Detect lines transitioning to blobs in images
If you are not interested in picking out the frame that bubbles start forming and since the transition is (clearly) from order (clearly formed shapes) to disorder (mostly noise), you can use a feature based approach. That is, characterise the whole image with a few numbers.
An obvious feature here is image entropy. The typical (but not the only) way to calculate entropy of an image / signal is through its normalised histogram (you need to normalise it, to get to a probability).
Are there any differences between the histograms of the images?
Yes there are (at least, the ones that come with the question):

From left to right, we have the image with the well defined shapes (ordered), the middle state and on the right the final state where there is mostly background plus some noise.
The striking feature here is that when the image is ordered, the ridges that are well defined because they create contrast with their background create a bimodal histogram. One mode corresponds to the background (the lower one) and one mode corresponds to the &quot;foreground&quot; (the one that forms a &quot;shelf&quot; towards the right) or the worm-like ridges.
You can catch these differences in many different ways including (of course) distance from Gaussian distribution, skewness, etc. Where the image is going to end up is a set of pixels distributed normally, any deviation from that &quot;profile&quot; must be due to formation of those ridges (in this particular context).
If you try to measure (Shannon) entropy over these images, you get the following three values:
    Ordered: 7.2331079063161425
    Middle:  6.734680695379271
    Noise:   6.591988661960253

(These measurements come from skimage.measure.shannon_entropy() )
This number looks at the frequencies of occurence of the pixels and returns the number of bits required to describe the value of a pixel.
You might feel here that these numbers are contradicting: Low entropy means more order, high entropy means less order. &quot;Less order&quot; here means closer to an absolutely uniform distribution. That is, you cannot really predict what comes next, the image is entirely random.
But at the same time, this description might also help understanding what is going on here: The &quot;Ordered&quot; image alternates between two means (more preference on the lower ofcourse) and spans a wider range of highly probable grey tones and the &quot;Noisy&quot; image is basically $128 \pm \approx 70$ with diminishing probability.
Shannon entropy evaluated in this way looks at the pixel values independently of each other. But they are not. The meandering structure forms straight and curved points to the extent that if one pixel is &quot;on&quot;, it is highly likely that the pixel next to it is on as well. So, we are not talking about single pixel probablities any more.
To &quot;catch&quot; this kind of differences, you need a measure of entropy that is sensitive to them. One of them is Entropy as calculated through the co-occurence matrix.
The name is kind of self-descriptive but very briefly: The co-occurence matrix describes how the values of two different pixels that are some distance appart co-vary. Additionally, since we now talk about the distance between two pixels, we can also define a bearing. This offset, implies that the co-occurence matrix describes co-occurence along a direction.
The corresponding values now are:
    Ordered: 14.197350713914734
    Middle : 13.128723041575485
    Noisy  : 12.824963198002461

(These measurements come from mahotas.features.haralick() with return_mean=True. Otherwise, you get the value of each feature for each direction considered (typically, the 4 directions around a pixel))
We observe a similar trend.
The difference between the two ways of computing the entropy here is robustness and sensitivity. We do not have a long enough sample here, but it is likely that the haralick features would be more sensitive to structural changes than a single histogram.
Therefore, you can get a long enough set of images from the experiments, &quot;map&quot; how the value of this feature varies and then set up &quot;alarms&quot; by setting thresholds around specific ranges.
Hope this helps.