([90, 60, 90], [10, 30, 50])
-image[rr, cc] = 200
+image[rr, cc] = 80
 if version.parse(skimage.__version__) < version.parse("0.21"):
   image = img_as_ubyte(random_noise(image, var=0.0005, seed=6))
 else:
   image = img_as_ubyte(random_noise(image, var=0.0005, rng=6))
 viewer.add_image(image)
 ```
 
-![](fig/manual-thresholding-exercise-shapes.png){alt="Test image containing a 
-rectangle, circle and triangle"}
+![](fig/shapes.png){alt="Test image containing a 
+rectangle, circle and triangle" width="50%"}
+
 
 Create a mask for each shape by choosing thresholds based on the image's 
 histogram.
@@ -228,30 +229,30 @@ viewer.add_labels(mask)
 First, we show a histogram for the image by selecting the 'image' layer, then:  
 `Plugins > napari Matplotlib > Histogram`
 
-![](fig/manual-thresholding-exercise-histogram.png){alt="Histogram of the 
+![](fig/shapes-histogram.png){alt="Histogram of the 
 shape image"}
 
 By moving the left contrast limits node we can figure out what each peak 
 represents. You should see that the peaks from left to right are:
 
 - background
-- rectangle
-- circle
 - triangle
+- circle
+- rectangle
 
 Then we set thresholds for each (as below). Note that you may not have exactly 
 the same threshold values as we use here! There are many different values that 
 can give good results, as long as they fall in the gaps between the peaks in the 
 image histogram.
 
 ```python
-rectangle = (image > 53) & (image < 110)
+rectangle = image > 171
 viewer.add_labels(rectangle)
 
 circle = (image > 118) & (image < 166)
 viewer.add_labels(circle)
 
-triangle = image > 171
+triangle = (image > 53) & (image < 110)
 viewer.add_labels(triangle)
 ```
 
@@ -676,7 +677,7 @@ spread of pixel values in both the background and your class of interest. In
 effect, it's trying to find two peaks in the image histogram and place a 
 threshold in between them.
 
-This means it's only suitable for images that show two clear peaks in their 
+This means it's most suitable for images that show two clear peaks in their 
 histogram (also known as a 'bimodal' histogram). Other images may require 
 different automated thresholding methods to produce a good result. 
 
@@ -688,8 +689,7 @@ For this exercise, we'll use the same example image as the [manual thresholding
 exercise](#manual-thresholds). If you don't have that image open, run the top 
 code block in that exercise to open the image:
 
-![](fig/manual-thresholding-exercise-shapes.png){alt="Test image containing a 
-rectangle, circle and triangle"}
+![](fig/shapes.png){alt="Test image containing a rectangle, circle and triangle" width="50%"}
 
 Try some of the other automatic thresholding options provided by the 
 `napari-skimage` plugin. Set the 'method' to different options in 
@@ -713,26 +713,14 @@ row of the layer controls.
 
 ## Solution
 
-Otsu thresholding chooses a threshold that separates the background 
-from the three shapes:
-
-![](fig/otsu-shapes.png){alt="Mask of shapes (brown) overlaid on shapes image - 
-made with Otsu thresholding"}
-
-Li and mean thresholding gives a very similar result.
-
-Yen gives a different result - isolating the triangle and circle from the rest 
-of the image. Some of the pixels in the rectangle are also labelled, but only 
-in patchy areas:
-
-![](fig/yen-shapes.png){alt="Mask of shapes (brown) overlaid on shapes image - 
-made with Yen thresholding"}
+![](fig/shapes-thresholds.png){alt="Masks via automated thresholding"}
 
-Finally, Sauvola gives a completely different result, including a large number 
-of pixels from the background:
+- **Otsu**: The mask does not include the low‑intensity triangle.
+- **Mean**: Generally good, but on close inspection a few darker triangle pixels are still missed.
+- **Li**: This looks great! The mask includes all pixels of the three shapes, while excluding the background. 
+- **Yen**: Includes the shapes but also includes some background.
+- **Sauvola**: A substantial amount of background is included here.
 
-![](fig/sauvola-shapes.png){alt="Mask of shapes (brown) overlaid on shapes 
-image - made with Sauvola thresholding" width="60%"}
 
 The important point is that different automatic thresholding methods will work 
 well for different kinds of images, and depending on which part of an image you