Apply suggestions from code review

Suggestions from review

Co-authored-by: Ulf Schiller <uschille@users.noreply.github.com>
Co-authored-by: Toby Hodges <tbyhdgs@gmail.com>

Author: 3 people

episodes/07-thresholding.md

@@ -347,18 +347,16 @@ plt.xlim(0, 1.0)
 
 ![](fig/maize-root-cluster-histogram.png){alt='Grayscale histogram of the maize root image'}
 
-The histogram has a significant peak around 0.2, and a second,
-shallower peak near 0.6.
+The histogram has a significant peak around 0.2 and then a broader "hill" around 0.6 followed by a smaller peak near 1.0. Looking at the grayscale image, we can identify the lower peak at 0.2 as the background and the larger pixel values as the foreground, but it is not so obvious what the threshold should be.
 Thus, this image is a good candidate for thresholding with Otsu's method.
 The mathematical details of how this works are complicated (see
 [the scikit-image documentation](https://scikit-image.org/docs/dev/api/skimage.filters.html#threshold-otsu)
 if you are interested),
 but the outcome is that Otsu's method finds a threshold value between
 the two peaks of a grayscale histogram.
-Note there is a third peak very close to 1.0 which corresponds to the white label and disk on the 
-left hand side of the image. It would be a good idea to remove these white pixels 
-before calculating the Otsu threshold. We will discuss how to do this later in the episode but for 
-now we will continue without removing them.
+:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+The histogram of the maize root image may prompt questions from learners about the interpretation of the peaks and the broader region around 0.6. The focus here is on the separation of background and foreground pixel values. We note that Otsu's method does not work well for the image with the shapes used earlier in this episode, as the foreground pixel values are more distributed. These examples could be augmented by a discussion of unimodal, bimodal, and multimodal histograms. While these points can lead to fruitful discussions, the text in this episode attempts to reduce cognitive load and deliberately simplifies the discussion.
+::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 The `ski.filters.threshold_otsu()` function can be used to determine
 the threshold automatically via Otsu's method.
@@ -662,7 +660,21 @@ data/trial-020.jpg,0.05886968085106383
 data/trial-216.jpg,0.13712117686170214
 data/trial-293.jpg,0.13190342420212767
 ```
+:::::::::::::::::::::::::::::::::::::::::: spoiler
 
+### What is `&` doing in the example above?
+
+The `&` operator above means that we have defined a logical AND statement. This combines the two tests of pixel intensities in the blurred image such that both must be true for a pixel's position to be set to `True` in the resulting mask.
+
+| Result of `t < blurred_image` | Result of `blurred_image < 0.95 | Resulting value in `binary_mask` |
+|----------|---------|---------|
+| False | True | False |
+| True | False | False |
+| True | True | True |
+ 
+Knowing how to construct this kind of logical operation can be very helpful in image processing. The University of Minnesota Library's [guide to Boolean operators](https://libguides.umn.edu/BooleanOperators) is a good place to start if you want to learn more.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
 Here are the binary images produced by the additional thresholding.
 Note that we have not completely removed the offending white pixels.
 Outlines still remain.