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Hi, I'm looking for the best approach to detect and assess the thickness of the leftmost "line" in a noisy image. ( Lines represent layers of a material captured with OCT imaging ).
Three examples attached, with the line of interest partially highlighted in red (source images are greyscale).

A), B) and C) (more difficult):
The leftmost band is (always) the line of interest; the next band is an underlying layer/interface; and the third band usually visible is actually a reflection of the first.

As you can tell (zooming in) images are noisy; each row is (in principle) a separate measurement - with occasional artefacts showing up. I don't need a 100% success rate, but over a series of measurements I'd like to report an averaged line thickness with a sub-pixel accuracy.

What are OpenCL primitives/operations that you would suggest using in this case? For instance: 1. Some de-noising 2. Detection of the right/left edges of the leftmost band 3. Reporting an average thickness with sub-pixel accuracy

Ultimate goal is to sort my 'samples', label areas with different thicknesses, and detect where was the thinnest/thickest zone within a labeled area resides)

I do have ideas from prior experience, but I'm looking for ideas on how OpenCV can be used here. Also I'll need a I will need a high processing throughput rate (say at least 100 such images per second).

Thanks in advance for sharing your ideas and insights, Ivan

Hi, I'm looking for the best approach to detect and assess the thickness of the leftmost "line" or band in a noisy image. ( Lines represent layers of a material captured with OCT imaging ).
Three examples attached, with the line below, with the band of interest partially highlighted in red (source images are greyscale).

A), B) and C) (more difficult): (trickier):
The leftmost band is (always) the line of interest; the next band is an underlying layer/interface; and the third band usually visible is actually a reflection of the first.

As you can tell (zooming in) images are noisy; each row is (in principle) a separate measurement - with occasional artefacts showing up. I don't need a 100% success rate, rate (I can disregard noisy lines), but over a series of measurements I'd like I need to report an averaged line average band thickness with a sub-pixel accuracy.accuracy. My ultimate goal is to sort my 'samples', label areas of different thicknesses, and detect where was the thinnest/thickest zone within a labeled area resides)

What are OpenCL primitives/operations that you would suggest using in this case? For instance: 1. Some de-noising 2. Detection of the right/left edges of the leftmost band 3. Reporting an average thickness with sub-pixel accuracy

Ultimate goal is accuracy Also, could OpenCL be used on a volume of such data? (Image series are in front of each other, it might be useful to sort my 'samples', label areas with different thicknesses, and detect where was the thinnest/thickest zone within a labeled area resides)

I do have ideas from prior experience, but interpolate or de-noise across images).

I'm looking for ideas on how OpenCV can be used here. Also I'll need a I will need a high processing throughput rate (say at least 100 such images per second).

Thanks in advance for sharing your ideas and insights, Ivan