As a first step, You can take the table of measurements as-is, run k-means on all the columns representing intensities.
In the image, I see pixels in 5 colors (red, orange, green, azure, magenta), so the author of the top image has decided to run k-means with k=5 clusters. (k in in k-means is often chosen to be an odd number)
To get a clearer signal, people often run preprocessing steps such as Principal Components analysis (PCA) on their datasets before running k-means, and then choose the first, say, 10 components (should be larger than 5). Then people run k-means on those 10 columns.
Choosing the number of components to calculate k from, and then choosing a value for k, is basically guesswork, at least initially.
It is also guesswork to assign colors to cluster numbers found by the k-means algorithm.
So you probably have to set a lookup table manually, in another preprocessing step, such as
1 => "red".
2 => "azure",
3 => "limegreen"
4 => "yellow",
5 => "orange" }. And then make an x-y plot coloring the pixels accordingly. The sequence is arbitrary.
Sorry, I don't know how to do this in Orange.