Heh. Sorry, I gave you a typo in the last loop there. You don’t want “dog”,
you want “dim(1)” in the loop at the bottom.
> On Apr 23, 2015, at 9:01 AM, Craig DeForest <[email protected]> wrote:
>
> If you just want to estimate the spot area, that is even easier than fitting
> a Gaussian to the shape. You can do something like:
>
> # Convert to floating point; rearrange to 3 x width x height; collapse
> to width x height
> $bw_image = $pdl1->float->mv(2,0)->sumover
>
> # Generate mask image
> $mask = ($bw_image < 255*3/2);
>
> If you’re actually trying to integrate photometry of your chromatographic
> images, you need to think about what, exactly, the dye is doing to the paper.
> The brightness of a dyed spot is multiplicative with more dye — each unit
> concentration of dye on the paper reduces the reflectivity at a particular
> wavelength by the same factor, so you don’t want to be integrating brightness
> or even darkness — you want to be integrating
> -log(brightness/undyed-brightness). The logarithm operation will screw you
> up unless you have a bright reference (fortunately you do — the undyed paper)
> and a dark reference (I don’t see one there). You’ll still have issues with
> that, because the exponential decay is at each wavelength, and the spots
> change color as they get darker - so the overall absorptivity per unit dye
> shifts as the spot gets darker. These aren’t PDL issues, they’re
> chromatographic issues.
>
> Anyhow, you might be better off just working with the geometry, unless you
> just need qualitative analysis. To find the area of a spot, in pixels, you
> can sum a region of the $mask.
>
> Separating the spots on a single image is something you can do by explicit
> slicing, or you can try to get fancy. Here’s one way to get fancy, by using
> a convolution to estimate the total number of masked points within a given
> radius. It’s very wasteful of CPU cycles, but quick to code up — which fits
> within the philosophy of PDL.
>
> # Make an image that is 1 within $rmax of the center of the image, 0
> elsewhere.
> # Choose $rmax large enough to include a spot; small enough to exclude
> neighbors.
> $rmax = 50;
> $mask_kernel = (rvals( $rmax * 2 + 1, $rmax * 2 + 1) <= $rmask;
>
> # Create an image containing just how many mask=true points are within
> # rmax of each pixel. This is wasteful because you only need like 8
> points,
> # but calculate the sum for each pixel in the whole image.
> $mask_smooth = $mask->convolveND($mask_kernel);
>
> # Now identify the middle of each spot. Smooth the mask_smooth even
> further,
> # to create a local maximum at the center of each spot. Then use
> range() to
> # generate a 3x3 collection of image lags (total dims 3 x 3 x w x h)
> shifted
> # in each of the 9 possible directions, and zero out the unshifted one
> in the middle.
> # Local maxima are places where the original smsm image is larger than
> any of
> # its shifted copies.
> $mask_smsm = $mask_smooth->float->convolveND($mask_kernel);
> $mask_smsm_lags = $mask_smsm->range( ndcoords(3,3)-1,
> $mask_smsm->shape, ’e’);
> $mask_smsm_lags->((1),(1)) .= 0;
> $localmax_mask = ($mask_smsm > $mask_smsm_lags->maximum->maximum);
>
> # $localmaxima is 2xN — it’s the pixel coordinates of all the local
> maxima in mask_smsm,
> # which should be the center of each spot. The area of each spot is
> just the smoothed mask
> # value at that location.
> $localmaxima = whichND($localmax_mask);
> $areas = $mask_smooth->indexND($localmaxima);
>
> print “Spot areas found:\n”;
> for $i($localmaxima->dog){
> print $localmaxima->( :, ($i) ),
> “: area is “.$areas->(($i)),
> “\n”;
> }
>
>
>
>
>
>
>> On Apr 23, 2015, at 8:24 AM, Demian Riccardi <[email protected]
>> <mailto:[email protected]>> wrote:
>>
>>
>> Thanks, Craig.
>>
>> Ideally, I would like to automatically fit a two gaussian to each blob, and
>> integrate it to get the total relative contents of the blobs, which I could
>> then connect back with the known amount of standards added. For starters I
>> was able to read in the file using PDL::IO::Image (awesome) and rimage. I
>> tried using PDL::IO::Pic rim, but that spit out an error for some reason.
>>
>> I sliced each column of the image and printed out the minimum value of the
>> image unit (RGB value? sorry, I’m not an expert in manipulating images!).
>> This spit out something that looked somewhat reasonable when graphed. Thus,
>> I should be able to grab out the index of the minimum paired with a column
>> to fit a the gaussian, but it there an easier way to do this?
>>
>> I’m still a PDL newbie, so even slicing is not trivial for me at this point…
>> bah!
>>
>> Demian
>>
>> below is a copy paste from an IPerl Notebook (I hope it translates!).
>>
>>
>> In [31]:
>>
>> use Modern::Perl;
>> use PDL;
>> use PDL::IO::Image;
>> In [32]:
>>
>> my $pdl1 = rimage('Desktop/TLC_arg.jpg');
>> print $pdl1->info;
>> Out[32]:
>> PDL: Byte D [2625,116,3]
>> 1
>> In [37]:
>>
>> foreach my $ic (0 .. 2624){
>> my $col = min($pdl1->slice("$ic"));
>> print $col . "\n";
>> }
>>
>>
>>> On Apr 23, 2015, at 9:34 AM, Craig DeForest <[email protected]
>>> <mailto:[email protected]>> wrote:
>>>
>>> How do you want to quantify them? E.g. are you doing photometry or are you
>>> doing geometry? PDL can help you either way. The “rim” function will load
>>> a color image (say a PNG or JPG) into a structured array. After that you
>>> can manipulate it. Simple geometric extraction or brightness extraction
>>> are easy after that.
>>>
>>>
>>>
>>>> On Apr 22, 2015, at 2:14 PM, Demian Riccardi <[email protected]
>>>> <mailto:[email protected]>> wrote:
>>>>
>>>> Hello,
>>>>
>>>> A need has come up for analyzing some thin layer chromatography in one of
>>>> my chemistry labs (see the attached jpg; I know it isn’t perfect). Is
>>>> there a way I can pull in this image and automatically quantify the spots?
>>>> Two of the sets of four are standards and the other two are samples.
>>>>
>>>> I was able to load it in with PDL::IO::Image so I know there must be a way!
>>>>
>>>> Demian
>>>>
>>>> <TLC_arg.jpg>
>>>> ------------------------------------------------------------------------------
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>>
>
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