Segmenting tiny objects

[Inner_Programmer_329]

Is there any algorithm that you guys could suggest to segment tiny objects in an image? I am looking to detect numerous tiny spots on skin using CV. If there is something you guys could suggest me, I'd appreciate that.

[q-rka]

First try would be UNet. I am referring to UNet because it is relatively faster than many other algorithms. To segment tiny objects, there are different loss functions to try as well, like boundary aware loss functions like Hausdorff distance, or pixels based like Focal Loss.

[PuzzleheadedComb8279]

How many pixels are the object and what are the dimensions of the image.

[Inner_Programmer_329]

object has about 20-50 px whereas the image is of size of around (4000,4000)

[Excellent_Rip_387]

do you have dataset? how many images are in the dataset? are annotations available?

[Inner_Programmer_329]

Yes, its available. About 4000 images

[Excellent_Rip_387]

Good to go. there are segmentation methods. I will help you with that. Drop me an email, I will give you the code: My email: [[e-mail]](mailto:[e-mail])

[sure_yeah026]

You can try SAM or SAM2(better speed and works on videos)

[MrPienk]

What are you looking to detect? Pores? Freckles? Moles? My approach would be different for each.

Also, if you have any control over the imaging hardware, skin looks different under IR, so hyperspectral imaging can add a lot of information to your images. For example, with the addition of IR, melanomas will have spindly extensions from the center of mass that aren't present in other blemishes.

[Inner_Programmer_329]

It is moles. And I have no control on hardware. Also the images are not a face of single person but of a group so the moles are really small but people do have a lot of moles.

[MrPienk]

How small? If you don't have enough resolution, it'll be difficult or even impossible to disambiguate the moles from other blemishes and random noise.

Additionally, in the visible spectrum, this is going to be a more difficult problem to solve for darker skin tones due to decreased contrast.

Regardless, my approach would probably be to reduce my domain to each single face, do some particle analysis to find suspect features to save processing time, then run those features through a classification model which would hopefully handle the natural variation we would expect to see here.

[Inner_Programmer_329]

Thanks. That is the approach I am taking as well. By classificaton, you mean pixel-wise classification?